DEV Community: interview-show-Etesis Elay

Amazon SDE Intern VO Experience: 4 Rounds, High-Frequency Questions, and What Actually Matters

interview-show-Etesis Elay — Wed, 01 Jul 2026 14:16:26 +0000

I recently helped several students prepare for their Amazon SDE Intern Virtual Onsite interviews, and they all received offers. After going through Amazon's interview process multiple times, one thing has become very clear: the structure is highly consistent. Most candidates can expect a combination of Behavioral (Leadership Principles), resume deep dives, Coding, and Object-Oriented Design. The question patterns change slightly, but the core topics remain very similar.

If you're currently preparing for Amazon, hopefully this interview recap gives you a clearer picture of what to expect.

Round 1 — Coding: All Nodes Distance K in Binary Tree

The interviewer asked for all nodes that are exactly K edges away from a given target node in a binary tree.

The main challenge is that binary trees only provide child pointers, while valid nodes may also exist by moving upward toward parent nodes. My solution first performed a DFS traversal to build parent references, effectively converting the tree into an undirected graph.

After that, I started a BFS from the target node. At every step, I explored three possible directions:

Left child
Right child
Parent

A visited set prevented revisiting previously explored nodes. After exactly K BFS levels, every node remaining in the queue belonged to the answer.

Complexity

Time: O(n)
Space: O(n)

The interviewer also asked what would happen if K exceeded the tree height. No special handling was necessary—the BFS simply finishes without reaching that level and returns an empty list.

Round 2 — Object-Oriented Design: Task Scheduling System

The design question involved building a task scheduler supporting different task types, priorities, dependency management, and retry mechanisms.

My design separated responsibilities into several classes.

Task stores task_id, priority, dependencies, status, retry_count, and execution metadata.
TaskScheduler manages task registration, dependency tracking, ready queues, and execution.

A priority queue determines execution order, while dependency management follows a topological scheduling approach. Tasks are only placed into the ready queue after all prerequisite tasks have completed successfully.

For retry logic, execution failures are caught through exception handling. If the retry limit has not been reached, the task is reinserted into the priority queue. Otherwise, it is marked as FAILED.

The interviewer focused on several follow-up discussions:

Detecting cyclic dependencies using indegree-based topological sorting
Supporting concurrent execution with thread pools
Making priority queues thread-safe
Scaling the scheduler for distributed execution

Round 3 — Coding: Longest Consecutive Sequence

The problem asked for the length of the longest consecutive integer sequence in an unsorted array while maintaining O(n) time complexity.

Sorting would require O(n log n), so instead I inserted every number into a hash set for constant-time lookups.

During traversal, only numbers whose predecessor (num − 1) was absent were considered sequence starting points. From each starting point, I continuously expanded forward until the consecutive sequence ended, updating the global maximum.

Although the algorithm appears to contain nested loops, every number is visited only a constant number of times, producing an overall O(n) solution.

Round 4 — Behavioral (Leadership Principles)

Behavioral questions remained heavily centered around Amazon's Leadership Principles.

Give an example of simplifying a complex problem or creating a novel solution. How was it better than the existing approach? (Invent & Simplify)
Describe a time you had to quickly learn a new skill or domain. How did you approach it? (Learn & Be Curious)
Recall a time you disagreed with a decision but still committed to it. How did you handle it? (Disagree & Commit)

I prepared every story using the STAR framework. The biggest recommendation is to include measurable outcomes whenever possible—performance improvements, time saved, customer impact, or percentages. Concrete numbers make answers significantly stronger during Amazon behavioral interviews.

Preparation Tips

This interview combination has appeared frequently in recent Amazon SDE Intern Virtual Onsites.

Two coding rounds focused on classic data structures and algorithms.
The OOD interview emphasized clean class design and scalability.
Behavioral questions repeatedly targeted core Leadership Principles.

Practicing likely follow-up questions is just as important as solving the original problem itself. Many candidates solve the coding question correctly but struggle when interviewers begin exploring trade-offs, optimizations, or edge cases.

If you're looking for structured preparation, including OA guidance, mock interviews, interview preparation, and virtual interview support, you can learn more at Interview Aid.

Final Thoughts

Even though Amazon's interview process is relatively predictable, every interviewer introduces unique follow-up questions that test communication, design decisions, and problem-solving depth.

During my own preparation, I worked with Interview Aid. Their experienced North American CS mentors helped refine my coding strategies, optimize solutions, and strengthen my behavioral storytelling. Those preparation sessions gave me much more confidence throughout the interview process, and ultimately helped me secure my offer.

If you're preparing for Amazon SDE Intern, Google, Microsoft, or other top tech interviews, feel free to share your experiences and exchange preparation tips with others in the community.

Tesla OA Questions | HackerRank Online Assessment Experience & Solutions

interview-show-Etesis Elay — Tue, 30 Jun 2026 11:29:11 +0000

I recently completed Tesla's Online Assessment on the HackerRank platform. The assessment consisted of three Tesla OA Questions, and I ended up writing around 200 lines of code in total. Overall, the difficulty was moderate. I finished all three problems in roughly 30 minutes and passed every test case successfully. Since I've completed quite a few online assessments from major tech companies this year, the overall workflow felt familiar, leaving enough time to review edge cases before submitting.

Tesla OA Questions Breakdown

Question 1: SQL Score Statistics

The first problem involved two database tables:

test_groups — stores group names and the score assigned to each test case.
test_cases — stores test case IDs, their corresponding group, and execution status.

The task required using test_groups as the primary table and performing a LEFT JOIN with test_cases. For every group, we needed to calculate:

Total number of test cases
Number of passed test cases
Total score earned

Finally, the output had to be sorted by total score in descending order and group name in ascending order.

The implementation was fairly straightforward. I used a LEFT JOIN to ensure every group appeared even if no test cases existed. The total number of test cases came from counting the primary key, while passed cases were counted using a CASE WHEN expression checking for the status 'OK'. The final score was simply the number of passed test cases multiplied by the score value stored in test_groups.

Question 2: Text Transcription (Maximum Insertions of 'a')

Given a lowercase string S, the goal was to insert as many lowercase 'a' characters as possible while ensuring that the resulting string never contained three consecutive 'a' characters.

If the original string already contained "aaa", the answer should immediately be -1.

Otherwise, every non-'a' character naturally separates the string into multiple independent gaps, including the beginning and the end of the string. Since each gap can contain at most two consecutive 'a' characters, the maximum number of insertions can be calculated based on the number of available gaps. After accounting for the existing 'a' characters already present, the remaining capacity gives the final answer.

The solution only requires a single linear scan, making it both simple and efficient.

Question 3: Zero Sum Fragments

Given an integer array A, count how many contiguous subarrays have a sum equal to zero. If the answer exceeds one billion, return -1.

This is a classic prefix sum and hash map problem.

I initialized a hash map with:

{0: 1}

This represents that a prefix sum of zero has appeared once before processing any elements.

While traversing the array, I continuously updated the running prefix sum. Whenever the same prefix sum appeared again, every previous occurrence represented another zero-sum subarray ending at the current position. Therefore, I simply added the existing frequency stored in the hash map to the answer. Afterward, I updated the frequency of the current prefix sum. If the accumulated count exceeded one billion, the function immediately returned -1.

This algorithm runs in O(N) time and is the standard optimal solution.

Overall Difficulty

Overall, Tesla's Online Assessment was moderate in difficulty.

The SQL question mainly tested joins, aggregation, and conditional counting.
The string problem focused on greedy thinking and careful edge-case handling.
The array problem tested familiarity with prefix sums and hash tables.

If you've practiced similar HackerRank problems before, the patterns become fairly recognizable, allowing you to finish comfortably within the time limit.

Preparation Tips for Tesla OA

Become comfortable with LEFT JOIN, GROUP BY, COUNT, and CASE WHEN in SQL.
Practice greedy string manipulation problems involving insertion and boundary conditions.
Master the Prefix Sum + Hash Map technique for subarray sum problems.
Spend some time getting familiar with HackerRank's coding environment and input/output format before the assessment.

Final Thoughts

Although Tesla's Online Assessment provides a reasonable amount of time, preparation still makes a significant difference. Many of these problems follow recognizable patterns, so practicing common HackerRank question types beforehand can dramatically improve both speed and confidence during the test.

If you're preparing for Tesla or other major technology companies and would like additional interview or OA support, you can learn more at Interview Aid, which provides resources and assistance for coding assessments and technical interview preparation.

Good luck with your Tesla OA, and feel free to share your own experience with other candidates preparing for upcoming assessments!

Virtu OA | Virtu Trading HackerRank OA Experience 2026

interview-show-Etesis Elay — Fri, 26 Jun 2026 09:30:04 +0000

I recently completed the Virtu Trading HackerRank Online Assessment, and overall it was more straightforward than I expected. The assessment consisted of five coding questions with a total time limit of 75 minutes. I finished all five problems in about 40–45 minutes, passed every test case on my first submission, and spent the remaining time reviewing edge cases and cleaning up my code.

Although the overall difficulty wasn't particularly high, Virtu clearly values coding speed, implementation quality, and attention to detail. Most questions focused on fundamental algorithms rather than advanced data structures, making it important to write clean and bug-free code under time pressure.

OA Overview

Platform: HackerRank
Duration: 75 Minutes
Questions: 5 Coding Problems
Difficulty: Easy to Medium
Main Topics: Strings, Greedy, Simulation, Arrays, Basic Mathematics

Unlike some quantitative trading companies that heavily emphasize advanced algorithms, Virtu's OA leaned more toward practical implementation. There were no graph algorithms, dynamic programming, or complicated data structures. Instead, the assessment rewarded candidates who could quickly understand the problem, implement a correct solution, and carefully handle boundary conditions.

Question 1 — Count Uniform Character Substrings

The first problem asked for the total number of contiguous substrings consisting entirely of identical characters.

The key observation is that if a consecutive block has length n, then it contributes n × (n + 1) / 2 valid substrings. The solution simply scans the string once, tracks the current run length, and adds the contribution whenever the character changes.

This is a classic linear scan problem with an overall time complexity of O(n) and constant extra space.

Question 2 — HexSpeak

The second problem involved converting a decimal number into a hexadecimal representation and then replacing specific digits according to the HexSpeak rules.

After converting the number into uppercase hexadecimal format:

Replace 0 with O
Replace 1 with I

The resulting string must contain only the characters A, B, C, D, E, I, and O. Otherwise, the answer should be ERROR.

This was mainly a straightforward simulation problem involving string manipulation and character validation.

Question 3 — Minimum Steps to a Fibonacci Number

The third question asked for the minimum number of increment or decrement operations required to transform an integer into the nearest Fibonacci number.

Since the maximum input value is only one million, generating every Fibonacci number up to that limit is trivial. After preprocessing the sequence, simply compute the absolute difference between the input value and every Fibonacci number, then return the minimum difference.

The algorithm is extremely efficient because there are only a few dozen Fibonacci numbers within the given range.

Question 4 — Apple Packing

This was a standard greedy problem.

The first element of the input represented the weight already inside the box, while the remaining elements represented the weights of individual apples. The objective was to maximize the number of apples packed without exceeding the box's capacity.

The optimal strategy is simple:

Calculate the remaining capacity.
Sort all apple weights in ascending order.
Add apples from lightest to heaviest until no additional apple fits.

This classic greedy approach guarantees the maximum number of apples.

Question 5 — Student Score Stabilization

The final question was a simulation problem.

Students stand in a line, and every day each student's score changes according to the scores of their immediate neighbors:

If both neighbors have higher scores, increase by one.
If both neighbors have lower scores, decrease by one.
The first and last students never change.

The process repeats until the entire array becomes stable.

The implementation is straightforward, but one important detail is that each day's updates must be calculated using a new array rather than modifying the original array in place. Otherwise, earlier updates would incorrectly affect later calculations within the same iteration.

Overall Difficulty

Overall, this OA focused much more on coding fundamentals than advanced algorithms.

The five questions mainly covered:

String manipulation
Greedy algorithms
Simulation
Basic mathematics
Array processing

The difficulty level was generally Easy to Medium, making coding speed and careful implementation more important than knowing sophisticated algorithms.

If you're preparing for Virtu, practicing common HackerRank-style implementation problems will likely provide a significant advantage.

Preparation Tips

If you're getting ready for Virtu Trading or other quantitative trading firms such as Jane Street, Citadel, Optiver, IMC, or Hudson River Trading, I would recommend focusing on high-frequency HackerRank topics like strings, greedy algorithms, simulations, arrays, and mathematical implementation problems.

If you're looking for additional interview preparation, Interview Aid has been helping candidates prepare for North American software engineering and quantitative interviews since 2014. Their team includes experienced engineers and interview coaches from leading technology companies, offering personalized support for online assessments, technical interviews, system design, behavioral interviews, and complete interview strategy planning.

ByteDance OA Experience | Passing the CodeSignal 70-Minute 4-Question Assessment

interview-show-Etesis Elay — Thu, 25 Jun 2026 08:17:38 +0000

Hello everyone! I recently completed a ByteDance Online Assessment hosted on CodeSignal, which follows a very similar format to TikTok's coding assessments. The test consisted of 4 programming questions with a total time limit of 70 minutes.

Fortunately, the questions I received were all from commonly seen CodeSignal-style patterns. From reading the problems to submitting all solutions, I spent less than twenty minutes and used the remaining time to review edge cases and optimize my code. In the end, I successfully passed the assessment.

ByteDance OAs primarily focus on practical programming skills. While the time limit can feel tight, candidates who have practiced similar CodeSignal questions beforehand will likely find the test manageable. Below is a detailed breakdown of the four questions and the approaches I used.

Question 1: User Rating Tier Calculation

The first problem involved determining a user's final ranking tier based on an initial rating and a list of rating changes.

After applying all rating adjustments, the final score must be mapped to one of four tiers:

beginner
intermediate
advanced
pro

The solution was straightforward. I summed the initial rating and all values in the changes array, then used simple conditional checks to determine the corresponding tier.

The main consideration was handling boundary values correctly, especially ratings that landed exactly on threshold values such as 1000, 1500, and 2000.

Question 2: Reverse the Middle of Vowel-Bounded Words

This problem focused on string manipulation.

Given an array of words, if both the first and last character of a word are vowels (case-insensitive), the middle portion of the word should be reversed while keeping the first and last characters unchanged.

Examples:

apple → alppe
OranGe → OGnare
banana → unchanged

I created a vowel lookup set containing both uppercase and lowercase vowels. For each word, I checked whether its first and last characters belonged to the set.

If the condition was satisfied and the length exceeded two characters, I reversed the middle substring and reconstructed the word. Otherwise, I kept the original word unchanged.

The problem was relatively simple but required careful handling of short strings with lengths of one or two characters.

Question 3: Circular Battery Usage Simulation

This was the most simulation-heavy problem of the assessment.

A device needs to remain powered for t minutes. Several batteries are available, and each battery has:

A capacity (how many minutes it can power the device)
A recharge time

After a battery is used, it enters a charging state and becomes unavailable until fully recharged. The batteries are checked in a circular order. If a battery is still charging, it is skipped.

The objective is to determine the minimum number of fully charged batteries required to keep the device running for the entire duration. If a situation occurs where all batteries are simultaneously unavailable, the answer should be -1.

I solved this using direct simulation.

An array stored the next available timestamp for each battery. At every step, I searched for a battery that could be used at the current time. Once selected, I updated the current time, increased the usage count, and calculated when the battery would become available again.

If no battery could be used at a given moment, the simulation immediately returned -1.

The implementation was not particularly difficult, but careful attention was needed when managing battery rotation and availability updates.

Question 4: Reconstructing a Travel Route from Adjacent Photos

The final problem involved rebuilding a travel itinerary.

A traveler visited multiple landmarks, but only a collection of photos remained. Each photo recorded two consecutively visited landmarks, although the travel direction was unknown.

The problem guaranteed that all landmarks formed a single path without branches.

The solution was essentially graph reconstruction.

I built an undirected adjacency list and calculated the degree of each node. Since the graph represented a path, one of the endpoints would have degree 1.

Starting from an endpoint, I repeatedly moved to the neighboring node that was not previously visited. By continuing this process until all nodes were traversed, I reconstructed the complete travel route.

Because the graph was guaranteed to be a simple chain, no complex DFS or BFS was required.

Overall Difficulty and Preparation Tips

Overall, the ByteDance OA felt easier than many candidates expect. The problems primarily tested:

Basic programming fundamentals
String manipulation
Simulation techniques
Simple graph construction and traversal

If you regularly practice CodeSignal-style questions, most of these patterns will become familiar very quickly.

My recommendations for future candidates are:

Practice string-processing problems extensively.
Become comfortable with simulation-based questions.
Review graph fundamentals, especially path reconstruction problems.
Pay close attention to edge cases and boundary conditions.
Build familiarity with common CodeSignal question patterns.

In my experience, prior exposure to similar problems dramatically improves both speed and accuracy during the assessment.

Additional Preparation Resources

One thing I noticed while preparing is that simulation and optimization problems can become time-consuming if encountered for the first time during an actual assessment.

For candidates looking to maximize their chances of success, structured preparation and targeted guidance can make a significant difference.

You can explore additional interview preparation resources, OA guidance, and technical interview support at Interview Aid.

Whether you're preparing for ByteDance, TikTok, or other CodeSignal-based assessments, feel free to share your experiences and discuss strategies with fellow candidates.

Goldman Sachs HackerRank OA Review | A More Challenging Round Than Expected

interview-show-Etesis Elay — Wed, 24 Jun 2026 05:47:15 +0000

Just completed a Goldman Sachs HackerRank OA. This round followed an A + B format (coding + math), and the overall difficulty and pacing felt noticeably higher than a standard OA.

Section A was a 120-minute data structure & algorithm coding round, while Section B was a 180-minute mixed math + programming assessment. The platform was still HackerRank, so the interface was familiar, but the question design leaned heavily toward thinking + optimization rather than straightforward pattern matching.

Question 1: First Compatible

The problem statement itself is simple, but the challenge lies in optimization.

Given arr1 and arr2, for each element in arr1, find the smallest index in arr2 such that the value is strictly greater than the element. If none exists, return -1.

Example:

arr1 = [3,8,1]
arr2 = [1,2,5]
Output: [3, -1, 2]

Core idea: treat arr1 as offline queries and process them efficiently using sorting or scanning techniques on arr2. The goal is to maintain a structure that allows fast lookup of the minimal valid index.

A more robust approach is to use a Fenwick Tree (BIT) or Segment Tree to maintain the minimum index dynamically, avoiding an O(n²) brute-force solution that would time out.

In essence, this is a classic combination of: sorting + offline queries + data structure optimization

Question 2: Lock Code

This question is a blend of math, greedy strategy, and pruning optimization.

You are given a codeSequence and a maxValue. You can modify elements (each modification costs 1), with the goal of selecting a value x such that all elements become coprime with x (GCD = 1), while maximizing:

x - modification cost

Key Insight

The main optimization lies in two observations:

First, x does not need to be fully enumerated across the entire range. Since modification cost is bounded by n, we only need to consider values in the range:

[maxValue - n, maxValue]

For each candidate x, compute how many elements are NOT coprime with x. Those elements must be modified, which gives the cost.

Finally, choose the maximum value of (x - cost).

Overall Difficulty Impression

This OA felt different from typical LeetCode-style assessments.

Q1 focuses heavily on offline processing + data structure optimization, where naive solutions quickly run into TLE. Q2 focuses on mathematical reasoning + pruning strategy rather than template-based solving.

If you're only comfortable with standard Easy/Medium LeetCode problems, this type of OA can still be tricky, especially when optimization is required under time pressure.

Preparation Takeaways

Offline query techniques (sorting + scanning)
Fenwick Tree / Segment Tree basics
GCD and coprime optimization tricks
Pruning and bounded enumeration strategies

The biggest gap in these OA problems is not understanding the idea, but turning a brute-force approach into a scalable solution within limited time.

Final Thoughts

In my case, preparation time was limited, and I referenced structured OA strategy notes and pacing reminders from Interview Aid during practice. They are a long-running team (founded in 2014) composed of engineers and competitive programmers focused on helping candidates with OA and VO preparation.

For candidates who are just starting OA preparation or often get stuck on optimization-heavy problems, it can act as a useful acceleration layer for building intuition and improving execution speed under pressure.

Learn more here: Interview Aid Official Site

Anthropic Software Engineer Interview Experience (2026)

interview-show-Etesis Elay — Tue, 23 Jun 2026 02:29:45 +0000

I recently completed the interview process for a Software Engineer position at Anthropic, and my biggest takeaway is this: they are deeply focused on LLM Infrastructure and AI Safety throughout the entire process. Unlike traditional Big Tech interviews that heavily emphasize LeetCode-style algorithms and standard system design questions, Anthropic evaluates your ability to build, optimize, and safely deploy large-scale AI systems.

The entire virtual onsite (VO) was intense and highly practical. Topics such as distributed systems, inference optimization, reliability engineering, and responsible AI deployment appeared repeatedly. Pure algorithm grinding is unlikely to be enough.

I have already received positive feedback from recruiting and successfully advanced to the Team Match stage, where I am currently being considered for teams focused on LLM Infrastructure, Inference Optimization, and AI Safety Engineering. Below is a complete breakdown of my interview experience.

Interview Timeline

1. Initial Screening (30 Minutes)

This round was entirely non-technical. The recruiter focused on understanding my background, previous engineering experience, long-term career goals, and work authorization status. Most of the conversation revolved around distributed systems, backend infrastructure projects, and why I wanted to work specifically in AI.

2. Technical Phone Screen (45 Minutes)

The coding portion was surprisingly practical. Instead of obscure algorithm puzzles, the interviewer asked me to implement an efficient token batching mechanism for handling LLM inference requests. The discussion included queue management, request scheduling, concurrency handling, and complexity analysis.

After coding, we briefly discussed:

Major performance bottlenecks during LLM inference
KV Cache fundamentals
Basic inference optimization strategies
Trade-offs between latency and throughput

The expectation was not deep research-level knowledge, but rather a solid understanding of how modern LLM systems operate in production.

Virtual Onsite (Four Consecutive Rounds)

Round 1: Coding & Optimization (60 Minutes)

The primary coding question was:

Design a globally unique, monotonically increasing 64-bit distributed ID generation service.

The system needed to support a simple nextId() API while maintaining uniqueness and scalability under heavy concurrency.

I proposed the classic Snowflake architecture:

1-bit sign bit
41-bit timestamp
10-bit machine identifier
12-bit sequence number

The implementation discussion then expanded into several difficult follow-up questions:

What happens when 4,096 IDs per millisecond is not enough?
How should machine IDs be automatically assigned without conflicts?
How would you handle clock rollback in production environments?
What if the business requires absolutely gap-free IDs?

The interviewer further explored alternative approaches such as:

Segment-based ID allocation
Database double-buffer optimization
Redis INCR-based centralized generators
Consistency and availability trade-offs

Round 2: System Design (60 Minutes)

The system design question was:

Design a Prompt Playground platform similar to ChatGPT Playground.

The platform would allow developers, researchers, and product teams to:

Create and manage prompts
Version control prompt iterations
Compare model outputs
Measure latency, quality, and cost
Collaborate across teams
Perform security and safety checks
Manage permissions and audit logs

One interesting aspect was that the interviewer explicitly asked me not to jump directly into architecture diagrams. Instead, they wanted the discussion to begin with product requirements and user workflows before moving into:

API design
Data modeling
Inference pipelines
Scalability considerations
Cost optimization
AI safety mechanisms
Access control systems

This felt much closer to a real engineering design review than a traditional system design interview.

Round 3: Project Deep Dive & Behavioral Interview (60 Minutes)

This round focused heavily on past engineering experience. Every project discussion eventually turned into a deep investigation of:

Production incidents
Performance bottlenecks
Capacity planning
Architecture trade-offs
Root-cause analysis processes

The interviewer repeatedly asked why specific technical decisions were made and what alternatives had been considered.

Behavioral questions were unusually focused on engineering in the age of AI:

Tell me about a time AI helped solve a difficult engineering problem.
How do you identify bugs generated by AI coding tools?
What characteristics define an effective AI-assisted engineer?

One important observation: Anthropic clearly values engineers who know how to collaborate effectively with AI systems. Claiming that you never use AI tools may actually work against you. They want engineers who understand both the strengths and limitations of AI-assisted development.

Round 4: Culture & Technical Values (45 Minutes)

This round focused heavily on:

AI Safety
Responsible Deployment
Alignment
Long-term risk management

Unlike many companies that rely on predictable STAR-format behavioral questions, Anthropic's interview style felt much more conversational and improvisational. Prepared scripts are easy for interviewers to detect.

A recurring theme was:

Why Anthropic?

Not once. Not twice. Multiple times.

The interviewer kept revisiting the question from different angles:

Why Anthropic specifically?
Why not OpenAI?
Why not other AI labs?
Why does AI Safety matter to you personally?

The goal was clearly to evaluate authenticity rather than rehearsed answers.

Key Lessons for Future Candidates

1. Focus on LLM Infrastructure

Distributed systems remain important, but the context is increasingly centered around AI workloads. Study topics such as:

Inference serving
KV Cache optimization
Batching strategies
GPU utilization
Request scheduling
Latency vs throughput trade-offs

2. Learn to Explain Trade-offs Clearly

Interviewers care less about buzzwords and more about engineering reasoning. Be prepared to explain:

Why a particular design was chosen
What alternatives were considered
What risks were accepted
How decisions affected scalability and reliability

3. Take AI Safety Seriously

At Anthropic, safety is not a separate discussion. It is integrated into system design, deployment processes, monitoring, and operational decision-making. Candidates should demonstrate an understanding of how safety considerations influence engineering choices.

How I Prepared

The preparation process lasted several months and covered distributed systems, LLM infrastructure, behavioral interviews, and AI safety concepts. One resource that proved particularly helpful was InterviewAid.

Their mentors come from top technology companies and research backgrounds, including engineers from Amazon, Google, and other major organizations. Throughout my preparation, they provided support across multiple stages, including resume review, mock interviews, technical guidance, and VO preparation.

The most valuable aspect was receiving detailed feedback on LLM infrastructure concepts and learning how to handle Anthropic's highly conversational, freestyle-style behavioral interviews. For candidates targeting AI labs, understanding how to communicate technical decisions clearly is often just as important as solving the technical problems themselves.

Final Thoughts

Anthropic's interview process feels fundamentally different from most traditional software engineering interviews. The company is looking for engineers who can build reliable AI systems, reason carefully about safety, and collaborate effectively with increasingly capable AI tools.

If you're preparing for Anthropic, OpenAI, or other frontier AI labs, focus less on memorizing interview scripts and more on developing a deep understanding of distributed systems, inference infrastructure, and responsible AI deployment.

Good luck to everyone pursuing opportunities in the AI industry. Hopefully this breakdown helps you navigate the process and land your dream offer.

Snowflake SDE Interview Experience | VO Deep Dive + System Design Follow-ups

interview-show-Etesis Elay — Mon, 22 Jun 2026 02:11:39 +0000

Snowflake interviews are seriously underestimated. As one of the leaders in the cloud data warehouse space, the process combines algorithmic problem-solving, system design, and deep technical discussions. I recently completed a round of interviews, and my biggest takeaway was this:

LeetCode gets you through the door, but follow-up discussions determine whether you move forward.

Interview Process Overview

Recruiter Screen
Coding Screen (OA)
Two Virtual Onsite Technical Interviews
Behavioral + Bar Raiser
Hiring Manager Interview

The OA consisted of three coding questions:

Prime String
Vowel Substring
Maximum Order Volume

All three were manageable and I finished with time remaining.

Virtual Onsite Technical Interviews

Coding Round 1: Design a Key-Value Store

The initial requirements were straightforward:

get(key)
set(key, value)
remove(key)

The interviewer then introduced transactional support:

begin()
commit()
rollback()

All operations were expected to run in O(1) time complexity.

My solution used a HashMap for storage and a Stack to maintain operation history, allowing efficient rollback and snapshot management.

The follow-up discussion quickly moved beyond implementation details. We explored concurrency control, memory optimization, transaction isolation, and consistency guarantees. At one point, drawing a state transition diagram became extremely helpful for explaining edge cases and recovery flows.

Coding Round 2: Tree Height Optimization

The base problem required calculating the height of a tree using DFS or BFS.

The follow-up significantly increased the difficulty:

Given a target height H, determine the minimum number of nodes that must be removed so that the resulting tree height is less than or equal to H. When a node is deleted, its children are directly attached to its parent.

The final solution involved:

Bottom-up height calculation
Greedy pruning strategy
Prioritizing nodes contributing the most excess height

The interviewer focused heavily on proof of correctness, complexity analysis, and worst-case scenarios such as skewed trees.

Coding Round 3: Design Rate Limiter II

This was very similar to the well-known Hack2Hire Rate Limiter problem.

Requirements included supporting multiple rate-limiting strategies while maintaining high efficiency.

The discussion evolved through:

Sliding Window
Fixed Window
Token Bucket
Leaky Bucket

Eventually the interviewer pushed toward distributed deployment scenarios. We discussed Redis, Lua scripts, synchronization challenges, and the trade-offs between eventual consistency and strong consistency.

System Design: Quota Management Service

The system needed to support:

User quota allocation
Quota release
Independent quota limits
Automatic expiration and recovery
High concurrency
Distributed deployment

The interviewer spent significant time on back-of-the-envelope calculations:

QPS estimation
Storage requirements
Peak traffic analysis
Machine sizing

After mentioning a balanced read/write workload, I was immediately asked about:

Database selection
Caching strategy
Read/write separation
Failure handling
Monitoring and alerting
Scalability bottlenecks

One lesson became very clear: non-functional requirements are just as important as functional requirements in modern system design interviews.

Behavioral & Bar Raiser

Typical behavioral questions included:

Tell me about a disagreement with your team and how you resolved it.
What role did you play in a major project?
Describe an important technical trade-off you made.

One memorable Bar Raiser question was:

Your manager asks you to improve a business metric by 20%. How would you approach it?

My response framework was:

Analyze existing data
Identify root causes
Form hypotheses
Run controlled experiments
Validate through small-scale rollout
Scale gradually with monitoring and iteration

Throughout the discussion, I emphasized ownership, cross-functional collaboration, and measurable impact.

Behavioral Preparation Tips

Snowflake strongly values engineers who demonstrate ownership and initiative.

Real project experiences, decision-making processes, stakeholder management, and measurable outcomes matter much more than rehearsed stories.

Final Thoughts

Today's top-tier tech interviews are becoming increasingly challenging. Solving coding problems alone is no longer enough.

Candidates should be prepared for:

Algorithms with heavy follow-up discussions
Distributed systems and consistency trade-offs
System design deep dives
Ownership-focused behavioral interviews

If you're preparing for Snowflake, Databricks, Stripe, Meta, TikTok, or other top engineering organizations, investing time in both technical depth and communication skills can make a huge difference.

Need Help Preparing for Technical Interviews?

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Airbnb SDE New Grad Interview Experience (2026) | Full Process Breakdown & Lessons Learned

interview-show-Etesis Elay — Thu, 18 Jun 2026 14:19:18 +0000

I recently completed the full interview process for an Airbnb Software Engineer New Grad position. From application to offer, the entire process took roughly 3–5 weeks, with feedback usually arriving within 2–3 business days after each round. Compared with many large tech companies, the process felt organized and efficient.

Most interview guides online are based on experiences from several years ago. The 2026 hiring cycle feels noticeably different, both in terms of difficulty and evaluation focus. Here's a detailed breakdown of what I experienced.

Interview Process Overview

Recruiter Screen (30 min) → Technical Screen (60 min, CoderPad) → Virtual Onsite (4 rounds) → Team Matching → Offer

The overall process was straightforward and transparent, without long periods of uncertainty between stages.

Recruiter Screen

Although this round appears to be a simple background conversation, it is also an important culture-fit assessment.

One of the most common questions is:

Why Airbnb instead of other tech companies?

Candidates who provide generic answers often struggle here. Airbnb places significant emphasis on its mission of creating a sense of belonging. Spending time understanding the company's products, recent initiatives, and mission statement can make your responses much more authentic.

Simply saying that Airbnb is a great company is rarely enough.

Technical Screen

The technical screen was conducted on CoderPad. Some candidates may receive a HackerRank assessment first depending on the role and recruiting pipeline.

One key takeaway: interviewers expect fully working code. Pseudocode is generally not sufficient.

My question involved string processing with multiple follow-up discussions. Friends of mine received graph traversal, interval scheduling, and BFS-related problems. Overall difficulty felt medium to medium-high.

After completing the implementation, I was expected to run through test cases, validate edge conditions, and explain potential improvements. Code quality and communication were evaluated alongside correctness.

Virtual Onsite

The onsite consisted of four rounds completed within a single day:

Algorithm Round #1
Algorithm Round #2
System Design
Core Values Behavioral Interview

Maintaining focus and energy throughout the day is almost as important as technical preparation.

Algorithm Interviews

Airbnb interviewers care about more than simply arriving at a correct solution. They want candidates to clearly explain the engineering reasoning behind each design decision.

For example, if you solve an LRU Cache problem, you should be able to discuss:

Why a doubly linked list is used
Why hash maps are necessary
The purpose of dummy head and tail nodes
Complexity tradeoffs

Dynamic Programming appears frequently. Rather than presenting standard textbook problems, Airbnb often wraps classic DP concepts inside realistic business scenarios. The challenge is not only solving the problem but also identifying the underlying DP model.

Being able to clearly explain state definitions, transition equations, and complexity analysis can significantly improve performance.

Many people still believe Airbnb asks relatively easy coding questions. Based on recent experiences, that perception feels outdated. The DP questions in the onsite process were noticeably more challenging than typical medium-level interview problems.

System Design

A common topic revolves around booking availability systems for short-term rentals.

For New Grad candidates, interviewers generally do not expect highly sophisticated distributed systems knowledge. However, they do expect a solid understanding of core consistency and concurrency challenges.

Topics that may be explored include:

Preventing double booking
Optimistic locking vs. pessimistic locking
Idempotency key design
Handling payment retries safely
Inventory release after payment timeouts
Delayed queue mechanisms
Consistency tradeoffs between hosts and guests

The emphasis is usually on reasoning through real-world scenarios rather than drawing complex architecture diagrams.

Core Values Interview

This round is frequently underestimated.

Many candidates focus heavily on coding and system design but neglect behavioral preparation. In reality, the Core Values interview can be a decisive factor.

The major themes typically align with Airbnb's values:

Champion the Mission
Be a Host
Embrace the Adventure
Be a Cereal Entrepreneur

Preparing strong STAR-format stories for each category is highly recommended.

One common mistake is recycling generic behavioral answers used for other companies. Airbnb interviewers are often able to distinguish between genuine experiences and heavily rehearsed responses.

The value "Be a Cereal Entrepreneur" originates from Airbnb founders selling cereal boxes to help fund the company during its early days. The interviewer is usually evaluating resourcefulness, ownership, and problem-solving under constraints rather than looking for formal startup experience.

Final Thoughts

Airbnb is not a company where coding practice alone guarantees success.

The technical interviews are challenging, particularly in Dynamic Programming. System design discussions are highly business-oriented and focused on practical tradeoffs. The Core Values interview acts as a hidden filter that many candidates underestimate.

Strong preparation across all three dimensions—coding, system design, and behavioral interviews—is essential. Neglecting any one of them can significantly reduce your chances of receiving an offer.

Interview Preparation Support

Before my Virtual Onsite, I used Interview Aid for interview preparation and mock interview support. Their services include OA assistance, interview preparation, and virtual onsite support designed to help candidates better understand the interview process and improve performance.

Learn more at: https://interview-aid.com/services

Anthropic CodeSignal OA Experience: 90-Minute Progressive OOD Assessment Breakdown

interview-show-Etesis Elay — Wed, 17 Jun 2026 11:18:07 +0000

I recently completed Anthropic's CodeSignal Online Assessment and honestly, it was one of the most unique OAs I've taken so far. Unlike traditional coding assessments filled with LeetCode-style algorithms, this one focused almost entirely on object-oriented design, state management, and business logic modeling. The assessment lasted 90 minutes and consisted of four progressively unlocked levels. You had to complete the current level before moving on to the next.

I managed to pass all four levels, but Levels 3 and 4 definitely required careful debugging and edge-case handling. The experience felt much closer to solving real software engineering problems than typical algorithm interviews. If you're preparing for Anthropic, CodeSignal, or similar business-oriented assessments, hopefully this breakdown will help you understand what to expect.

Assessment Structure

The entire OA revolves around building and extending an employee time-tracking and payroll management system. Each level introduces additional requirements while preserving all previous functionality.

A typical design might maintain the following worker state:


class Worker:
    def __init__(self, worker_id, position, compensation):
        self.worker_id = worker_id
        self.position = position
        self.compensation = compensation
        self.is_inside = False
        self.enter_time = None
        self.work_records = []
        self.pending_promotion = None

One of the most important design decisions is that completed work records should only be generated when an employee leaves the office. Every future calculation, including total working time and salary computation, should rely exclusively on completed records. This keeps the system consistent and avoids many edge-case issues later.

Level 1: Employee Registration and Time Tracking

The first level introduces basic employee management and clock-in/clock-out functionality.

Main operations:

add_worker(workerId, position, compensation)
register(workerId, timestamp)

The register operation follows a simple state-toggle pattern.

If the employee is outside the office, they enter and a new work session begins.
If the employee is already inside, they leave and the current work session is finalized.

When entering the office:

Apply any pending promotion if one exists.
Record the entry timestamp.

When leaving the office:

Create a completed work record.
Store the interval and compensation associated with that session.

Although this level is relatively straightforward, building clean state transitions here is critical because every later feature depends on this foundation.

Level 2: Total Working Time Calculation

The second level introduces:


get_total_time(workerId)

This function returns the total duration of all completed work sessions.


def get_total_time(self, worker_id):
    if worker_id not in self.workers:
        return -1

    worker = self.workers[worker_id]

    return sum(
        end - start
        for start, end, _ in worker.work_records
    )

The biggest detail to remember is that active work sessions should not be included. Only completed records count toward total working time.

Many candidates lose points by accidentally including the currently active session in their calculations.

Level 3: Delayed Promotions (The Hardest Part)

This level introduces promotions:


promote(workerId, new_position, new_compensation, start_timestamp)

This turned out to be the most important design challenge in the entire assessment.

At first glance, it seems natural to immediately update an employee's position and compensation when promote() is called. However, that approach causes multiple test cases to fail.

The correct behavior is that promotions do not become effective immediately. Instead, they should only take effect the next time the employee enters the office.

This means the promotion must be stored temporarily:


worker.pending_promotion = (
    new_position,
    new_compensation
)

Then, during the next clock-in event, the promotion is applied before the new work session begins.

Why is this necessary?

Because compensation should remain consistent throughout an entire work session. A worker should never have one salary rate for the first half of a session and another salary rate for the second half.

This design mirrors real-world HR systems where compensation changes often take effect at the start of a new work cycle rather than in the middle of an active period.

I spent the majority of my debugging time on this level. If you're practicing similar problems, make sure to create test cases involving:

Multiple promotions
Consecutive clock-ins and clock-outs
Promotions scheduled between work sessions
Repeated promotion requests

Level 4: Salary Calculation Within a Time Range

The final level introduces:


calc_salary(workerId, start, end)

The goal is to calculate total compensation earned within a specified time interval.

For each completed work record:

Determine the overlap between the work session and the query interval.
Multiply the overlap duration by the compensation rate associated with that record.
Accumulate the results.


def calc_salary(self, worker_id, start, end):
    if worker_id not in self.workers:
        return -1

    total = 0

    for r_start, r_end, comp in self.workers[worker_id].work_records:
        overlap_start = max(r_start, start)
        overlap_end = min(r_end, end)

        if overlap_start < overlap_end:
            total += (
                overlap_end - overlap_start
            ) * comp

    return total

The overlap calculation itself is simple, but boundary handling is extremely important.

Common edge cases include:

Query interval fully contains a work session
Work session fully contains the query interval
Partial overlap on either side
No overlap at all
Intervals touching at a single point

Remember that only completed work records should be considered. Any active session currently in progress must be ignored.

Time Allocation

My approximate time breakdown looked like this:

Level 1: 10 minutes
Level 2: 10 minutes
Level 3: 15–20 minutes
Level 4: 15 minutes
Remaining time: debugging and edge-case testing

Most of the extra time went toward validating promotion behavior and interval boundary conditions.

Biggest Lessons Learned

This OA is not primarily about algorithmic complexity.

Instead, it evaluates:

Object-oriented design
State management
Business logic implementation
Code organization
Attention to edge cases

Anthropic appears to place significant emphasis on writing software that is maintainable, logically consistent, and easy to reason about.

The challenge felt much closer to implementing a real internal business system than solving competitive programming problems.

Preparation Tips

If you're preparing for Anthropic or similar CodeSignal assessments, I would recommend:

Practicing object-oriented design problems rather than only algorithm questions.
Building systems such as parking lots, library management systems, file systems, and reservation platforms.
Thinking carefully about state transitions before writing code.
Prioritizing correctness and readability over premature optimization.
Writing comprehensive tests for boundary conditions.

A useful mindset is to imagine how a real HR platform or attendance-tracking system would behave. Many of the requirements in this OA closely resemble real-world business workflows.

Final Thoughts

This assessment was a refreshing change from traditional coding interviews. Rather than focusing on advanced algorithms, it emphasized software design, state consistency, and practical engineering judgment.

As online assessments continue evolving, more companies seem to be moving toward realistic business scenarios that better reflect day-to-day engineering work. Strong algorithm skills are still valuable, but system thinking and careful implementation are becoming increasingly important.

If you're preparing for Anthropic or other CodeSignal-based assessments, hopefully this breakdown gives you a clearer picture of what to expect. Good luck, and happy coding!

Amazon SDE OA Common Questions & Preparation Guide

interview-show-Etesis Elay — Tue, 16 Jun 2026 13:51:30 +0000

I recently reviewed some of the most common Amazon SDE Online Assessment (OA) question patterns. Although Amazon OA questions change frequently, many core patterns appear repeatedly. Most questions focus on areas such as Arrays, Strings, HashMaps, Sorting, Simulation, and Greedy algorithms.

Amazon SDE OA Coding Questions Breakdown

Coding Question 1: Transaction Logs

Problem Overview:

Given a list of user transaction records. Each transaction contains:

sender receiver amount

You need to count how many transactions each user is involved in. Both sending and receiving transactions should be counted. Return all user IDs whose transaction count is greater than or equal to the given threshold.

Example:

Input:

logs = [
"1 2 50",
"2 3 100",
"1 3 20"
]

threshold = 2

Output:

[1,2,3]

What it tests:

HashMap
String Parsing
Sorting

Key points:

A user appearing as both sender and receiver needs to be counted correctly. Careful parsing and handling duplicate transactions are important.

Coding Question 2: Server Updates

Problem Overview:

You are given a group of servers with different states. During each update round, the state of neighboring servers affects the next state.

Return the final server state after performing k update operations.

What it tests:

Array Simulation
Dynamic Programming
Edge Case Handling

This type of simulation problem appears frequently in Amazon OA. The main challenge is implementing state transitions correctly and handling boundary conditions.

Coding Question 3: Password Strength

Problem Overview:

Given a password, determine how many additional characters are needed to make it valid.

Typical requirements include:

Minimum length requirement
Contains numbers
Contains uppercase letters
Contains lowercase letters
Contains special characters

What it tests:

String Processing
Character Counting
Condition Handling

This problem focuses on careful implementation rather than advanced algorithms. Missing one condition is a common mistake.

Coding Question 4: Fresh Promotion

Problem Overview:

Given a shopping cart item list and promotion rules, determine whether the customer qualifies for a promotion.

Example:

Shopping Cart:

[apple, apple, orange]

Promotion Rule:

apple apple
orange anything

Return true or false depending on whether the promotion pattern matches.

What it tests:

Sliding Window
Two Pointer
Pattern Matching

The main challenge is handling wildcard items and matching the promotion sequence correctly.

Coding Question 5: Nearest Delivery Route

Problem Overview:

Given delivery locations represented as coordinates:

[x, y]

Find the k closest locations to the warehouse.

What it tests:

Sorting
Heap
Priority Queue

This is a common optimization-style problem where candidates need to decide between sorting all locations or using a heap for better efficiency.

Amazon SDE OA Preparation Focus

For Amazon OA preparation, it is usually better to focus on high-frequency patterns instead of randomly solving a large number of problems.

HashMap + Array
Sliding Window
Heap / Priority Queue
Greedy Algorithms
Simulation
BFS / DFS

Amazon OA also includes the Leadership Principles Assessment. Many candidates focus only on coding but underestimate the behavioral assessment, which can also affect the final result.

How to Prepare for Amazon SDE OA

A strong Amazon OA preparation strategy is to understand recurring patterns, improve coding speed, and practice solving problems under time constraints. Instead of only increasing the number of problems completed, learning how to quickly identify the correct algorithm pattern is usually more effective.

Interview Aid - Technical Interview Preparation Support

Interview Aid focuses on North America technical interview preparation, providing support for OA, coding interviews, and virtual onsite interview preparation. We help candidates prepare more efficiently through algorithm analysis, coding strategy review, and interview communication improvement.

If you are preparing for Amazon SDE OA, coding interviews, or final round interviews, building a focused preparation plan early can improve your overall interview performance.

Optiver OA Experience | 90min Coding + Probability + Logic + Game Test Breakdown

interview-show-Etesis Elay — Mon, 15 Jun 2026 14:11:52 +0000

Just finished the Optiver Online Assessment, and the best way to describe it is simple: it’s not about difficulty alone — it’s about endurance under sustained pressure.

The entire assessment takes around 3 hours, but what stands out is the intensity. There is almost no breathing room between sections, and every part is designed to test how consistently you can perform under time constraints. For anyone new to quant or trading firm interviews, the experience can feel like a shock in terms of pace and density.

OA Structure Overview

The Optiver OA is composed of four major components:

Quantitative Coding (90 minutes)
Beat the Odds (Probability Test)
Number Logic Test
Zap-N Game Test

Each section evaluates a different dimension — from algorithmic thinking to probability intuition and cognitive speed.

1. Quantitative Coding (90 min)

This section includes two coding problems. The difficulty is around Medium to Medium+, but the real challenge lies in modeling real trading scenarios rather than solving abstract algorithm puzzles.

Problem 1: Worst Trade Reporter

The task involves processing trade data and computing profit and loss (PnL). Each record contains fields such as trade type, instrument, buy/sell indicator, price, and volume.

The core logic revolves around:

Data aggregation
State maintenance across trades
Hash map / grouping logic
Edge case handling for incomplete or overlapping trades

This is less about algorithms and more about correctly simulating a financial data pipeline.

Problem 2: Order Book Matching Engine

This problem simulates a simplified trading engine. You need to match buy and sell orders based on price constraints:

Buy orders match with the lowest sell price ≤ P
Sell orders match with the highest buy price ≥ P
Matched orders are removed from the order book
Track total transaction value from all executed trades

A typical approach involves using:

Min-heap for sell orders
Max-heap for buy orders

However, the real difficulty is not the data structure — it’s handling cascading matches, repeated executions, and maintaining consistency when orders continuously interact.

2. Beat the Odds (Probability Test)

This section contains 30 questions, each with a strict 90-second time limit and no ability to revisit previous questions.

The focus is heavily on core probability concepts:

Conditional probability
Bayes’ theorem
Expected value
Permutations and combinations
Independence vs dependence

The main challenge here is not conceptual difficulty, but time pressure. Many questions are solvable, but there is simply not enough time to deeply analyze each one.

3. Number Logic Test

This section lasts about 25 minutes and contains around 26 sequence-based questions.

The patterns typically involve:

Arithmetic and geometric sequences
Pattern transformations
Multi-step logical deductions
Rapid pattern recognition

The pace is intense — averaging less than one minute per question. By the later part of the test, it becomes more about instinctive recognition than deliberate reasoning.

4. Zap-N Game Test

This section consists of 9 mini cognitive games covering memory, reaction, and attention control.

It is less about “playing well” and more about maintaining consistency under stress:

Reaction speed
Working memory capacity
Attention switching ability
Performance stability under pressure

Key Takeaways

1. Coding is only one part of the evaluation

Unlike traditional SWE interviews, coding is just one component. The problems are business-oriented and require strong modeling ability rather than pure algorithmic tricks.

2. Math and logic carry significant weight

Probability and number logic sections are critical filters. Many candidates underestimate how important these are compared to coding.

3. Time pressure is the real filter

Across all sections, the defining factor is not difficulty but speed. The OA is designed to test sustained accuracy under continuous time constraints.

Preparation Tips

If you're preparing for Optiver or similar firms like Jane Street or IMC, focus on the following areas:

Master probability fundamentals (Bayes, conditional probability, expected value)
Practice trading-style coding problems (order books, transaction systems)
Improve mental math speed and accuracy
Train sequence and pattern recognition under time pressure
Simulate full-length timed assessments regularly

Final Thoughts

The biggest takeaway from the Optiver OA is this: it’s not about solving a single hard problem — it’s about maintaining accuracy and composure under sustained cognitive pressure.

Many candidates can handle coding problems individually, but performance often drops when probability, logic, and fatigue accumulate across multiple sections.

If You Are Preparing for Quant / Big Tech OA

If you are targeting companies like Optiver, Jane Street, IMC, DRW, or other trading firms, the key gap is usually not knowledge — but exposure to real assessment conditions.

Most candidates struggle because they have not experienced:

Full-length OA simulation under strict timing
Probability and mental math under pressure
Continuous multi-module fatigue management

Structured preparation and realistic simulation can significantly improve stability on test day.

Interview Aid focuses on North America tech and quant interview preparation, including OA simulation, mock interviews, and full interview strategy design. Many candidates use structured practice sessions before their actual OA to replicate real testing pressure and stabilize performance.

You can learn more here: Interview Aid Services

Hope this breakdown helps anyone preparing for Optiver OA. Good luck and stay consistent under pressure.

Tesla OA Questions & Solutions | SQL, String Manipulation and Prefix Sum Problems

interview-show-Etesis Elay — Sun, 14 Jun 2026 12:34:15 +0000

I recently completed the Tesla Software Engineer Online Assessment and managed to AC all three questions in about 30 minutes. The problem set covered three very different areas: SQL, string processing, and prefix-sum hashing. None of the questions were extremely difficult, but each one had a few subtle edge cases that could easily cost you hidden test cases if you weren't careful.

Here's a breakdown of the three questions along with the key ideas and common pitfalls.

Question 1: SQL – Test Group Statistics

The first question involved two tables:

test_groups – stores group names and point values for each test case in the group.
test_cases – stores execution results for individual test cases.

The task was to calculate, for every test group:

Total number of test cases
Number of passed test cases
Total score

The final result needed to be sorted by total score descending and group name ascending.

Solution Idea

Use test_groups as the primary table and perform a LEFT JOIN with test_cases. Then aggregate by group name.

Common Pitfalls

The biggest trap was using an INNER JOIN instead of a LEFT JOIN.

If a group contains no test cases, an INNER JOIN removes the group entirely from the result set. However, the problem explicitly requires every group to appear, with a score of 0 if no test cases exist.

Another subtle issue is score calculation. The correct formula is:

Passed Test Cases × Test Value

Many candidates mistakenly use:

SUM(test_value)

Since test_value exists at the group level, failed test case rows still contain the same value. Summing directly will overcount and produce incorrect scores.

A good validation test is creating a group with no associated test cases and verifying that it still appears in the final output with a score of zero.

Question 2: Insert Maximum Number of 'a' Characters

Given a string S, insert as many lowercase 'a' characters as possible at arbitrary positions.

The constraint is that the final string must never contain three consecutive 'a' characters.

If the original string already contains "aaa", return -1.

Solution Idea

Treat every non-'a' character as a separator.

These separators divide the string into multiple gaps. Each gap can contain at most two consecutive 'a' characters. If more than two appear within a gap, the string would contain "aaa".

For every gap:

Remaining Capacity = max(0, 2 - Existing A Count)

The answer is simply the sum of the remaining capacities across all gaps.

Common Pitfalls

The first mistake is forgetting the final gap after the last character. If the calculation only occurs when a non-'a' character is encountered, the last segment is never processed.

The second mistake is misunderstanding the limit. Each gap may contain at most two total 'a' characters, not two newly inserted characters.

For example:

S = "aa"

The gap already contains two 'a's, so its remaining capacity is 0. The correct answer is 0, not 2.

Question 3: Count Zero-Sum Subarrays

Given an integer array A, count the number of subarrays whose sum equals zero.

If the answer exceeds 1,000,000,000, return -1.

Solution Idea

This is a classic Prefix Sum + Hash Map problem.

Initialize:

prefixSum = 0
result = 0
map = {0:1}

As you iterate through the array:

Update prefix sum.
Check how many times the current prefix sum has appeared before.
Add that frequency to the result.
Update the hash map.

Whenever the same prefix sum appears twice, the subarray between those positions has sum zero.

Common Pitfalls

The order of operations matters:

Check frequency
→ Add to result
→ Update frequency

Changing this order can easily lead to overcounting or missing valid subarrays.

Another important detail is the initialization:

{0:1}

Without it, any zero-sum subarray starting from index 0 will be missed.

The overflow condition should also be checked immediately after each update to the result rather than waiting until the end.

Consider:

A = [2, -2, 3, 0, 4, -7]

Prefix sums become:

2, 0, 3, 3, 7, 0

The final answer is 4, corresponding to four valid zero-sum subarrays.

Final Thoughts

Overall, the Tesla OA felt very manageable if you're comfortable with common interview patterns. None of the questions required advanced algorithms, but each one contained subtle implementation details that could easily cause hidden test case failures.

The SQL question tested careful aggregation logic, the string problem focused heavily on edge-case handling, and the prefix-sum question rewarded candidates who understood the standard hash-map counting pattern.

Interview Preparation Support

Before taking the OA, I worked with Interview Aid to review common Tesla-style assessment patterns and edge cases. Having someone point out the typical hidden pitfalls ahead of time saved a lot of debugging time during the actual assessment.

Interview Aid provides:

OA Assistance
OA Guidance & Support
VO (Virtual Onsite) Assistance
Mock Interviews
Interview Strategy Coaching

Learn more here: Interview Aid – Service Details