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Bloomberg 26NG SWE VO Interview Experience | 3 Rounds Back-to-Back, 4.5 Hours

interviewshow-cs — Mon, 14 Sep 2026 12:17:45 +0000

I just finished my Bloomberg 26NG SWE VO. Three rounds were scheduled back-to-back, taking around four and a half hours in total. I wanted to write down the experience while everything is still fresh.

One thing that stood out about Bloomberg is that the hardest part is often not getting the initial solution, but defending it and extending it under follow-up pressure. Both technical rounds followed this pattern. The questions themselves were not extremely difficult, but the follow-ups kept adding another layer.

Interview Process Overview

Technical Round 1: 45–60 minutes
Technical Round 2: 45–60 minutes
HR Round: around 30 minutes
EM Round: around 40 minutes, scheduled as a follow-up in this process

The main rounds were concentrated on the same day with very little buffer in between. The overall Bloomberg process can take roughly 3–7 weeks, and both communication and technical ability matter. Each round may also include a deeper discussion of your resume.

Round 1: Behavioral Questions + Merge Intervals + Secret String

Resume and Behavioral Questions

The behavioral section took around 15 minutes and moved quickly. There were three main questions:

What was the most difficult technical problem you have faced?
How do you handle disagreements with a teammate or manager?
How do you prioritize when multiple tasks become urgent at the same time?

The follow-ups focused on the reasoning behind your decisions and how you handled the situation afterward. For example, when discussing a technical challenge, you should be able to explain why you chose a particular approach, not just describe what you implemented.

Bloomberg seemed to care more about technical decision-making than having an especially flashy project. Being able to clearly explain what you built, why you built it that way, and what you would change matters more.

Coding 1: Merge Intervals

The first coding problem was LeetCode 56 – Merge Intervals.

The standard solution is to sort the intervals by their starting point and then scan through them while maintaining the current merged interval. If two intervals overlap, extend the right endpoint. Otherwise, add a new interval to the result.

def merge(intervals):
    intervals.sort(key=lambda x: x[0])
    result = [intervals[0]]

    for start, end in intervals[1:]:
        if start <= result[-1][1]:
            result[-1][1] = max(result[-1][1], end)
        else:
            result.append([start, end])

    return result

The time complexity is O(n log n), mainly because of sorting.

Coding 2: Secret String Guessing

The second problem was a secret-string guessing problem. For each character:

If guess[i] == secret[i], return *.
If the character exists in the secret but is in the wrong position, return +.
Otherwise, return -.

A basic version can be handled with a set, but the important follow-up was to account for character frequency.

You need to process exact matches first and consume those characters from a frequency counter. Then, in a second pass, determine whether the remaining guessed characters can be matched elsewhere in the secret.

from collections import Counter

def check_guess_with_freq(secret, guess):
    freq = Counter(secret)
    result = [''] * len(secret)

    for i, (s, g) in enumerate(zip(secret, guess)):
        if s == g:
            result[i] = '*'
            freq[s] -= 1

    for i, (s, g) in enumerate(zip(secret, guess)):
        if result[i]:
            continue

        if freq.get(g, 0) > 0:
            result[i] = '+'
            freq[g] -= 1
        else:
            result[i] = '-'

    return ''.join(result)

The key point is the two-pass approach. Without consuming the exact matches first, duplicate characters can easily be counted incorrectly.

Round 2: Resume Deep Dive + LC 1209 + Linked List Implementation

Resume Deep Dive

This section took around 15 minutes and involved two interviewers. The questions went deeper into the technical details of projects on my resume.

If you could redesign the project, what would you improve?
You mentioned smart pointers — how do they work?

The general lesson is simple: anything technical on your resume can become a follow-up question. If you mention a specific technology or concept, be prepared to explain how it works and why you used it.

Coding 1: Remove All Adjacent Duplicates in String II

The first coding question was LeetCode 1209 – Remove All Adjacent Duplicates in String II.

The standard solution uses a stack containing pairs of characters and their consecutive counts. Once a character reaches k occurrences, the stack entry is removed.

def removeDuplicates(s, k):
    stack = []

    for c in s:
        if stack and stack[-1][0] == c:
            stack[-1][1] += 1

            if stack[-1][1] == k:
                stack.pop()
        else:
            stack.append([c, 1])

    return ''.join(c * cnt for c, cnt in stack)

Follow-Ups

The interviewer then pushed the problem further:

What are the time and space complexities?
What happens if you remove the if count == k: pop logic?
Can you implement the same idea using a linked list?

The linked-list version was the most challenging part of this round. Instead of explicitly popping a stack entry, the previous node becomes the new head.

class Node:
    def __init__(self, char, count, prev=None):
        self.char = char
        self.count = count
        self.prev = prev


def removeDuplicatesLL(s, k):
    head = None

    for c in s:
        if head and head.char == c:
            head.count += 1

            if head.count == k:
                head = head.prev
        else:
            head = Node(c, 1, head)

    result = []

    while head:
        result.append(head.char * head.count)
        head = head.prev

    return ''.join(reversed(result))

The main thing being tested here was pointer manipulation. Setting head = head.prev effectively performs the pop operation without explicitly deleting the node.

Round 3: HR Interview

The HR round lasted around 30 minutes and was purely behavioral.

Tell me about yourself.
What project did you find the most interesting?
Why are you interested in Bloomberg?
What would you value most after joining the company?

“Why Bloomberg?” is worth preparing carefully. Doing some research into Bloomberg Terminal, financial data infrastructure, quantitative tools, and the company's products will make your answer much stronger than simply saying that Bloomberg is a leader in financial technology.

Common Topics and Overall Impression

Common Coding Topics

Based on this interview, useful areas to prepare include string matching and elimination, interval problems, stack-based problems, hash maps and frequency counting, as well as some graph and tree questions.

Common Behavioral Topics

For behavioral questions, be ready to discuss situations where your initial approach did not work, how you prioritize competing tasks, how you handle disagreements, and how you change direction when circumstances change.

The Biggest Takeaway

Bloomberg's interviews felt somewhat like a code review. The goal is not simply to see whether you can write working code. The interviewer wants to know whether you fully understand the code you just wrote and whether you can reason about what happens when the requirements change.

After finishing the initial solution, it helps to proactively explain the complexity, mention important edge cases, and think about possible extensions. That gives you more control over the discussion instead of waiting for every follow-up to come from the interviewer.

Also, be prepared to defend every technical keyword on your resume. If you mention something like smart pointers or a particular system component, you should be comfortable explaining the underlying concept, your implementation choices, and the relevant trade-offs.

Final Thoughts

The follow-ups were the real dividing line in this Bloomberg interview. Getting the initial problem solved is only the starting point. The more important part is whether you can extend the solution, switch implementations, and explain the impact of changing or removing a piece of code while under pressure.

For candidates preparing for Bloomberg, Goldman Sachs, JPMorgan, and other financial technology companies, it is worth spending extra time on interval variations, stack-to-linked-list implementations, and frequency-based string matching.

InterviewShow also covers Online Assessment, Coding Interview, System Design, and Virtual Onsite preparation, with one-on-one support for candidates who want more targeted interview practice.

Good luck with the interview and hope you get the offer!

First Report: Google 27 Intern OA Experience – Two Problems, but Read the Conditions Carefully

interviewshow-cs — Thu, 10 Sep 2026 13:04:20 +0000

Just finished one of the first Google 2027 Intern OAs today. There were two coding problems, and neither felt particularly difficult. The main challenge was understanding the exact conditions before jumping into a standard template.

The first problem was a greedy problem with a binary-search/LIS connection. The second was a classic subset-sum problem that could be solved with 0/1 Knapsack DP.

Overall, the OA felt fairly fundamental, but small details in the wording mattered a lot. Here’s a breakdown of both problems and the approaches that worked.

Q1: Minimum Number of Rows for Students

Problem Overview

You are given an array A representing the heights of students arriving in order.

For each student:

If there is an existing row where every student is taller than the new student, the student can join that row.
Otherwise, a new row must be created.

The goal is to determine the minimum number of rows required.

For example:

A = [5, 4, 3, 6, 1]

The answer is 2.

The most important detail here is the phrase “every student is taller.” It is easy to accidentally interpret the condition as simply requiring the new student to be shorter than the last student in a row, but the greedy state needs to represent the shortest student currently in that row.

Approach

Think of each row as a decreasing sequence.

For each row, we only need to keep track of its current shortest student, which is effectively the row's tail.

When a new student with height h arrives, we need to find a row whose current shortest student is still taller than h. Since that student is already the shortest person in the row, everyone else in that row must also be taller than h.

To leave as much flexibility as possible for future students, we can use a greedy strategy and update the appropriate row tail to the new, shorter height.

An ordered array with binary search works well for this:

import bisect

def min_rows(A):
    # tails[i] = current shortest height in row i
    tails = []

    for h in A:
        # Find the first tail greater than h
        idx = bisect.bisect_right(tails, h)

        if idx == len(tails):
            tails.append(h)
        else:
            tails[idx] = h

    return len(tails)

This problem can also be viewed as a partition into decreasing chains. The minimum number of rows is closely related to the length of the longest strictly decreasing subsequence.

The key detail is the comparison direction. Because the condition says that everyone in the row must be taller than the new student, we maintain the shortest person in each row rather than simply tracking the most recently added student.

Q2: Minimize the Load Difference Between Two Servers

Problem Overview

You are given an array of process loads. The processes need to be distributed between two servers so that the difference between their total loads is as small as possible.

For example:

A = [1, 2, 3, 4, 5]

One possible partition is:

Server 1: 1 + 2 + 4 = 7
Server 2: 3 + 5 = 8

So the minimum difference is 1.

Approach

This is essentially a classic Subset Sum problem.

Let the total load be S. If we choose a subset with sum x for the first server, the second server receives S - x.

The difference is therefore:

|S - 2x|

So instead of directly searching for two partitions, we can find a subset whose sum is as close as possible to S / 2.

A standard 0/1 Knapsack feasibility DP is sufficient when the total sum is reasonably small:

def min_load_diff(A):
    S = sum(A)
    target = S // 2

    dp = [False] * (target + 1)
    dp[0] = True

    for x in A:
        for j in range(target, x - 1, -1):
            if dp[j - x]:
                dp[j] = True

    for s in range(target, -1, -1):
        if dp[s]:
            return S - 2 * s

    return S

One important OA detail is that the DP array must be updated from right to left.

If we iterate from left to right, the same element could potentially be used multiple times during a single iteration, turning the solution into an unbounded knapsack rather than a 0/1 Knapsack.

If the total sum is extremely large, techniques such as Meet-in-the-Middle could be considered. But with reasonable OA constraints, the standard DP approach is usually enough.

Key Takeaways From This Google 27 Intern OA

Problem	Core Concept	Main Pitfall
Q1	Greedy + Binary Search / LIS	Understanding “everyone is taller” rather than only comparing with the row tail
Q2	0/1 Knapsack / Subset Sum	Updating the DP array in the wrong direction

Looking at the two questions together, neither requires an especially advanced algorithm. The bigger challenge is recognizing the underlying model and carefully following the constraints in the problem statement.

Q1 is a good example of why wording matters. The difference between “everyone in the row is taller” and “the last student is taller” completely changes how the greedy state should be maintained.

Q2 is much more familiar as a standard subset-sum problem, but the implementation detail of iterating backward in the DP array is critical.

How to Prepare for the Google 27 Intern OA

If you're preparing for the Google 2027 Intern OA, I wouldn't focus exclusively on grinding difficult problems.

It is more useful to get comfortable recognizing common patterns quickly:

Grouping or ordering problems → Greedy / LIS
Splitting values into two balanced groups → Subset Sum / Knapsack
Continuous ranges → Sliding Window / Prefix Sum
Shortest paths → BFS / Dijkstra
Dependency relationships → Graph / Topological Sort

More importantly, don't immediately apply a familiar template just because the problem looks similar to something you've seen before.

Read the constraints first, then choose the algorithm.

That was especially important in Q1. The “every student must be taller” condition determines exactly what information needs to be maintained for each row.

Final Thoughts

Google Intern OAs don't necessarily require extremely difficult algorithms. Under a time limit, careful reading, modeling, and edge-case handling can make a bigger difference than simply knowing more advanced techniques.

Before coding, make sure you understand exactly what the problem is asking. Even if the problem looks familiar, verify that its constraints and comparison conditions are actually the same as the problems you've solved before.

Interview Preparation Resources

If you're preparing for Google, Amazon, TikTok, Stripe, Goldman Sachs, or other North American tech companies, InterviewShow is also a useful resource to check out.

InterviewShow focuses on practical interview preparation, including Online Assessments, Coding Interviews, System Design, and Virtual Onsite interviews. Going through real interview experiences can help you understand the actual question styles, interview structure, and common follow-ups for different companies before you start preparing.

For internship recruiting in particular, getting familiar with the OA format and recent question patterns early can make preparation much more targeted than simply following a generic coding-question list.

Good luck with the Google 27 Intern OA, and hopefully everyone gets through to the next round!

LinkedIn SDE Interview Experience: 8 Rounds in 5 Weeks — More Product Thinking Than Expected

interviewshow-cs — Wed, 09 Sep 2026 08:39:23 +0000

After five weeks and eight interview rounds, my LinkedIn SDE interview process finally came to an end.

Before going in, I read quite a few interview experiences online, but very few covered the full eight-round process. So I tried to document each round as clearly as possible.

My biggest takeaway is this: LinkedIn is not a company that simply filters candidates based on how quickly they can solve LeetCode problems. The early rounds definitely include coding, but starting around the fourth round, technical depth, project judgment, and product thinking become much more important.

Your ability to explain the systems you have actually built — including the trade-offs, mistakes, bottlenecks, and decisions behind them — matters much more than instantly finding the optimal solution.

Interview Process Overview

Round	Interview Type	Core Focus
1	Recruiter Call	Why LinkedIn, product understanding, metrics
2	Phone Screen	Project discussion, behavioral questions, minimum workers problem
3	Coding	Currency conversion using graphs and BFS
4	Tech Lead Interview	Deep project dive and high-density behavioral questions
5	LLM + Coding	RAG, hallucinations, and longest path in a DAG
6	System Design	Professional social platform and Feed architecture
7	Project Deep Dive	Payment gateway, idempotency, and reconciliation
8	Hiring Manager Interview	Technical follow-ups and culture fit

Round 1: Recruiter Call

This round lasted around 30 minutes. It felt much more structured than a casual conversation.

Why LinkedIn?

This is worth preparing properly. Saying something like "I want to work at a big tech company" probably will not leave much of an impression.

A stronger answer connects your interests to LinkedIn's actual engineering challenges. For example:

I am particularly interested in recommendation and Feed ranking systems, and LinkedIn's scale and data density make it an interesting environment for building products around professional relationships and career growth.

Product Understanding

The recruiter also asked about LinkedIn's product and business model. It is useful to understand the company's major business areas, including:

Talent Solutions
Marketing Solutions
Premium Subscriptions

Simply describing LinkedIn as "a platform for finding jobs" is far too shallow. Understanding how the product creates value and how success is measured helps throughout the entire interview process.

Round 2: Phone Screen — Project + Behavioral + Coding

This round was conducted through CoderPad and lasted about 60 minutes.

The first 15 minutes focused on project discussion and behavioral questions, followed by a coding problem.

Behavioral Question

I was asked to describe a time when I received negative feedback and how I responded.

This theme appeared again in later rounds. The interviewer seemed more interested in your actual judgment and actions than in a perfectly polished story about how grateful you were for feedback.

Coding: Minimum Number of Workers

Given the start and end times of multiple tasks, find the minimum number of workers required to process them.

The standard solution was to convert each interval into two events:

Task starts: +1
Task ends: -1

After sorting the events by time, scan through them and track the maximum number of concurrent tasks.

The interviewer then asked several follow-up questions:

If one task ends exactly when another begins, can the same worker be reused?
Can one worker process multiple tasks at the same time?

The main lesson here was to clarify boundary conditions proactively instead of assuming them.

Round 3: Coding — Currency Conversion

This round was entirely focused on coding.

The problem modeled currencies as graph nodes and exchange rates as directed edges. Given a source and target currency, the goal was to find a valid conversion path and multiply the exchange rates along that path.

A straightforward BFS or DFS works well for a single query.

Possible Follow-Ups

Cache frequently queried currency pairs
Use graph connectivity techniques for reachability checks
Handle floating-point precision issues carefully
Support reverse exchange rates
Consider cycles in the graph

If numerical stability becomes important, logarithmic transformations can also be useful depending on the problem constraints.

Round 4: Tech Lead Interview — Project Depth + Behavioral Questions

This was probably the highest-density interview in the entire process.

The first 20 minutes focused on a deep dive into one of my projects. The remaining 40 minutes were almost entirely behavioral questions.

Project Deep Dive

The interviewer asked questions such as:

Why did you choose this technology stack?
What alternatives did you consider?
Where were the performance bottlenecks?
How did you profile the system?
If you rebuilt it today, what would you change?

Simply explaining why every decision was correct is not enough. A strong answer should explain the reasoning behind the decision and acknowledge what you did not know at the time.

Being honest about trade-offs usually works better than trying to defend every past decision.

Common Behavioral Questions

Tell me about a conflict you had with someone.
What was your most challenging project, and what would you change if you did it again?
Tell me about a time when you had to learn a new technology quickly.
Why did something pass local testing but still fail in production?
How do you deal with ambiguous requirements?

For conflict questions, a weak answer is:

The other person simply did not understand the system.

A much stronger answer focuses on how the disagreement was resolved:

We realized that we were optimizing for different risks. We wrote down the possible failure scenarios, compared their potential impact, and ran a small-scale validation before making the final decision.

That demonstrates engineering judgment instead of simply assigning blame.

Round 5: LLM + Coding

LLM Discussion

The discussion covered several practical LLM topics:

RAG vs. fine-tuning
Common causes of hallucinations
Ways to reduce hallucinations

Potential approaches included retrieval augmentation, temperature tuning, citation constraints, and human validation.

One interesting follow-up was about the downside of setting the temperature too low. Lower temperature can make responses more repetitive and conservative, which may reduce quality for open-ended tasks.

Coding: Longest Dependency Chain in a DAG

The coding problem involved finding the longest dependency chain in a directed acyclic graph.

A typical solution combines:

Topological sorting
Dynamic programming

Follow-up questions included:

How would you handle weighted edges?
What happens if the graph contains multiple disconnected components?

A properly implemented graph traversal naturally handles disconnected components.

Round 6: System Design — Designing a Professional Social Platform

The system needed to support:

User profiles
Social relationships
Posts
Personalized feeds

I started by clarifying several important requirements:

DAU and overall scale
Read-to-write ratio
Real-time requirements
One-way vs. two-way relationships

Possible Storage Design

User profiles: relational database such as MySQL
Social graph: sharded by user_id
Posts: write-heavy, time-ordered storage such as Cassandra

Feed Architecture

The most important design decision was the Feed generation strategy.

A hybrid push-and-pull model works well:

Normal users: fan-out on write
High-follower or celebrity accounts: fan-out on read

A Redis Sorted Set can be used to maintain time-ordered Feed entries for fast retrieval.

The interviewer also asked about hot users and extreme relationship growth. For example, if someone follows hundreds of thousands of users, generating their Feed cannot simply rely on a naive fan-out strategy.

You need to discuss concrete mitigation strategies rather than stopping at a generic answer like "we can add more servers."

Round 7: Project Deep Dive — Payment Gateway

This round focused on a payment system I had worked on.

The discussion covered:

Payment channel abstraction
Transaction state machines
Webhook processing
Reconciliation workflows

Duplicate Callbacks

The solution was to make processing idempotent. A channel transaction ID could be stored with a unique constraint to prevent duplicate processing.

Missing Callbacks

For payments stuck in a PROCESSING state:

Periodically query the payment provider.
Update the transaction state if the external result is available.
Retry within a defined limit.
Move transactions that exceed the retry threshold into a manual review queue.

The important concept here is eventual consistency. Distributed payment workflows should not assume that every callback will arrive exactly once and succeed immediately.

Round 8: Hiring Manager Interview

The technical discussion continued from previous rounds, especially around performance bottleneck analysis.

Topics included profiling tools and approaches such as:

Flame graphs
async-profiler
CPU and memory profiling

Legacy System Scenario

A classic scenario was:

You inherit a slow legacy system with poor test coverage and limited monitoring. What would you do?

The answer I gave followed this general approach:

Improve observability first.
Use production data to identify the highest-impact bottlenecks.
Make incremental improvements.
Avoid attempting a massive rewrite before understanding the actual problem.

The culture-fit discussion also returned to conflict resolution and product thinking.

I was asked something along the lines of:

If you joined LinkedIn as an engineer, what area would you most want to invest in?

Topics such as recommendation cold starts, real-time content moderation, and Feed quality can all lead to interesting discussions if you connect them to real product problems.

How I Would Prepare for LinkedIn

1. Understand the Product

Learn the major business lines and important product metrics. Do not treat LinkedIn as simply a job-search website.

2. Prepare for Deep Project Discussions

You should be able to explain:

Why you chose a particular architecture
What alternatives you considered
Performance bottlenecks and actual numbers
How you profiled the system
What you would change if you rebuilt it
What would break if traffic increased by 10×

3. Prepare Behavioral Stories Beyond the First Answer

Conflict, negative feedback, and ambiguous requirements are all worth preparing for multiple layers of follow-up questions.

4. Focus on Practical Coding Patterns

Topics worth reviewing include:

Interval scheduling
Graphs
Currency conversion problems
DAGs
Top-K problems

Most questions felt closer to Medium difficulty. Clear reasoning and high-quality implementation mattered more than solving extremely difficult problems.

5. Practice Feed System Design

The push-vs.-pull trade-off is particularly important. Be prepared to discuss hot users, fan-out costs, storage, caching, and scalability.

Final Thoughts

Coding mainly dominated the first few rounds. The biggest differentiator came later.

The candidates who stand out are usually not just the ones who can solve algorithms quickly. They can explain their projects deeply, understand how engineering decisions affect products, and demonstrate real judgment when dealing with trade-offs and conflicts.

If you only prepare by grinding LeetCode, the later LinkedIn rounds may feel surprisingly uncomfortable.

Personally, I think spending time deeply understanding your most complex project — along with Feed architecture, payment idempotency, and LinkedIn's product logic — is more valuable than solving another hundred random problems.

If you are preparing for LinkedIn or other major tech companies, it can also be helpful to practice project deep dives, Feed system design, and payment system fundamentals separately.

InterviewShow provides interview preparation support for companies such as LinkedIn, Google, and Meta, with one-on-one guidance throughout the preparation process.

Good luck with your interviews — hope you get the offer you're aiming for.

Walmart Global Tech SDE Interview Experience: Three Rounds of Fundamentals, Concurrency, and Deep-Dive Questions

interviewshow-cs — Tue, 08 Sep 2026 14:11:56 +0000

I recently completed a three-round Walmart Global Tech SDE interview process. The overall schedule was intense, but the interviews did not feel intentionally tricky or designed to catch candidates off guard.

My biggest takeaway was that Walmart Global Tech does not seem to focus on extremely difficult algorithm questions. Instead, the interviewers care much more about whether your engineering fundamentals are solid and whether you can make reasonable technical decisions in real-world scenarios.

The experience felt more like discussing engineering problems with other developers than performing memorized interview tricks.

Interview Process Overview

Recruiter Call → Technical Screening (Karat / HackerRank) → Virtual Loop (DSA + LLD + HLD + Hiring Manager)

In my case, I completed three Virtual Loop interviews across three different days. Each round lasted approximately 45–60 minutes.

The entire process, from completing the OA to finishing the final round, took around two weeks.

Karat Screening: A Stage Worth Taking Seriously

The Karat screening lasted 60 minutes.

First 10 minutes: Technical multiple-choice questions
Remaining 50 minutes: Two coding problems

The technical questions covered topics such as:

Threads and concurrency
JVM fundamentals
Spring Boot

This round appears to be an important gate before the Virtual Loop. A lot of candidates seem to underestimate the technical knowledge section and focus only on algorithms.

Round 1: Engineering Fundamentals + Coding

Technical Discussion: High Question Density

The interviewer started with a deep dive into my previous projects. Typical questions included:

Why did you choose this particular technology?
What were the biggest bottlenecks?
If you rebuilt the project today, what would you change?

After that came a rapid series of engineering fundamentals questions.

Process vs. Thread: Memory, overhead, and communication
RESTful APIs: Statelessness, uniform interfaces, and PUT vs. PATCH
HTTP vs. HTTPS: SSL/TLS and the connection process
From URL to Webpage: DNS, TCP, CDN, and browser rendering
SQL Injection: How it works and why parameterized queries help
Microservices vs. Monoliths: Especially in large-scale e-commerce systems
Arrays vs. Linked Lists: Random access, insertion, and cache locality

The important part was that a short conclusion was rarely enough. Most answers could lead to follow-up questions, so you need to understand the underlying principles rather than simply memorize definitions.

Coding: Longest Substring Without Repeating Characters

The coding problem was the classic Longest Substring Without Repeating Characters.

The standard solution uses:

A sliding window
A hash map storing the latest position of each character

The overall time complexity is O(n).

The interesting part was the follow-up:

What if the input string is several gigabytes and cannot fit into memory?

At that point, the discussion shifted away from pure algorithms and toward system thinking.

A possible approach would be to process the input as a stream while maintaining only the necessary window state. When processing data in chunks, you also need to preserve the relevant boundary state between chunks.

This was a good example of Walmart's interview style: knowing the algorithm is not always enough. The interviewer may ask how your solution behaves under realistic system constraints.

Round 2: Two Coding Problems + Concurrency and Distributed Systems

Coding Problems

The two coding questions were relatively straightforward.

Merge Two Sorted Linked Lists

The standard solution uses a dummy head and iterates through both linked lists.

Balanced Binary Tree

The optimal approach uses post-order traversal.

Instead of calculating the height of each subtree separately, return the height during the traversal and immediately terminate when the height difference exceeds 1.

Implementation Discussion

The interviewer also asked me to explain several common data structures and implementation patterns.

LRU Cache: OrderedDict or a Hash Map + Doubly Linked List implementation with O(1) get and put operations
Top K Problems: Sorting, min-heaps, and bucket-based approaches depending on the constraints

LRU Cache is particularly important. It seems to appear very frequently in Walmart-related technical interviews, so it is worth understanding both the built-in and manual implementations.

Concurrency and Distributed Systems

The discussion also covered:

Race conditions
Inter-process communication
Optimistic vs. pessimistic locking
Database optimization under high concurrency
Read/write separation
Database sharding
Redis caching
Slow query optimization
CAP trade-offs
Git rebase vs. merge

One interesting discussion involved CAP trade-offs in different business scenarios.

For example, some large-scale e-commerce services may prioritize availability and partition tolerance, while payment-related systems often require stronger consistency guarantees.

The Project Deep-Dive Pattern

Several questions followed a similar structure:

What was your biggest challenge?

→ How did you solve it?

→ What would you do differently if you started over?

The third question is particularly important. The interviewer is not expecting you to claim that every decision you made was perfect. They want to see whether you can reflect on previous technical decisions and recognize better approaches.

Round 3: Resume Pressure Test + Hiring Manager Behavioral Interview

Resume Deep Dive

This round felt the most intense.

The interviewer selected the most technically complex part of my resume and kept drilling deeper.

Typical questions included:

Why did you choose this technology stack?
Where was the biggest performance bottleneck?
What would fail first if the data volume increased by 10x?
What would happen if concurrency increased significantly?

The purpose was clearly to determine whether you actually built and understood the system, rather than simply memorizing project descriptions.

If there was something I had not investigated deeply, being honest worked much better than trying to bluff. For example:

"I didn't perform a deep profiling analysis at that time, but based on the system architecture, my current assessment would be..."

That type of answer demonstrates ownership and engineering judgment much better than pretending to know every detail.

Hiring Manager Behavioral Questions

The behavioral discussion included questions such as:

Why Walmart Global Tech?
Tell me about a time you had to learn a new technology quickly.
What are your long-term career goals?
How would you handle a product request with extremely high technical costs?

For Why Walmart Global Tech?, I focused on the scale of Walmart's business and the engineering challenges behind areas such as Catalog Engineering, FinTech, and large-scale retail infrastructure.

For project and learning-related questions, the STAR framework works well, but make sure the story emphasizes:

The time pressure
The technical challenge
Your individual contribution
The measurable result

For the question about expensive product requirements, my approach was:

Quantify the engineering cost → Evaluate the business value → Provide alternatives → Let stakeholders make an informed trade-off.

The goal is not simply to say "no" to the product manager. A good engineer should help the team understand the cost of different options.

High-Frequency Topics at a Glance

Category	Common Topics
DSA	Sliding Window, Monotonic Stack, BFS, Dynamic Programming, Interval Merging, Linked Lists, Balanced Binary Trees, Level-Order Traversal
LLD	LRU Cache, Feature Toggle Systems, Thread-Safe Key-Value Stores
HLD	Distributed KV Stores, Real-Time Inventory Systems, Event-Driven Catalog Architecture with Kafka and Redis
Java & Concurrency	Thread Safety, CAS, Thread Pools, Garbage Collection, Spring Boot, Dependency Injection

How to Prepare for Walmart Global Tech Interviews

1. Project Deep Dives Are Extremely Important

Be ready to explain:

Why you made certain technical decisions
The biggest bottlenecks
How you investigated performance issues
What you would redesign today
What would break first under 10x traffic or concurrency

2. Understand the Principles, Not Just the Definitions

For topics such as HTTP/HTTPS, CAP, locks, and distributed systems, avoid memorizing textbook answers.

You should be able to explain the underlying ideas in your own words and apply them to an actual engineering scenario.

3. Backend Candidates Should Take Java and Concurrency Seriously

For backend-oriented roles, Java concurrency and Spring Boot are not simply bonus topics. They can become a significant part of the technical discussion.

4. Prepare a Genuine Answer for "Why Walmart?"

Before the interview, spend some time understanding the engineering challenges behind Walmart's large-scale retail systems.

Relevant areas may include:

Catalog Engineering
FinTech
Supply Chain Technology
Real-Time Inventory
Large-Scale Distributed Systems

FAQ

How difficult is the Karat interview?

The coding problems are generally around the LeetCode Medium level. The first technical knowledge section should not be ignored, especially if you are interviewing for a backend role.

Can the Virtual Loop be scheduled across multiple days?

In some cases, yes. It is worth discussing scheduling options with your recruiter.

Is LRU Cache necessary?

Absolutely. It is a very common topic in technical interviews. You should understand both the high-level design and how to implement it using a Hash Map and Doubly Linked List.

Can I use Python if I don't know Java?

Python is generally fine for DSA interviews. However, for backend or Java-oriented positions, interviewers may still ask detailed questions about Java concurrency, the JVM, or Spring Boot.

How long does the entire process take?

The process can vary, but a common timeline is roughly 2–4 weeks from the OA to the final interview. Offers may arrive within one or two weeks after the final round.

Is there a cooldown period after rejection?

A six-month cooldown is commonly mentioned, but policies can vary by role and team. Always confirm the latest information with your recruiter.

Final Thoughts

If you are preparing for a Walmart Global Tech SDE interview, I would recommend spending extra time on system fundamentals, project trade-offs, LRU Cache, concurrency, and realistic engineering scenarios.

Don't focus exclusively on grinding harder LeetCode problems. Being able to explain why a system was designed in a certain way—and what you would change when the scale increases—can be just as important as solving the coding problem.

For candidates preparing for Walmart Global Tech or other major tech company interviews, InterviewShow provides interview preparation and coaching support for companies such as Walmart, Google, Amazon, and Microsoft.

Good luck with your interviews and hope you get the offer!

Capital One OA Experience – 4 CodeSignal Questions in 70 Minutes

interviewshow-cs — Mon, 07 Sep 2026 12:10:43 +0000

Capital One's Online Assessment was hosted on CodeSignal: four coding questions in 70 minutes.

My actual timing was:

Q1: 9 minutes
Q2: 11 minutes
Q3: 17 minutes
Q4: 25 minutes
Final review: about 8 minutes

Q4 was the closest one to going wrong. I initially submitted a brute-force solution, then realized the input size required a more efficient approach and rewrote it using a dictionary-based interval technique.

That was probably the biggest lesson from this OA: when you see an interval coverage problem, look at the constraints first and think about the optimized approach before submitting a brute-force solution.

Q1: Good Tuples

Given an array a, count how many consecutive triples (a[i-1], a[i], a[i+1]) are "good tuples."

A tuple is considered good when exactly two of its three values are equal.

All three values equal → not valid
All three values different → not valid
Exactly two values equal → valid

For example:

[1, 1, 1, 2, 1, 3, 4]

The tuple (1,1,1) does not count because all three values are the same. The tuples (1,1,2) and (1,2,1) are good tuples.

The easiest solution is to scan the array and count how many equal pairs exist inside each triple:

def goodTuples(a):
    count = 0

    for i in range(1, len(a) - 1):
        equal_pairs = (
            (a[i - 1] == a[i])
            + (a[i] == a[i + 1])
            + (a[i - 1] == a[i + 1])
        )

        if equal_pairs == 1:
            count += 1

    return count

If exactly one of the three comparisons is true, then exactly one pair matches, which means the tuple contains exactly two equal values.

Time complexity: O(n)

One easy mistake is the loop range. Since every tuple needs both a previous and next element, iterate from index 1 to len(a) - 2.

Q2: Absolute Difference Sums After Circular Shifts

You are given two arrays of equal length, nums1 and nums2.

Perform every possible circular right shift on nums1. For each shifted version, calculate the sum of absolute differences between corresponding elements in the two arrays. Return all results in sorted order.

Example:

nums1 = [1, 4, 2, 11]
nums2 = [10, 1, 8, 4]

The sums for shifts 0, 1, 2, and 3 are:

25, 7, 25, 13

After sorting:

[7, 13, 25, 25]

A direct simulation works well:

def absoluteDifferenceSum(nums1, nums2):
    n = len(nums1)
    results = []

    for shift in range(n):
        total = sum(
            abs(nums1[(i - shift + n) % n] - nums2[i])
            for i in range(n)
        )
        results.append(total)

    return sorted(results)

The most common mistake here is getting the shift direction wrong.

For a right shift:

[a, b, c] → [c, a, b]

Therefore, the value at position i in the new array comes from:

(i - shift + n) % n

It is worth manually checking the formula with a three-element array before submitting.

Q3: Two-Sum Queries with Updates

You are given arrays a and b, along with several queries:

[0, i, x]: update a[i] to x
[1, x]: count the number of pairs (i, j) such that a[i] + b[j] = x

Queries must be processed in order, and the results of all type-1 queries should be returned.

The key observation is that array b never changes.

Because of that, we can build a frequency map for b once. For every query, iterate through a and check how many matching values exist in b.

from collections import Counter

def solve(a, b, queries):
    b_freq = Counter(b)
    results = []

    for query in queries:
        if query[0] == 0:
            a[query[1]] = query[2]
        else:
            target = query[1]

            count = sum(
                b_freq.get(target - value, 0)
                for value in a
            )

            results.append(count)

    return results

A few easy mistakes:

Build Counter(b) only once. Do not rebuild it for every query.
Use get(key, 0) so missing values do not cause errors.
Do not remove duplicates from a. Every occurrence represents a separate valid pair.

Q4: Interval Coverage and Counting Unique Points

You are given a list of rays. Each ray is represented as:

[starting_angle, number_of_rotations]

A ray starts at its given angle and covers additional positions every 360 degrees. The goal is to count the total number of unique angle points covered by all rays.

My first attempt was brute force:

# Too slow for large inputs
covered = set()

for start, rotations in rays:
    for r in range(rotations + 1):
        covered.add(start + r * 360)

This passed some smaller test cases but timed out when the input became larger.

The optimized approach is to treat each ray as an interval:

[start, start + rotations × 360]

Then use a difference map and sweep through the interval events:

def raysCoverage(rays):
    from collections import defaultdict

    events = defaultdict(int)

    for start, rotations in rays:
        end = start + rotations * 360

        events[start] += 1
        events[end + 1] -= 1

    total = 0
    active = 0
    last_pos = None

    for pos in sorted(events.keys()):
        if last_pos is not None and active > 0:
            total += pos - last_pos

        active += events[pos]
        last_pos = pos

    return total

The +1 in:

events[end + 1] -= 1

is important because the endpoint itself is included in the interval. The coverage stops at the position after end.

Another important detail is that the event positions must be processed in sorted order.

Final Thoughts

The biggest lesson from this Capital One OA was definitely Q4.

Do not submit a brute-force solution first and hope the constraints are small enough. For interval coverage problems, check the input size immediately and decide whether you need a difference array, sweep line, or hash-based optimization before writing the first submission.

I finished the first three questions in about 37 minutes, which left enough time for Q4 and the final review. That pacing worked well.

One advantage of CodeSignal is that you can solve the questions in any order. I would recommend spending the first minute or two scanning all four problems, identifying the easiest ones, and securing those points first.

The Capital One OA also felt very similar to the broader CodeSignal-style question pool used by companies such as TikTok, Uber, and HRT. There is a significant amount of overlap in common problem patterns, so preparing for one of these companies can also help with several others.

If you're preparing for Capital One or other companies using CodeSignal, InterviewShow has organized common CodeSignal problem patterns and high-frequency question types into targeted preparation lists based on different companies. Feel free to reach out if you need a more focused practice plan.

IBM OA Interview Experience Two Questions, Finished in About 20 Minutes

interviewshow-cs — Fri, 04 Sep 2026 08:31:20 +0000

Honestly, this IBM OA was pretty low-pressure.

The question style was straightforward—no unusual algorithms or tricky concepts. It mainly tested whether you could read the rules carefully and handle the edge cases correctly. There were two questions in total: one involving a greedy approach with a heap, and the other focused on grouped simulation. I didn't really get stuck on either one.

That said, there were two details worth knowing in advance:

Q1: The rental price is based on the current remaining inventory, not the original inventory. It's easy to misread this the first time.
Q2: The multiplication and addition sequence needs to follow the parentheses exactly. It may look obvious, but it's surprisingly easy to reverse the order when implementing it.

Q1: Total Revenue from VM Rentals

You are given n types of virtual machines, each with a certain amount of inventory. There are m customers arriving one by one.

Each customer always rents the VM type with the largest current remaining inventory. The rental price equals the number of machines remaining at that exact moment. After a machine is rented, the inventory for that type decreases by 1.

Return the total revenue.

Example:

Inventory: [1, 2, 4]
m = 4

Revenue:
4 + 3 + 2 + 2 = 11

Approach

The most straightforward solution is a max heap.

Put all inventory values into a max heap.
Remove the current maximum value.
Add that value to the total revenue.
Decrease it by 1.
If the remaining inventory is still greater than 0, push it back into the heap.
Repeat until m customers have rented a VM or the inventory is completely exhausted.

Important edge case: If m is larger than the total inventory, stop once the heap becomes empty. Don't keep looping and accidentally count rentals that cannot happen.

The time complexity is O(m log n).

Q2: Odd and Even Index Group Operations

Split the array into two groups:

Elements at even indices
Elements at odd indices

For each group, perform operations from left to right using the specified alternating pattern:

multiply → add → multiply → add → ...

After calculating both groups, take each result modulo 2.

If the odd-index group produces the larger result → ODD
If the even-index group produces the larger result → EVEN
If both results are equal → NEUTRAL

Example:

[12, 3, 5, 7, 13, 12]

Both groups produce 1 after the final calculation,
so the answer is:

NEUTRAL

The Main Trap

The operation order matters a lot. Don't rely on intuition—follow the parentheses and the expression in the original problem statement exactly.

After implementing the solution, manually walk through the sample once. This is the kind of problem where the general idea can be completely correct while a single reversed operation causes the wrong answer.

Since only the final result modulo 2 matters, you can apply modulo 2 throughout the calculation instead of dealing with potentially large numbers.

IBM Interview Process: Quick Overview

Stage	Typical Format
Online Assessment	HackerRank, around 60–90 minutes, usually 2–3 questions
Recruiter Call	15–30 minutes, background discussion and Why IBM
Technical Interviews	1–2 rounds, Coding + project discussion, with SQL often carrying more weight
Behavioral Interview	STAR-style questions about teamwork, disagreements, and project delays
Overall Timeline	Roughly 4–8 weeks, usually moving at a relatively slow pace

A Few Preparation Notes

The OA itself isn't particularly difficult. The bigger risk is spending too much time on problems you already know how to solve. If you're comfortable with the heap pattern, Q1 can be finished very quickly.
Prepare for a deep dive into your projects. Be ready to explain your architecture decisions, bottlenecks, testing methods, and what you would change if you had to rebuild the project.
Three or four solid behavioral stories are usually enough. In my experience, IBM's follow-up questions were generally less aggressive than Amazon's, so clear and authentic examples matter more than memorizing a huge number of stories.

FAQ

Q1: Why not simply sort the inventory?

Because the inventory changes after every rental. The original maximum decreases by 1, which means it may no longer remain the largest value.

A max heap lets you efficiently maintain the current maximum after every update. Re-sorting the entire array after each rental would be much less efficient.

Q2: Is it valid to take modulo 2 during every step?

Yes. Both addition and multiplication preserve modular arithmetic, so applying mod 2 during the calculation produces the same final result as applying it only at the end.

Final Thoughts

If you're preparing for an IBM OA or other North American tech company assessments, it can help to focus on common patterns such as greedy heap problems, string manipulation, hashing, and simulation.

For candidates who want more targeted practice, InterviewShow provides interview preparation support, including personalized problem walkthroughs and targeted practice for common technical interview patterns.

Good luck with your OA!

Anthropic SWE Four-Round VO Interview Experience | Latest at the End of August

interviewshow-cs — Thu, 03 Sep 2026 13:12:26 +0000

I recently finished the Anthropic SWE Virtual Onsite after completing OA1, OA2, and the Take-Home Project.

Anthropic's SWE interview process feels quite different from that of most traditional tech companies. It is not simply about solving LeetCode problems quickly. The interviews focus much more on whether you can design reliable systems in realistic engineering scenarios, reason about concurrency and distributed systems, and think seriously about AI safety.

The four rounds moved quickly, and interviewers frequently added new constraints after I proposed a solution. The key was being able to adapt the design while keeping the system reliable.

My four rounds were:

R1: Coding — Rate Limiter
R2: Coding — Lock-Free Queue
R3: System Design — Web Crawler
R4: Behavioral — AI Safety, Teamwork, and Why Anthropic

Each round lasted approximately 60 minutes.

Anthropic SWE Interview Process Overview

The overall process I went through was:

OA1 (CodeSignal) → OA2 → Take-Home Project → Four-Round VO → Offer

The VO can be completed in one day or split across two days depending on scheduling. Unlike a typical LeetCode-style interview, the focus is heavily on practical engineering, system behavior, trade-offs, and communication.

Round 1: Coding — Distributed Rate Limiter

The first round started directly with an engineering implementation problem: design a rate limiter.

The requirements included:

Maximum 100 requests per user per minute
Maximum 1,000 requests per organization per minute
The service needs to work correctly in a distributed environment

I started with a Token Bucket implementation. Each bucket keeps track of the remaining tokens and the last refill timestamp. When a request arrives, we calculate how many tokens should have been replenished and then determine whether the request can proceed.

The interviewer quickly added another constraint:

If the rate limiter is deployed across multiple machines, how would you keep the counters consistent?

This required moving the shared state outside of the individual application instances. I suggested using Redis with atomic operations and TTL-based expiration.

The next follow-up was:

What happens if Redis becomes slow or temporarily unavailable?

Instead of simply retrying everything, I discussed a graceful degradation strategy. For example, we could maintain a short-lived local cache and tolerate a small amount of temporary counting inconsistency while monitoring Redis health and triggering alerts.

The interviewer also asked about the trade-offs between Token Bucket and Sliding Window approaches.

Token Bucket:

Relatively simple to implement
Handles bursts naturally
Does not provide the same precision as a sliding-window approach for certain short time windows

Sliding Window:

More precise control
Can require more memory and computation

This round made one thing very clear:

Anthropic cares a lot about engineering trade-offs.

It is not enough to know how to implement something. You also need to explain why you chose that design and what you would sacrifice by choosing it.

Round 2: Coding — Lock-Free Queue

The second round focused much more heavily on concurrency.

The task was to implement a lock-free queue supporting concurrent enqueue and dequeue operations without using traditional locks.

I started from the concept of CAS (Compare-And-Swap) and discussed a linked-list-based lock-free queue.

For enqueue, the idea is to use CAS to update the tail. For dequeue, CAS is used to move the head forward.

The interviewer then asked about the ABA problem.

This is where concepts such as tagged pointers and hazard pointers become relevant. The important part is recognizing that seeing the same pointer value does not necessarily mean the underlying object has remained unchanged.

Another interesting follow-up was:

How does Python's GIL affect your lock-free implementation?

This is a topic worth preparing if you are interviewing with Python.

The GIL should not simply be treated as a replacement for proper concurrency control. It does not eliminate the need to reason about concurrency, and it does not solve synchronization problems in multiprocessing scenarios.

The interviewer then asked:

What if the queue needs to support priorities?

I discussed using a Skip List as a possible underlying structure and explained that a complete lock-free priority queue would be significantly more complicated.

I was transparent about the implementation complexity and gave a conceptual solution rather than trying to fake a complete implementation within the remaining interview time.

The interviewer agreed with the direction and moved on.

My main takeaway from this round:

You need to understand concurrency beyond the API level.

Round 3: System Design — Web Crawler

The third round was a System Design interview focused on building a Web Crawler.

Before jumping into the architecture, I clarified several requirements:

Single-domain or multi-domain crawling?
Expected number of URLs?
Maximum crawl depth?
Do we need to respect robots.txt?
How will the crawled data be stored and consumed?

URL Scheduling

I proposed using a priority queue to manage URLs waiting to be crawled.

For deduplication, a Bloom Filter can significantly reduce memory usage compared with maintaining a complete HashSet, at the cost of allowing a very small false-positive rate.

Fetching Layer

For the fetching layer, I suggested asynchronous requests with concurrency control.

One particularly important detail was per-host rate limiting.

A crawler should not simply maximize its own throughput. It also needs to respect the capacity of the websites it is crawling.

The design should also account for robots.txt and crawl-delay requirements.

Storage Layer

Raw HTML could be stored in object storage such as S3, while parsed structured data could go into a database.

URL state and crawl scheduling information could be stored in a distributed key-value store to make scheduling and recovery easier.

The interviewer then introduced several additional constraints.

What if dynamically generated URLs cause URL explosion?

We can normalize URLs before deduplication, remove meaningless parameters where appropriate, and impose a maximum crawl limit per domain.

What if fetching a URL times out?

Use exponential backoff with a maximum retry count. URLs that continue to fail can be moved into a dead-letter queue with failure information recorded for later analysis.

How would you implement graceful shutdown?

After receiving a shutdown signal, the crawler should stop accepting new work, allow in-flight requests to finish, persist the current queue state, and restore unfinished work when the service starts again.

This round felt particularly representative of Anthropic's engineering philosophy.

If you only focus on making the crawler faster, it is easy to forget about robots.txt, per-host rate limiting, and the impact your system has on external services.

Those details demonstrate a more responsible engineering mindset rather than simply optimizing for throughput.

Round 4: Behavioral — AI Safety, Teamwork, and Why Anthropic

The final round was behavioral, but it was probably the round with the deepest follow-up questions.

AI Safety

One question was essentially:

What do you think is the most serious safety risk in current AI systems, and what can you do about it as an engineer?

There is no single correct answer to this question.

What matters is whether you can discuss a concrete risk scenario, explain how the risk could occur, and propose engineering-level mitigations.

For example, I discussed ideas such as:

Output monitoring
Abuse detection
Rate limiting
Architectural safeguards
Continuous monitoring and alerting

The key is not memorizing an AI Safety answer. The interviewer wants to see whether you have genuinely thought about safety as part of engineering.

Teamwork and Technical Disagreements

Another question focused on a situation where I had a strong disagreement with a teammate.

The follow-ups went deeper:

Why did you make that decision?
Did you understand the other person's concerns?
How was the final decision made?
What would you do differently if you could do it again?

Anthropic does not seem particularly interested in a generic answer such as “we eventually reached consensus.”

They want to understand your decision-making framework, whether you can maintain your own judgment during disagreement, and whether you are genuinely willing to listen to other perspectives.

Why Anthropic / Why This Role?

This question also felt more important at Anthropic than at many other companies.

The interviewer is trying to understand whether you genuinely care about what Anthropic is building or simply see it as another strong software engineering opportunity.

If you are preparing for Anthropic, I would recommend learning about Constitutional AI, the Responsible Scaling Policy, AI Safety, and Anthropic's broader research and product direction.

Anthropic SWE VO Preparation Guide

Coding

Rate Limiter
Lock-Free Queue
Thread Pool
asyncio
CAS and concurrency primitives
Race conditions
Distributed concurrency
Python GIL

System Design

Web Crawler
Distributed Cache
Real-Time Systems
Rate Limiting
Multi-Tenant Systems
High-Concurrency Services

Behavioral

AI Safety
Technical disagreements
Ownership
Project failures
Cross-team collaboration
Why Anthropic?
Responsible engineering

For coding interviews in particular, don't prepare only for the initial implementation.

Get comfortable with follow-up questions such as:

What if the system scales?

What if Redis goes down?

What if requests become highly concurrent?

What if this component becomes the bottleneck?

What are the trade-offs?

This is one of the biggest differences I noticed during the Anthropic VO.

FAQ

Is the Anthropic VO completed in one day?

It can be completed in one day, although some candidates may have the interviews split across two days depending on scheduling, time zones, and recruiter availability. Each round is typically around 60 minutes.

How long does it take to hear back after the Take-Home Project?

The timeline can vary by role and hiring batch. Candidates may wait one or more weeks after submitting the Take-Home Project before receiving the VO invitation.

What tools are used during the VO?

The interviews generally use a shared coding environment such as CoderPad or a similar collaborative editor. The emphasis is on explaining your reasoning while coding rather than simply submitting a solution through a LeetCode-style platform.

How is Anthropic's Behavioral interview different from Amazon's Leadership Principles?

Amazon has a clearly defined Leadership Principles framework. Anthropic does not follow the same type of fixed framework.

Instead, candidates should be prepared to discuss AI Safety, technical disagreements, teamwork, ownership, responsible engineering, and Why Anthropic. The follow-up questions can feel more conversational and exploratory rather than like a checklist.

How deeply should I prepare for concurrency?

I would recommend being comfortable with Rate Limiters, Lock-Free Queues, Thread Pools, asyncio, CAS, race conditions, synchronization, distributed concurrency, and the Python GIL.

You should be able to implement basic versions while also explaining the trade-offs and failure modes in distributed environments.

Can I interview at Anthropic without an AI Safety background?

Yes. You do not need to be an AI Safety researcher.

However, you should be able to demonstrate that you have seriously thought about AI safety from an engineering perspective. Reading Anthropic's public materials on Constitutional AI and the Responsible Scaling Policy can help you understand the company's approach.

Why is per-host rate limiting important in a Web Crawler interview?

Because a good crawler should not optimize only for its own throughput. It should also consider the impact it has on external websites.

Per-host rate limiting, robots.txt compliance, retries, and graceful degradation demonstrate that you are thinking about responsible system design rather than simply maximizing performance.

Final Takeaways

After completing all four rounds, the biggest difference I noticed between Anthropic and traditional big-tech interviews is that Anthropic seems less interested in whether you can quickly recall a standard solution and much more interested in how you reason through an incomplete problem.

This is especially obvious in System Design and AI Safety.

The initial problem may not look extremely difficult. The challenge is that the interviewer keeps changing the constraints.

When preparing, don't just memorize system design templates. For every problem, ask yourself:

What happens if traffic increases by 10x?
What happens if a dependency fails?
What happens if users abuse the system?
What happens if the design itself introduces a new security risk?

If you can reason through those questions clearly, you'll be much better prepared for the Anthropic VO.

For anyone preparing for Anthropic or other AI-company SWE interviews, I would prioritize concurrency, distributed systems, System Design, and AI Safety-related behavioral preparation.

InterviewShow also covers interview preparation for Anthropic, OpenAI, Google, and other top technology companies, including Rate Limiter follow-ups, Web Crawler System Design, concurrency questions, and AI Safety behavioral interviews.

If you're preparing for an upcoming interview and want structured one-on-one preparation, you can learn more at InterviewShow.

Oracle SDE Interview Experience: 4 Rounds in 4 Hours | August 31 Update

interviewshow-cs — Tue, 01 Sep 2026 14:01:58 +0000

I recently finished my Oracle SDE interview, and all four rounds were scheduled back-to-back on the same day, taking around four hours in total.

Overall, Oracle's interview style felt pretty practical. The coding questions were around LeetCode Medium level, while the System Design round focused heavily on whether you could clearly explain your trade-offs. The Behavioral rounds also went much deeper into project details than I expected.

My biggest takeaways were:

Coding: LC Medium fundamentals are enough, but follow-up questions matter.
System Design: Be prepared to explain why you chose one approach over another.
Behavioral: Don't just memorize STAR stories. Be ready to explain your technical decisions and ownership.

Here is my interview experience round by round.

Round 1: Coding

This was the most technical round of the four, with two coding questions.

Question 1: Binary Tree Maximum Path Sum

This was the classic LeetCode 124 – Binary Tree Maximum Path Sum.

The standard solution is a post-order DFS traversal.

For every node, calculate the maximum contribution that can be returned from the left and right subtrees. If a subtree contributes a negative value, simply discard it:

max(0, subtree contribution)

Then use:

left contribution + current node + right contribution

to update the global maximum path sum.

When returning to the parent node, only one side can be selected:

node + max(left contribution, right contribution)

The follow-up was:

Can you also return the actual path?

To handle this, I needed to track the node sequence corresponding to the current best path. Whenever the global maximum was updated, I also saved the corresponding path.

The interviewer then introduced a concurrency-related follow-up: what happens if the global state is modified by multiple threads?

I discussed protecting the shared state with a lock to make the update thread-safe.

It was interesting because the original problem was not a concurrency problem at all, but the interviewer used the follow-up to see whether I could think beyond the basic algorithm and consider how the implementation would behave in a real engineering environment.

Question 2: First Non-Repeating Character

The second question was straightforward.

Given a string, find the first character that appears only once and return its index.

The standard approach is:

Use a hash map to count the frequency of every character.
Scan the string again from left to right.
Return the index of the first character whose frequency is 1.

The time complexity is O(n).

If the character set is fixed to 26 lowercase English letters, the auxiliary space can be considered O(1).

The follow-up was:

What if the string is extremely large and cannot fit into memory?

In that case, you should not assume the entire string can be loaded into memory at once. A possible approach is to process the input in chunks and maintain the necessary frequency information incrementally.

The broader lesson from this round was that Oracle does not necessarily stop once you have the correct algorithm. The interviewer may ask how the same solution would behave under real-world constraints.

Round 2: Behavioral Interview

Oracle's Behavioral round was not just a collection of standard behavioral questions. The interviewer often followed the story with technical questions about the decisions I made during the project.

Tell Me About a Challenging Project You Worked On

I talked about a distributed caching optimization project.

After I explained the project, the interviewer asked:

Why did you choose this solution instead of simply adding more machines?

This was a good example of why memorizing a STAR story is not enough.

I had to explain the complete reasoning behind the decision, including:

What the actual bottleneck was
Why horizontal scaling alone would not solve the problem
Resource and infrastructure costs
Latency considerations
Maintainability
Alternative approaches that were considered

The important part was being able to explain why I made the decision, rather than simply describing what I implemented.

Tell Me About a Time You Worked With Someone Whose Personality Was Very Different From Yours

This was another collaboration-focused question.

The interviewer was interested in how I handled real friction within a team.

Instead of ending the story with “we eventually reached an agreement,” it was much more useful to explain:

What caused the disagreement
What the other person's concerns were
What I believed at the time
What actions I personally took
How the disagreement was ultimately resolved

I would recommend preparing these stories several levels deeper than the initial STAR structure.

How Do You Prioritize Tasks When You Have Multiple Deadlines?

This question sounds like a basic time-management question, but I felt Oracle was really testing ownership and execution.

Instead of only talking about general prioritization frameworks, be prepared to explain:

How you determine which task is most important
What you do when two tasks are both high priority
How you handle conflicting deadlines
When and how you communicate with stakeholders
What you do if it becomes impossible to finish everything on time

Round 3: Bar Raiser

Oracle's Bar Raiser, sometimes referred to internally as a Bar Tender, is typically a senior engineer from another team.

This round focused heavily on:

Problem ownership
Technical depth
Cross-team collaboration
Communication

There was also a technical question mixed into the behavioral discussion.

Resume Deep Dive

The interviewer went deep into my resume and asked about topics such as:

API Gateway architecture
Microservice decomposition
Docker
Kubernetes
Scalability improvements

This is why I would strongly recommend being prepared to explain every technology listed on your resume.

If you mention Kubernetes, for example, you should be ready for questions such as:

Why did you choose Kubernetes for this system?

Knowing a technology name is very different from understanding why it was used.

Technical Question: Combinatorics

There was also a combinatorics-related problem in this round.

I did not need to write a complete implementation. The interviewer mainly wanted to hear the reasoning, approach, and complexity analysis.

While Working on a Team, How Did You Deal With a Conflict?

This question came with several follow-ups:

What was the root cause of the conflict?
What was your initial reaction?
Why didn't you use the other person's approach?
How did you reach the final decision?
What would you do differently if you could do it again?

This follow-up chain makes the Bar Raiser round particularly important to prepare for.

For each behavioral story, I would recommend thinking through not just the STAR structure, but also the reasoning behind your decisions.

Tell Me About a Time You Had to Learn Something New Quickly to Deliver a Feature

The two important words here are “quickly” and “deliver.”

The interviewer is not simply asking what new technology you learned.

A stronger answer explains:

Why you needed to learn it quickly
How you approached the learning process
How you decided what was important to learn
How you applied the knowledge
Whether you actually delivered the feature successfully

Round 4: System Design — File Conversion System

The final round was a System Design question about building a file conversion system.

The basic requirement was:

A user uploads a file, the system converts it into a target format, and the user receives the converted result.

This type of problem is closely related to large-scale file storage and asynchronous job processing.

Requirement Clarification

Before discussing the architecture, I clarified several requirements:

What file formats need to be supported?
What is the maximum file size?
How long should converted files be stored?
What is the expected traffic and concurrency?
Should conversion be synchronous or asynchronous?

Once the requirements were clear, I started designing the system.

High-Level Architecture

The overall architecture looked roughly like this:

Client
  ↓
Object Storage
  ↓
Message Queue
  ↓
Worker Pool
  ↓
Object Storage
  ↓
Notification

A more concrete implementation could use:

Object Storage: Store uploaded files and conversion results
Kafka: Queue conversion jobs
Worker Pool: Process conversion tasks
Webhook / Polling: Notify users when the conversion is complete

The key idea is to decouple file uploads from the actual conversion process.

This prevents a user request from remaining open while a potentially expensive conversion is running and makes it easier to scale the worker layer independently.

Follow-up 1: Error Handling and Retries

Conversion failures need more than simply returning a 500 error.

The system should consider:

Retries
Exponential backoff
Maximum retry count
Dead-letter queues
Failure reason tracking
User notifications

After the maximum number of retries is reached, the task can be moved to a dead-letter queue and the failure reason recorded for debugging and monitoring.

Most importantly, the user should not be left with a task that remains in “processing” forever.

Follow-up 2: Scalability

What happens if the number of conversion requests suddenly increases?

The worker pool can scale based on metrics such as:

Queue depth
Processing latency
Worker utilization

For very large files, another possible optimization is:

File chunking
Parallel processing
Chunk merging

This prevents a single large file from occupying one worker for an excessive amount of time.

Follow-up 3: Performance Optimization

I discussed two main optimization strategies.

Deduplicate Identical Files

Calculate a hash for the input file.

If the same input hash and target format have already been processed, the system can reuse the existing conversion result rather than performing the same computation again.

Cache Popular Results

Frequently downloaded conversion results can be served through a CDN.

This reduces repeated requests to the origin storage layer and improves download latency for users.

The Architecture Diagram Wasn't the Most Important Part

The interviewer explicitly emphasized that the goal was not to draw a “perfect” architecture.

What mattered more was the reasoning process and trade-offs.

For example:

Why Kafka?
Why asynchronous processing?
Why object storage instead of a database?
When would file chunking be necessary?
Why retry failed conversions?
When should the system stop retrying?

Being able to explain the reasoning behind each decision was more important than producing a textbook architecture diagram.

Final Takeaways

Overall, I don't think Oracle is as algorithm-focused as companies like Google or Meta.

If you are comfortable with LeetCode Medium-level problems, the coding portion should be manageable.

The bigger differentiators are elsewhere.

1. Be Ready for Coding Follow-ups

Don't stop once you have the correct solution.

Be prepared to discuss complexity, edge cases, scalability, memory constraints, and how the algorithm would behave in a real production environment.

2. Focus on Trade-offs in System Design

Don't simply memorize System Design templates.

The more important skill is being able to explain:

Why did you choose A instead of B?

And then adjust your design when the requirements change.

3. Prepare BQ Stories Around Ownership

For every major project on your resume, I would prepare for questions such as:

Why did you make this decision?
Why not use another approach?
What was your specific contribution?
What went wrong?
What would you do differently?

Being able to answer these questions naturally is much more useful than memorizing a polished story.

Preparing for Oracle SDE Interviews

If you're preparing for Oracle, Microsoft, Google, or other North American software engineering interviews, InterviewShow offers interview preparation covering System Design trade-offs, project deep dives, behavioral interview preparation, and other SWE interview topics.

The biggest lesson from this Oracle interview was simple: don't just prepare for the first question. Prepare for the follow-up.

TikTok OA Questions: 4 Coding Problems Solved in Under 30 Minutes

interviewshow-cs — Mon, 31 Aug 2026 15:24:09 +0000

Just finished another TikTok Online Assessment on CodeSignal. This one took less than 30 minutes to complete all four questions.

The difficulty wasn't particularly high. Most of the problems were focused on implementation, simulation, and basic algorithm patterns. The main challenge was reading the requirements carefully and not missing small constraints.

Here are the four questions I encountered this time.

T1: Product of Digits Minus Sum of Digits

Given a positive integer n, calculate the product of all digits minus the sum of all digits.

For example:

n = 123456

Product = 1 × 2 × 3 × 4 × 5 × 6 = 720
Sum = 1 + 2 + 3 + 4 + 5 + 6 = 21

Answer = 720 - 21 = 699

The implementation is straightforward. Repeatedly use n % 10 to extract the last digit and n //= 10 to remove it.

Maintain two variables for the product and sum, then return product - sum.

Time complexity: O(log n)

One small detail: use a sufficiently large integer type because the intermediate product can become much larger than the original number.

T2: Add One Pair of Parentheses to an Addition Expression

This one looks simple but has several conditions that are easy to misread.

Given an expression consisting of two positive integers separated by +, such as:

741+12

You must add exactly one pair of parentheses.

The + must be inside the parentheses.
There must be at least one digit on both sides of the + inside the parentheses.
The goal is to minimize the resulting expression value.

For each valid placement of the parentheses, calculate the value in the form:

leftNumber × (insideLeft + insideRight) × rightNumber

Since the string is short, there is no need for anything complicated. I simply enumerate all valid positions for the left and right parentheses, calculate the resulting value, and keep the minimum.

The important part is making sure the enumeration respects all the constraints around the + sign.

T3: Memory Allocation and Release

This was a classic simulation problem.

The memory array uses:

0 for a free memory unit
1 for an occupied memory unit

There are two operations.

alloc X

Scan from left to right and find the first contiguous block containing at least X free units.

Mark that block as occupied and return its corresponding ID or starting index, depending on the exact problem specification.

erase ID

Use the ID to locate the previously allocated block and turn those memory units back to 0.

My implementation used:

Array simulation + HashMap

The HashMap stores the mapping between the allocation ID and its corresponding memory range. This makes the erase operation straightforward.

One thing to watch out for is fragmentation. After multiple alloc and erase operations, the free memory may be split into many small blocks. Each new allocation still needs to search from the left for the first valid contiguous block.

T4: Place a Lamp to Cover the Maximum Number of Objects

Given an array objects containing object coordinates in ascending order, place a lamp at an integer coordinate.

The lamp has a coverage radius of radius, so a lamp placed at coordinate c covers:

[c - radius, c + radius]

The goal is to maximize the number of covered objects. If multiple positions cover the same maximum number of objects, return the smallest valid lamp coordinate.

The clean solution is a Sliding Window.

Maintain a window from l to r. As long as:

objects[r] - objects[l] ≤ 2 × radius

there exists a lamp position that can cover the entire window.

Move the right pointer forward, and when the window becomes too wide, move the left pointer forward.

For every valid window, track:

the maximum number of objects covered
the smallest lamp coordinate when the coverage count is tied

Time complexity: O(n)

Quick Summary

Question	Core Concept
T1	Digit Simulation
T2	Enumeration + String Processing
T3	Array Simulation + HashMap
T4	Sliding Window

Overall, this was a very implementation-heavy OA. There wasn't much advanced algorithmic theory involved.

The biggest things to pay attention to were the small requirements:

Product minus sum, not the other way around
Exactly one pair of parentheses
The + must be inside the parentheses
First contiguous free block for allocation
For equal maximum coverage, choose the smaller lamp coordinate

If you can quickly recognize basic patterns such as simulation, enumeration, HashMap, and Sliding Window, this type of OA becomes much more manageable.

TikTok CodeSignal assessments also share some common patterns with OA processes at companies such as Amazon, Microsoft, Uber, Roblox, and others. Practicing implementation-heavy problems under time pressure can make a noticeable difference.

How to Prepare for TikTok OA

For this type of assessment, I would prioritize speed and accuracy over grinding extremely difficult problems.

Practice common CodeSignal-style implementation problems.
Review Sliding Window, HashMap, sorting, and array simulation.
Practice reading long problem statements quickly.
Pay special attention to tie-breaking rules and edge cases.
Do timed practice so that four questions in one session doesn't feel unfamiliar.

If you're preparing for TikTok or other software engineering OA/VO processes, InterviewShow also provides interview preparation resources, including Coding, System Design, behavioral interview preparation, and mock interview practice.

Final Takeaway

This TikTok OA was a good reminder that not every assessment is about solving the hardest possible algorithm problem.

Sometimes the difference between passing and failing is simply whether you noticed one sentence in the requirements.

For implementation-heavy OA questions, I would rather be extremely comfortable with the fundamentals and finish four medium-level problems cleanly than spend all my preparation time on difficult algorithms.

NVIDIA Interview Experience: Coding, System Design, OS & AI Infrastructure

interviewshow-cs — Thu, 27 Aug 2026 14:38:40 +0000

NVIDIA interviews are quite different from those at most big tech companies. Algorithms are still important, but Operating Systems, low-level systems, and infrastructure fundamentals are also heavily tested.

The exact process varies significantly by team, but a few patterns seem consistent: interviewers frequently drill into low-level details, project deep dives are genuinely deep, and OS fundamentals carry much more weight than they do at many traditional software companies.

Here is how my NVIDIA interview process went, round by round.

Recruiter Call

The recruiter call was relatively short, but it was more technical than I expected. I mentioned that I had worked on inference optimization, and the conversation immediately moved into technical details.

I was asked where latency was coming from, whether the bottleneck was compute or I/O, and whether I had used batching.

That gave me an early sense of what NVIDIA was looking for: if your background involves AI workloads, you should expect to explain the underlying performance characteristics rather than simply describe the project at a high level.

Technical Screen: Streaming Computation + Low-Level Follow-Ups

The technical screen started with a relatively simple C++ coding problem: calculate the average of a set of numbers.

The algorithm itself was straightforward. The interesting part came from the follow-ups.

Streaming Data

I was asked how I would handle numbers arriving as a continuous stream instead of having the entire dataset available upfront.

This shifts the discussion toward maintaining the necessary state incrementally rather than storing the entire input.

SIMD and Vectorization

The interviewer then asked what SIMD vectorization is and whether it could be applied to this type of computation.

This was less about memorizing the definition of SIMD and more about understanding when parallel operations can improve throughput and what constraints might prevent vectorization from being effective.

CPU Cache

We also discussed set-associative caches, how cache lookup works, and how cache misses could appear in a computation like this.

That was the main theme of this round: the interviewer took a simple coding problem and connected it to memory access patterns, CPU caches, and hardware-level optimization.

Project Deep Dive

The project discussion was much deeper than a typical resume walkthrough.

I talked about a data pipeline optimization project and ended up spending more than ten minutes going through the technical details.

The interviewer asked:

Why did you choose this architecture?
How did you measure throughput?
Did you use profiling?
Where were the cache misses happening?
Why didn't you use an asynchronous queue?
How did you control memory footprint?

This is one area I would specifically prepare for. At NVIDIA, being able to say "the system became 30% faster" is not enough. You should be able to explain why it became faster, how you measured it, where the original bottleneck was, and what evidence supported your design decisions.

VO Round 1: Algorithms

The first Virtual Onsite round focused on algorithms.

One of the problems involved implementing inorder traversal using a stack and then optimizing the solution.

The basic solution uses an explicit stack to simulate recursive traversal. The follow-up asked whether the auxiliary space could be reduced to O(1).

The expected direction was Morris Traversal, which uses threaded binary tree concepts to perform inorder traversal without maintaining an additional stack.

The important point was not simply knowing the optimization. I also had to explain why the approach works and what trade-offs it introduces.

That seems to be a recurring NVIDIA interview pattern: solving the coding problem is only part of the evaluation. You should be able to explain the design decisions behind your solution.

VO Round 2: System Design

The System Design discussion focused on AI infrastructure. Common directions include:

Distributed model serving
GPU resource allocation
Real-time inference platforms
Large-scale training pipelines

The biggest difference from a typical high-level System Design interview was the depth of the follow-ups.

If you say you would use a message queue, expect questions about when the queue itself becomes the bottleneck.

If you propose caching, be prepared to explain the invalidation strategy, consistency requirements, and what happens when cached data becomes stale.

In other words, drawing a clean architecture diagram is not enough. The interviewer wants to understand whether you know what happens inside each component and where the real bottlenecks could emerge.

VO Round 3: Operating Systems — Design an OS Scheduler

This was the biggest differentiator of the entire interview process.

The problem was to design an OS scheduler, including the choice of scheduling algorithms, selecting an appropriate algorithm for an autonomous-driving scenario, and handling priority inversion.

Scheduling Algorithm Trade-Offs

The interviewer wanted to understand how the requirements of the workload influence the scheduling strategy.

For an autonomous-driving workload, for example, real-time guarantees and deadline-aware scheduling become much more important than simply maximizing average throughput.

Priority Inversion

We also discussed how to handle priority inversion, where a high-priority task can be indirectly blocked by a lower-priority task holding a required resource.

Other OS Fundamentals

The discussion also touched on several lower-level concepts:

Virtual memory implementation
CPU cache hierarchy
NUMA and its impact on memory access latency
Lock-free data structures

This round was not about reciting textbook definitions. The interviewer kept connecting the concepts to concrete system scenarios and asking about their practical impact.

If you have only prepared LeetCode and high-level System Design, this round can be very difficult. For NVIDIA roles involving systems, performance, AI infrastructure, or C++, I would treat OS and low-level fundamentals as a dedicated preparation area.

VO Round 4: Behavioral

The final round focused on behavioral questions, but the discussion remained strongly technical.

Some common themes included:

The most difficult optimization you have worked on
How you collaborated with a research team
How you handled an architecture disagreement
Whether you have dealt with a production incident

For optimization questions, simply saying "latency improved by 40%" is not enough. Be prepared to explain the original bottleneck, what you changed, how you measured the improvement, and why the optimization actually worked.

NVIDIA's behavioral discussion can therefore feel more technical than the behavioral interviews at many other companies. The interviewer is interested in your technical judgment and decision-making, not just whether you work well with a team.

What I Would Focus on When Preparing for NVIDIA

1. Prepare for the Project Deep Dive

The Technical Screen project discussion is an area many candidates underestimate. Know your projects beyond the resume bullets.

You should be able to discuss profiling results, bottlenecks, throughput, latency, memory usage, cache behavior, architectural alternatives, and why you rejected other approaches.

2. Take OS Preparation Seriously

OS was the biggest dividing line for me. If you're interviewing for a role close to systems, GPUs, AI infrastructure, performance, or C++, don't assume that algorithm preparation alone will be enough.

Review scheduling, virtual memory, cache hierarchy, synchronization, concurrency, NUMA, lock-free programming, and other relevant systems fundamentals.

3. Go Deeper Than the Architecture Diagram

For System Design, practice defending every major component. If you choose a queue, cache, database, scheduler, or load balancer, understand its bottlenecks, failure modes, consistency implications, and scaling limits.

Final Thoughts

The biggest difference between NVIDIA and a typical internet-tech interview is the emphasis on low-level engineering and performance.

Algorithms still matter, but they are only one part of the picture. The interview can move from a simple coding problem to SIMD, CPU caches, memory access patterns, OS scheduling, concurrency, or GPU infrastructure within a few follow-up questions.

If you're preparing for NVIDIA or another AI infrastructure company, check out
InterviewShow.
We cover interview preparation for companies including NVIDIA, Google, and Meta, with focused practice on OS and low-level systems questions, AI infrastructure System Design, and project deep dives.

Good luck with your NVIDIA interview!

Capital One Power Day Interview Experience: 4 Rounds in One Day

interviewshow-cs — Tue, 25 Aug 2026 13:01:00 +0000

Capital One's Power Day is a full-day interview loop with four rounds:
Behavioral → Coding → Case Study → System Design.
Compared with many big tech interview loops, I found it relatively straightforward to prepare for because the question overlap is surprisingly high. A lot of online interview reports line up closely with the types of questions that actually show up.

Here is a breakdown of all four rounds, along with some preparation notes that may be useful if you're getting ready for a Capital One Software Engineer interview.

Overall Interview Structure

The four rounds are usually scheduled on the same day, with each round lasting roughly 45–60 minutes and relatively short breaks in between.

The structure feels somewhat similar to an Amazon Loop, but the content is more closely tied to financial and banking applications. Coding questions often involve accounts and transactions, while Case Study and System Design questions frequently involve virtual cards, compliance, security, and auditing.

If you prepare the common banking system and virtual card patterns in advance, a large part of the interview can feel like applying a familiar framework rather than solving everything from scratch.

Round 1: Behavioral Interview

The first round was a behavioral interview lasting around 20–30 minutes. The discussion was structured around the STAR framework.

Some of the recurring themes include:

Challenge the Status Quo: Tell me about a time you challenged an existing solution and pushed for a change.
Handle Something Unfamiliar: Tell me about a time you had to quickly learn an unfamiliar technology or domain.
Most Challenging Project: Describe the most technically challenging project you worked on and the trade-offs you made.
Mentor / Leader: Tell me about a time you mentored someone or helped move a team forward.

My questions focused on stepping back and reconsidering an approach after running into an obstacle, handling conflicts with other people, and the most difficult technical challenge I had encountered in a project.

The biggest lesson here is that your story needs to have a complete structure. Don't stop at "we eventually solved the problem." Explain the context, your specific actions, the reasoning behind your decisions, and the measurable result.

Round 2: Coding

Coding had the highest level of question overlap. Recent interview reports consistently point toward banking system problems and business-oriented object-oriented programming.

Warm-Up: Valid Parentheses

The warm-up was a standard Valid Parentheses problem. Use a stack to store opening brackets and compare every closing bracket against the top of the stack. If there is a mismatch or the stack is empty, return false. At the end, the stack must also be empty.

A common follow-up is to return the position of the mismatched bracket. One approach is to store (character, index) in the stack and track both unmatched opening brackets and invalid closing brackets.

Core Problem: Banking System

The main problem was similar to LeetCode 2043 — Simple Bank System, but with additional functionality.

The system needed to support operations such as:

Create an account
Deposit money
Withdraw money
Transfer money between accounts

The important part was handling business errors correctly, including nonexistent accounts, insufficient balances, and invalid transactions.

Common Follow-Ups

Transaction History

Each account can maintain a transaction history, with support for querying the most recent N transactions or transactions within a specific time range.

Top N Active Accounts

If accounts need to be ranked by transaction count or transaction amount, a min-heap can maintain the top N accounts in roughly O(m log N) time.

The evaluation was not purely about algorithmic correctness. The interviewer also looked at OOP design, class responsibilities, balance consistency, and error handling.

Some interviewers may also ask how you would make concurrent transfers safe, including approaches such as locking or optimistic concurrency control to prevent inconsistent balances or duplicate deductions.

Round 3: Case Study — Code Review

The third round was a Case Study / Code Review.

You are given an existing piece of code and asked to understand it, identify logical or business issues, and suggest refactoring improvements. The goal is usually not to rewrite the entire codebase on the spot.

A common scenario involves a virtual credit card system, including card number generation, transaction validation, and authorization logic.

The key skills being evaluated are:

Understanding unfamiliar data structures and state transitions quickly
Identifying business logic bugs rather than only syntax problems
Spotting missing idempotency or incomplete state handling
Recognizing incorrect validation logic
Explaining how you would improve the design and why

One common mistake is focusing entirely on code style. In a financial application, you should also ask whether the business state transitions are correct and whether the same request could accidentally be processed twice.

The goal isn't to say, "I would rewrite everything." A stronger answer explains the specific problem, its potential impact, and the smallest reasonable change that would fix it.

Round 4: System Design

System Design questions commonly focus on a credit card account management system or a virtual card system.

Virtual Card Design

For a virtual card system, the design can be broken into several key areas:

Generating globally unique card identifiers
Mapping primary and secondary cards with low-latency lookups
Managing one-time, spending-limit, and time-based restrictions
Managing card lifecycle states and soft deletion

For unique ID generation, approaches such as Snowflake-style IDs can be discussed. Redis can be used for low-latency lookups between primary and virtual cards, depending on the consistency requirements.

Security and Compliance Matter

Because Capital One operates in the financial industry, System Design discussions tend to go deeper into security, compliance, and auditability.

Some topics worth proactively bringing up include:

Passwords: Use bcrypt with appropriate salting instead of storing plaintext passwords or using outdated hashing approaches such as MD5.
Encryption Keys: Consider HSM or KMS and establish a key rotation strategy.
PII: Encrypt sensitive information at rest and restrict access through appropriate authorization controls.
Authentication & Authorization: Separate authentication from authorization and enforce least-privilege access.
Auditing: Maintain audit logs for sensitive financial operations and administrative actions.

Bringing up these concerns proactively can make the design discussion much stronger than waiting for the interviewer to ask about security or compliance.

Common Mistakes to Avoid

There are a few patterns that can easily hurt your score during a Capital One Power Day.

Behavioral: Your story describes the process but never clearly explains the result or the reasoning behind your decision.
Coding: The code works, but responsibilities are mixed together and error handling is inconsistent.
Case Study: You focus only on syntax or code style instead of identifying business-level problems.
System Design: You draw a large architecture diagram but fail to discuss security, compliance, idempotency, or auditing.

How I Would Prepare

Round	Preparation Focus
BQ	Prepare 4–6 STAR stories covering challenging the status quo, conflict, unfamiliar domains, leadership, and difficult projects.
Coding	Practice banking accounts, transaction history, Top N problems, and use Valid Parentheses as a warm-up.
Case Study	Practice reading unfamiliar code and identifying issues around state, validation, and idempotency.
System Design	Prepare one solid virtual card or banking account architecture and integrate security and compliance into the design.

The biggest advantage of preparing for Capital One is the relatively high degree of question overlap. Banking systems and virtual card Case Study/System Design questions show up frequently, so targeted preparation can be much more effective than randomly covering hundreds of unrelated problems.

For coding, clean OOP structure and complete error handling can matter as much as getting the algorithm to work. For System Design, proactively discussing password hashing, auditing, encryption, authorization, and key rotation can demonstrate that you understand the requirements of financial systems.

How Capital One Compares With Other Companies

Compared with Amazon, Capital One generally has less emphasis on deep Leadership Principle-style behavioral questioning. Compared with pure consumer-tech companies, there is more emphasis on financial workflows, security, compliance, and business logic.

Compared with companies such as JPMorgan, Capital One's Power Day can feel more standardized, with a relatively concentrated set of recurring interview patterns.

Coding also feels different from a typical algorithm-heavy interview. Instead of focusing primarily on difficult LeetCode problems, the questions often combine algorithms with OOP and real-world business requirements.

Final Thoughts

If I had to summarize Capital One Power Day in one sentence, it would be: high question overlap, but strong expectations around clean engineering and financial-system thinking.

If you're preparing for Capital One or another fintech interview, we are
InterviewShow.
We cover interview preparation for companies including Capital One, JPMorgan, and Bloomberg, with focused practice on banking-system OOP, virtual card System Design, Case Study analysis, and behavioral story preparation.

Good luck with your Power Day!

Akuna OA Questions & Solutions | 3-Problem Breakdown

interviewshow-cs — Mon, 24 Aug 2026 14:07:02 +0000

Recently took an Akuna OA with three coding problems. Overall, the difficulty was manageable, focusing mainly on simulation, basic DP, and rule-based string processing.

There were no particularly tricky data structures, but the problems required careful reading and attention to edge cases. Here’s a quick breakdown of the three questions and the key ideas behind each one.

Problem 1: Server Error Replacement

Given server logs containing success or error, a server is replaced whenever it records 3 consecutive errors. After a replacement, the error counter resets. A success also resets the consecutive error count.

Approach: Use a HashMap to track the current consecutive error count for each server. Scan the logs once, incrementing the count on error and resetting it on success. When the count reaches 3, increment the replacement counter and reset the count.

Complexity: O(m) time and O(n) space.

The two details to watch are simple but easy to miss: success must reset the counter, and replacement must reset it as well.

Problem 2: Maximum Remaining Drone Battery

A drone starts with 100 units of battery and can begin from any position in the first row of an n × m grid. From each cell, it can move to the next row at column j-1, j, or j+1. Each cell consumes a certain amount of battery. The goal is to maximize the remaining battery after reaching the last row.

Approach: This is a standard grid DP problem.

Initialize the first row with:

dp[j] = 100 - city[0][j]

For every following row, take the maximum remaining battery from the valid three positions in the previous row, then subtract the current cell's cost.

Complexity: O(nm) time. A rolling array reduces the space complexity to O(m).

Problem 3: Vowel Substring Game

Alex and Chris take turns removing substrings based on the number of vowels they contain. The task is to determine the winner for each input string.

For this particular version, the practical conclusion was essentially:

String contains a vowel → Alex
No vowels → Chris

The implementation is therefore straightforward: scan each string and check whether it contains one of a, e, i, o, or u.

For different versions of this problem, make sure to follow the exact statement and examples, especially the rules involving vowel parity and the winner when there are no valid moves.

What This Akuna OA Actually Tests

This set does not rely heavily on advanced algorithms. Instead, it tests whether you can read the rules carefully, maintain state correctly, handle boundaries, and implement basic DP without mistakes.

Before submitting, I would specifically check:

Whether consecutive states are reset correctly
Whether DP initialization is correct
Whether grid boundaries are handled safely
Whether special cases match the problem statement
Whether the output format exactly matches the required format

Preparing for Akuna and Other Trading-Firm OAs?

If you're preparing for Akuna, Optiver, Stripe, Amazon, or other SDE/Quant interviews, InterviewShow collects recent interview experiences, OA questions, and commonly tested topics to help you understand the actual question styles and interview processes before you start preparing.

Knowing the patterns in advance can make your preparation much more targeted and help you avoid spending time on the wrong types of problems.