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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.

Amazon SDE2 Virtual Onsite Interview Experience: 5 Rounds in One Day

interviewshow-cs — Sat, 22 Aug 2026 15:50:26 +0000

I just finished my Amazon SDE2 Virtual Onsite (VO). Five rounds in one day, and honestly, my brain was completely empty afterward.

Looking back, I realized that I had prepared for the wrong thing. I spent a lot of time preparing for System Design, and that preparation definitely helped. But the round that pushed me closest to my limit was actually the Bar Raiser behavioral interview, especially the continuous follow-up questions.

Here is a detailed breakdown of all five rounds for anyone preparing for an Amazon SDE2 / L5 interview.

Round 1: Coding + Deliver Results

The round started with a behavioral question around Deliver Results. I was asked about a project where I had to deliver under significant time pressure.

The interviewer then went several levels deeper:

How did you prioritize when the deadline was approaching?
Did you ever sacrifice quality to meet a deadline?
What did you learn from the situation?
What would you do differently if you faced the same situation again?

This type of question is much harder than simply telling a good story. You need to be prepared for the third or fourth layer of follow-ups. Otherwise, it is very easy to run out of things to say.

Coding Questions

1. Task Scheduler Variant

Given tasks with a cooldown period, determine the minimum time required to complete all tasks. The solution uses a greedy approach.

2. Sliding Window Maximum

This was a classic LeetCode Sliding Window Maximum problem using a monotonic deque.

Both coding questions were around classic medium-level problems. However, the interviewer did not stop after the code was working. I was asked to explain the time and space complexity and walk through several edge cases.

At Amazon, finishing the code is not necessarily the end of the coding round. Be ready to explain why your solution works and what happens in unusual cases.

Round 2: Coding + Ownership

The behavioral question focused on Ownership: taking responsibility for a problem that technically was not part of your job.

One important lesson from this question is that you should not only explain what you took ownership of. The interviewer is often more interested in how you decided whether you should take ownership in the first place.

Be prepared to explain your decision-making process, the trade-offs you considered, and what happened after you got involved.

Coding Questions

1. LRU Cache

The interviewer followed up by asking how the design could be changed to support LFU Cache, and how the implementation could remain thread-safe in a concurrent environment.

2. Merge K Sorted Lists

Another classic problem involving multiple sorted linked lists. The key was being able to clearly explain the complexity and implementation trade-offs.

Round 3: OOP / Low-Level Design — Parking Lot

The third round was an Object-Oriented Design / Low-Level Design problem: design a parking lot system.

The system needed to support:

Different vehicle types
Parking spot allocation
Payment and billing
Checking available parking spaces

The main evaluation was not about how much code you could write. It was about whether your class design made sense and whether the system could be extended without requiring major changes.

I used the Strategy Pattern for the billing logic, and the interviewer seemed to respond positively to that design.

The follow-ups included:

How would you support multiple floors?
How would you handle different pricing rules?
What if the billing rules changed based on time?
How would you make the design easier to extend?

This is where the Open-Closed Principle (OCP) becomes more than a textbook concept. The interviewer wants to see whether your design can support new requirements without constantly modifying existing classes.

Round 4: System Design — URL Shortener

The System Design round was a classic URL Shortener design.

After clarifying the requirements, I proposed a read/write architecture where a new short URL generates a 7-character Base62 code, which is stored in a key-value store.

For reads, the request first goes through Redis and falls back to the database when there is a cache miss.

The interviewer then went deeper into several areas:

301 vs. 302 redirects
Handling extremely popular or "hot" URLs
Database sharding
Shard routing strategies
Read-heavy traffic patterns

I had prepared relatively well for System Design, so this round went smoothly. However, one thing I realized afterward is that System Design does not end when you finish presenting your architecture.

In many interviews, the follow-up questions are where a significant part of the evaluation happens. I had a few moments where I was slower than I wanted to be when responding to the deeper trade-off questions.

Round 5: Bar Raiser — The Hardest Round

The fifth round was the Amazon Bar Raiser interview, and by far the most difficult round of the day.

The Bar Raiser was a senior interviewer from another team. The behavioral follow-ups were significantly deeper than in the previous rounds.

Behavioral Questions

1. Disagreeing With Your Manager

I was asked about a situation where I strongly disagreed with a decision made by my manager.

The follow-ups focused on what happened after my proposal was rejected and whether I was able to execute a plan that I personally disagreed with.

2. Learning a New Technology Outside of Work

I was asked about a technology I learned purely out of personal interest.

The interviewer wanted to know how I learned it, why I chose it, and whether I eventually applied it to a real project.

3. Making a Decision With Incomplete Information

Another question focused on a decision I had made without having all the necessary information.

The interviewer asked about the outcome, what assumptions I made, and what I would change if I could do it again.

The important thing about this round is that the interviewer is not simply listening to your story. They are testing whether the logic behind the story is consistent.

Every answer can trigger another follow-up question, which can trigger another one. It feels almost like an endless chain of questions.

Coding Question

Maximum Product Subarray

This was the classic maximum product subarray problem. The key is to maintain both the maximum and minimum product ending at the current position, because a negative number can turn the smallest product into the largest one.

When encountering a negative value, the maximum and minimum states need to be swapped before updating them.

My Biggest Takeaways From the Amazon SDE2 Interview

Looking back, I think the biggest difference between Amazon SDE1 and SDE2 is not necessarily the difficulty of the coding questions.

The two biggest differences are System Design and Behavioral Interview depth.

1. System Design Is Mandatory

For SDE2, System Design is a core part of the interview. You need to be comfortable discussing scalability, caching, databases, partitioning, reliability, and trade-offs.

More importantly, you cannot stop after drawing a reasonable architecture. The interviewer will likely challenge individual components and ask why you made certain decisions.

2. Behavioral Questions Go Much Deeper

Amazon's Leadership Principles, especially Ownership and Deliver Results, can lead to very deep follow-up questions at the SDE2 level.

Saying "my team decided to do X" is often not enough. The interviewer wants to understand your personal judgment, your decisions, your mistakes, and your position.

3. Coding Is Usually Not the Biggest Trap

The coding questions were mostly around LeetCode Medium difficulty. With enough preparation, there was enough time to solve them.

The bigger challenge was being able to explain the solution clearly, analyze complexity, handle edge cases, and respond to follow-up questions without losing the thread.

How I Prepared for the Amazon SDE2 VO

One of the resources I used during my preparation was
InterviewShow.

In hindsight, I would spend even more time preparing behavioral stories than I originally did. For every major Amazon Leadership Principle story, I would prepare not only the main STAR answer but also the possible third- and fourth-level follow-ups.

If you are preparing for an Amazon SDE2 / L5 Virtual Onsite, don't just memorize your stories. Make sure you can defend every decision in those stories under pressure.

Final Thoughts

The biggest lesson from this interview was that Amazon SDE2 is less about solving hard coding problems and more about demonstrating engineering judgment.

Coding gets you through the basic technical bar. System Design shows whether you can think at a broader engineering level. But the Bar Raiser behavioral round can reveal whether your decisions, ownership, communication, and judgment actually match the expectations for an SDE2.

If I were preparing again, I would spend less time worrying about whether I can solve another random LeetCode Hard and more time practicing how to defend my decisions when an interviewer keeps asking, "Why?"

TikTok Intern OA Experience: Familiar Problems, Two Questions Worth Remembering

interviewshow-cs — Wed, 12 Aug 2026 14:09:51 +0000

TikTok Intern OA rounds are starting to roll out again in batches. After opening the assessment, the overall problem style felt fairly familiar, and I was able to move through it relatively quickly. A typical OA has four questions. Below are the two questions from this round that are worth documenting, especially if you are preparing for the TikTok Intern process.

Question 1: Sort Each k-Border and Write It Back Clockwise

Given an n × n integer matrix, define the 0-border as the outermost layer consisting of the first row, last row, first column, and last column.

After removing the 0-border, the outer layer of the remaining matrix becomes the 1-border. The same definition applies to 2-border, 3-border, and so on, up to floor((n - 1) / 2).

For every k-border, extract all elements in that layer, sort them, and then write them back to their original positions starting from the top-left corner and proceeding in clockwise order.

Approach

The simplest implementation is to process the matrix layer by layer.

For each layer, calculate top, bottom, left, and right. Then collect the elements in this order:

Top row: left → right
Right column: top + 1 → bottom
Bottom row: right - 1 → left
Left column: bottom - 1 → top + 1

This avoids counting the four corners more than once. After collecting the values, simply sort the array and write the sorted values back using the exact same traversal order.

The problem does not require an especially optimized solution, so an O(n³) implementation can still pass. The main thing to watch is indexing, especially when the innermost layer contains only one element.

Question 2: How Many Segments Remain After Removing Houses?

You are given several houses located at distinct integer positions on a number line. The initial positions are stored in houses. The queries array gives the order in which houses are removed.

After removing each house, return the number of remaining house segments. A segment consists of one or more houses occupying consecutive positions. A single isolated house also counts as one segment.

For example:

houses = [1, 2, 3, 6, 7, 9]
queries = [6, 3, 7, 2, 9, 1]

output = [3, 3, 2, 2, 1, 0]

Approach

A clean solution is to maintain the current number of segments while processing the queries from left to right.

First, calculate the initial number of segments from all houses. Then store the currently active house positions in a set or ordered data structure.

When removing a house at position x, only its immediate neighbors x - 1 and x + 1 matter:

Both neighbors exist: one segment is split into two, so the segment count increases by 1.
Only one neighbor exists: the house was at the end of a segment, so the count stays unchanged.
Neither neighbor exists: the house was an isolated segment, so the count decreases by 1.

After processing each query, append the current segment count to the answer array.

The key observation is that removing one position only affects its two adjacent positions, so there is no need to scan the entire array after every deletion. This gives an efficient implementation with roughly O(n + q) expected time when using a hash set.

Overall Takeaway

TikTok Intern OAs often have four questions with a relatively tight time limit, but the difficulty is usually manageable if you are familiar with common implementation patterns. These two problems are good examples: the first is mainly matrix simulation plus sorting, while the second is about maintaining connected segments under deletions.

If you are preparing for a TikTok Intern OA, it is worth reviewing matrix traversal, simulation, hash sets, sorting, and simple dynamic connectivity patterns before starting the assessment. When the question looks familiar, recognizing the underlying pattern quickly can save a lot of time.

Need More TikTok OA & Interview Preparation?

If you are currently preparing for TikTok, Meta, Google, Amazon, or other North American tech interviews, Interview Show provides interview preparation and assistance services covering OA preparation, coding practice, system design, mock interviews, and Virtual Onsite preparation.

You can visit Interview Show to learn more about the available services and get personalized preparation support for your upcoming technical interviews.

Roblox OA Experience: Mini-Games + Coding, Two Coding Problems Fully Solved

interviewshow-cs — Tue, 11 Aug 2026 13:12:28 +0000

The Roblox OA is quite different from a typical Big Tech coding assessment. Instead of focusing entirely on LeetCode-style problems, the assessment combines several timed mini-games and decision-making tasks before moving into the actual Coding Skills section.

The overall format felt somewhat similar to the modular, time-limited structure used by companies like Optiver. Each section has its own fixed time limit, and once you start a section, you cannot switch to another one. Managing your time and staying focused is therefore really important.

Roblox OA Format

My assessment was roughly divided into the following sections:

Robots: 25 minutes
Factories: 25 minutes
Decision-Making: 25 minutes
Outpost: Mars: 40 minutes
Coding Skills: 50 minutes / 2 problems

The first four sections are not traditional programming questions. They are more focused on following instructions, making decisions quickly, and adapting to the rules of each mini-game.

Once you enter a section, the timer is locked to that section, so you cannot pause it and come back later. Keeping track of your time is especially important.

Coding Skills: Two Problems

I got full credit on both coding problems. The two questions were quite different in style, but neither required an extremely complicated algorithm.

Problem 1: Reordering 4×4 Matrices with Missing Numbers

You are given a large matrix with dimensions 4 × (4 × n). It consists of n separate 4×4 matrices placed side by side.

Each 4×4 matrix has one missing number represented by ?.

The task is to:

Find the missing number in each 4×4 matrix.
Fill in the missing value.
Sort the 4×4 matrices by their missing values in ascending order.
If two matrices have the same missing value, preserve their original relative order.
Combine the sorted matrices back into the original large matrix format.

The key observation is that the sum of all numbers in a complete 4×4 matrix is:

1 + 2 + ... + 16 = 136

So for each small matrix, the missing value can be calculated as:

missing value = 136 − sum of the existing values

After filling in the missing values, the rest is essentially matrix slicing + stable sorting.

The main implementation challenge is correctly splitting the large matrix into individual 4×4 blocks and then putting the sorted blocks back together.

Problem 2: Stock Trading Robot and Maximum Profit

The second problem was more algorithmic.

You are given:

prices: the stock price for each day
algo: the robot's original action for each day, where 0 means buy and 1 means sell

The robot performs exactly one action per day. You are allowed to choose one consecutive interval of length k and change all actions in that interval to sell.

The goal is to maximize the total profit.

The easiest way to think about it is to first calculate the profit using the original strategy.

Then, for every day where the original action is buy, changing that action to sell increases the profit by:

2 × price

For days that are already sell actions, changing them has no additional effect.

Therefore, we can construct a gain array representing the additional profit obtained by changing each position to sell.

Now the problem becomes finding the maximum sum of a consecutive subarray of length k.

That can be solved efficiently with a sliding window:

Calculate the original trading profit.
Build the gain array.
Calculate the sum of the first window of length k.
Slide the window across the array while maintaining its sum.
Take the maximum gain.
Return base profit + maximum gain.

The overall complexity is O(n), which is enough for large inputs.

My Takeaways from the Roblox OA

The biggest difference between this assessment and a traditional coding OA is that coding is only one part of the entire evaluation.

For the non-coding sections, I would recommend following the instructions carefully and avoiding spending too much time getting stuck on one task. Since each section has its own fixed timer, losing several minutes in one section can be costly.

For the Coding Skills section, 50 minutes for two problems is tight but manageable. In my case, the first problem was mainly about matrix manipulation and stable sorting, while the second was based on calculating the original result and then using a sliding window to optimize the modified strategy.

If you're preparing for Roblox, it is worth practicing problems involving matrix slicing, stable sorting, simulation, sliding windows, and strategy optimization. These patterns can be more useful here than simply memorizing a large number of advanced algorithms.

Final Thoughts

Overall, I found the Roblox OA quite interesting because it tests more than just coding speed. The combination of mini-games, decision-making, and programming makes the assessment feel noticeably different from a standard Big Tech OA.

If you're preparing for Roblox or other Big Tech OA/VO interviews, you can also check out Interview Show for more interview experiences, OA questions, and preparation resources.

Why Bloomberg Interviews Make Strong LeetCode Candidates Fail

interviewshow-cs — Sun, 09 Aug 2026 06:06:16 +0000

I recently finished the full Bloomberg 26NG SDE interview process and wanted to share my experience while everything is still fresh.

Many candidates think Bloomberg is a "finance giant with good benefits and a relatively easier interview process" compared with companies like Google or Meta. But after going through the entire process, I realized the difficulty comes from somewhere many candidates don't prepare for:
your problem-solving process and communication matter as much as the final code.

Some candidates solve every problem correctly but still get rejected. Others may not reach the optimal solution but receive offers. Bloomberg is not only evaluating whether you can solve problems — they want to understand how you think, how clearly you communicate, and whether you can build reliable systems.

This mindset appears throughout the entire interview loop.

How Many Rounds Does Bloomberg SDE Interview Have?

Many people assume Bloomberg only has a phone screen and onsite interviews. In reality, the full process usually looks like:

Phone Screen → VO1 → VO2 → HR → EM Interview

The whole process usually takes around 2.5 months from application to final decision. The timeline is relatively efficient, and most rounds provide feedback within about a week.

One important detail: Bloomberg interviews usually require camera on and screen sharing throughout the process. If you are not used to this format, practice beforehand because it can affect your interview rhythm.

Phone Screen: Easy Coding, Difficult Behavioral Questions

The phone screen is around 45 minutes. The first 10 minutes focus on resume discussion, followed by coding questions.

The coding problems are usually Easy to Medium level, but many candidates underestimate the behavioral portion.

"Why Bloomberg?" Is Not a Question You Can Ignore

A common mistake is memorizing Bloomberg company information and repeating it during the interview. Interviewers are not looking for a company introduction.

They want to know whether you understand:

What Bloomberg Terminal provides
Why financial data infrastructure requires high reliability
Why low latency matters in financial systems
Why Bloomberg's engineering challenges match your background

Connecting Bloomberg's products with your own technical experience creates a much stronger answer.

Coding Questions

The questions I received were:

Validate Binary Search Tree
Longest Palindromic Substring

For BST validation, I used inorder traversal and maintained the previous node value.
For longest palindrome, I used center expansion.

The code itself was not difficult. However, the interviewer followed up with deeper questions:

How would you handle integer boundary cases in BST validation?
Is there an O(n) solution for longest palindrome?

These follow-ups are where Bloomberg evaluates your depth. Mentioning techniques like using infinity boundaries or Manacher's algorithm is enough — you usually don't need to implement them.

VO1 and VO2: Follow-ups Matter More Than the First Solution

Each virtual onsite round lasts around 60 minutes. Usually, you spend a few minutes discussing your resume, then move directly into coding.

The pace is fast. Two Medium problems plus follow-ups require you to finish each solution quickly while explaining your reasoning.

VO1 Experience

The first problem was a string matching problem. Given two equal-length strings, secret and guess, return:

* for exact matches
+ for characters that exist but appear in different positions
- for missing characters

The key detail is that each character can only be matched once. The correct approach is using Counter instead of Set:

First pass handles exact matches
Second pass handles misplaced matches
Decrease character counts after every match

The follow-up focused on duplicate characters, which is exactly why frequency counting matters.

The second problem was counting valid triangle triplets. The standard approach is sorting and using two pointers, achieving O(n²).

VO2 Experience

The first problem was flattening a multilevel linked list with next and child pointers.

I used a stack-based DFS approach:

Push next nodes into the stack
Connect child nodes directly
Continue traversal
Restore next pointers from the stack

The interviewer asked whether O(1) space was possible. The discussion was more important than the final answer — they wanted to see whether I understood the trade-offs.

The second question was an object-oriented design problem:
Design a subway system that supports check-in, check-out, and average travel time queries.

The design used:

HashMap for active passengers
HashMap for route statistics

Follow-ups focused on concurrency and distributed scaling. This is where Bloomberg shows its engineering-focused interview style.

HR Round: Not Just a Formality

Many candidates relax after technical rounds, assuming HR is guaranteed. Bloomberg HR still evaluates motivation and alignment.

You may be asked:

Why Bloomberg?
What Bloomberg products do you know?
How is Bloomberg different from other fintech companies?
What are your long-term career goals?

Weak answers can still hurt your chances.

EM Interview: The Most Challenging Round

The Engineering Manager round usually lasts 45-60 minutes. Depending on the team, it focuses on resume deep dive, system design, or both.

Resume Deep Dive

EM interviews go much deeper than simply asking what you built.

Expect questions like:

Why did you choose this architecture?
What alternatives did you consider?
What would you improve if rebuilding the project?
If traffic increased 10x, where would the bottleneck be?

System Design: Financial Systems Matter

One design question was:
Design a real-time stock price subscription system.

Users subscribe to stocks, and the system pushes price updates whenever prices change.

The architecture:

Exchange Data → Kafka → Price Processor → WebSocket Gateway → Users

Subscription data is stored in Redis for fast access, while persistent storage remains in a database.

Follow-ups included:

100K users subscribing to the same stock: Use batching and broadcast updates.
Service restart: Rebuild Redis cache from persistent database storage.
Latency requirements: Financial systems require low latency, and WebSocket is more suitable than HTTP polling.

The Real Reason Bloomberg Interviews Are Difficult

After completing the entire process, my biggest takeaway is:

Bloomberg is not looking for someone who can only solve algorithm problems. They want engineers who can build reliable systems in a high-pressure environment.

In financial systems, missing an edge case or making a poor design decision can create real impact.

Bloomberg evaluates:

Attention to edge cases
Understanding of trade-offs
Ability to communicate technical decisions
Engineering maturity

Tips for Bloomberg Interview Preparation

Coding

Focus on:

Trees
Two pointers
HashMap
Linked lists
Object-oriented design

Practice solving Medium problems within 15 minutes while explaining your thoughts clearly.

Behavioral Preparation

Prepare a strong "Why Bloomberg" answer. Understand Bloomberg Terminal, financial infrastructure, and connect them with your own experience.

System Design

The goal is not designing the most complicated system. The goal is explaining your decisions, handling follow-up questions, and showing structured thinking.

For more interview experiences, OA reviews, and SDE preparation resources, visit
InterviewShow.

Databricks 26NG Full Interview Experience Review (Phone Screen to Onsite)

interviewshow-cs — Fri, 07 Aug 2026 12:58:07 +0000

Just received the final feedback from Databricks. Overall, the process was fast-paced and well organized. The interviewers were friendly, discussions went deep, and the recruiter response time was among the fastest I have experienced at large tech companies. The biggest takeaway: Databricks places significantly more emphasis on distributed systems and concurrency compared with many traditional software companies.

Interview Process Overview

The full process was:

Technical Phone Screen
HR Scheduling for Onsite
One-day Virtual Onsite: 2 Coding Rounds + Behavioral + System Programming

The entire timeline was around three weeks. After each round, feedback usually came the same day or the following day.

Technical Phone Screen

Weighted Graph Shortest Path (BFS / Dijkstra)

The question focused on finding the optimal path in a weighted graph. After implementing the standard priority queue-based Dijkstra solution, the interviewer followed up:

"What if there are multiple transportation methods, such as walking, buses, and driving, where each option has different cost and time?"

I explained that the edge weight could be modeled as a multi-dimensional vector instead of a single value. The problem then becomes a multi-objective optimization problem where we search for Pareto optimal solutions. The interviewer accepted the approach and the round ended successfully.

Onsite Interview Rounds

Coding Round 1: Circular House Robber

This was a classic House Robber problem with an additional circular constraint. The solution was to split it into two linear cases:

Do not rob the first house
Do not rob the last house

Then return the maximum result from both scenarios. The discussion covered edge cases and complexity analysis. The follow-up question asked how the state transition would change if houses had additional dependencies.

Coding Round 2: Interval Removal + Streaming Scenario

The first part involved removing intervals and handling different overlapping cases. Then the interviewer extended the problem into a streaming scenario where deletion intervals continuously arrive.

After clarifying the requirements, I proposed maintaining the current interval collection and discussed different implementation trade-offs:

Simple list-based approach for smaller datasets
Segment tree or advanced interval structures for better scalability

This round lasted around 35 minutes and focused heavily on engineering judgment.

Behavioral Interview

The behavioral round focused on:

Deep dive into resume projects
Handling conflicts
Most impactful project experience

For conflict resolution, I shared an example where we used A/B testing data to make decisions instead of relying on opinions. For the most impactful project discussion, I mentioned that if rebuilding it, I would introduce monitoring and alerting earlier.

The interviewer appreciated the focus on engineering maturity and operational awareness.

System Programming Round (Databricks-Specific)

This was the most distinctive round and closely matched Databricks' engineering culture.

The problem involved designing a CacheFile class:

Retrieve files remotely
Return data based on offset and length
Support multiple clients

The discussion focused on:

Multiple clients requesting the same file simultaneously
Cache eviction strategy
Prefetching under network latency

The solutions discussed included:

Per-file locking for concurrency control
LRU cache eviction
Background prefetching
Priority queues for scheduling

The interviewer agreed with the design direction. The round ended before completing full implementation, but the overall approach was considered solid.

Preparation Advice

Coding questions are mostly around Medium difficulty. Focus on writing clean code and handling follow-up questions.
Prepare 3-4 strong behavioral stories with measurable impact, decision-making process, and lessons learned.
System Programming is where candidates can differentiate themselves. Practice thread-safe data structures, caching systems, and distributed system fundamentals.
Reading implementations from projects like Spark and Delta Lake can help understand Databricks' engineering mindset.

Final Thoughts

Overall, Databricks has a very engineering-focused interview process. The System Programming round especially reflects their focus on distributed systems, storage, concurrency, and large-scale data infrastructure.

If your background is not heavily focused on concurrency or distributed systems, spending two extra weeks specifically preparing these areas can make a significant difference.

Good luck with your Databricks interview preparation!

If you are preparing for Databricks or other infrastructure-focused software engineering roles, you can find more interview preparation resources, system programming materials, and real interview experiences at:

InterviewShow