DEV Community: interviewshow-cs

How We Helped a Candidate Land a Citadel SDE Offer: Full Interview Breakdown (2026)

interviewshow-cs — Fri, 24 Jul 2026 15:53:49 +0000

We recently worked closely with a Software Engineer candidate throughout the entire Citadel interview process, and they successfully received an offer.

If you're preparing for top quantitative trading and fintech companies like Citadel, Jane Street, Optiver, Hudson River Trading (HRT), or Two Sigma, here's a quick breakdown of what the interview looked like and how we prepared for each stage.

Online Assessment (OA)

The OA was hosted on HackerRank and included 2–3 LeetCode Medium to Hard coding problems. Common topics included:

Arrays
Sliding Window
Dynamic Programming
Graphs
Interval Merging

Citadel places significant emphasis on efficiency. Passing visible test cases isn't enough—poor time complexity or missing edge cases can easily lead to rejection.

During preparation, we focused heavily on optimization strategies, complexity analysis, and hidden edge cases rather than simply finding an accepted solution. The candidate successfully passed the OA.

Phone Screen

The first technical interview combined coding and behavioral questions.

The interviewer evaluated:

Core data structures and algorithms
Problem-solving process
Complexity analysis
Quick mathematical reasoning
C++ language fundamentals

Before the interview, we practiced explaining trade-offs, Big-O analysis, and answered numerous C++ deep-dive questions, helping the candidate stay confident throughout the conversation.

Superday (Four Consecutive Interviews)

Round 1 — Coding Interview

The coding problem was around LeetCode Hard difficulty. The interviewer expected continuous communication while writing code rather than silent implementation.

The primary evaluation focused on code robustness, handling of edge cases, clean implementation, and the ability to explain design decisions throughout the solution.

Round 2 — C++ Deep Dive

This round explored low-level C++ knowledge in depth, including:

Virtual functions and polymorphism
Object memory layout
Move semantics
Lock-free data structures
Cache locality
Lock contention optimization
Nanosecond-level latency optimization

The interviewer also discussed why high-frequency trading systems often prefer C++ over Python, asking the candidate to compare performance, latency, development efficiency, and engineering trade-offs.

Round 3 — System Design

This interview focused on low-latency distributed systems commonly found in quantitative trading infrastructure.

The candidate designed and discussed:

Real-time Order Book Matching Engine
Pricing Engine
Distributed Backpressure
Message Queues
Time-Series Storage
Hybrid Push/Pull Architecture

Beyond drawing the architecture, the discussion covered bottlenecks, failure recovery, scalability, consistency, and performance trade-offs under production workloads.

Round 4 — Technical Behavioral Interview

The final round combined technical discussion with behavioral questions.

Typical topics included:

Using the STAR framework to explain a production incident
Balancing code readability against extreme performance optimization
Resolving cross-team engineering conflicts
Why Citadel?
Examples of ownership and engineering impact

Preparation Tips

All interviews were conducted entirely in English, so clear technical communication is just as important as coding ability.

Prepare genuine project stories rather than memorized answers. Quant firms care deeply about engineering judgment, especially your ability to explain trade-offs, performance considerations, scalability decisions, and production reliability.

Also, apply early. Most quantitative trading firms recruit on a rolling basis, and interview opportunities become increasingly competitive as hiring progresses.

About Interview Show

Interview Show is a coaching team focused on helping candidates prepare for software engineering and quantitative finance interviews across North America.

Our mentors come from leading technology companies and quantitative trading firms and have extensive experience supporting candidates through highly competitive interview processes.

We provide personalized one-on-one guidance throughout the entire recruiting journey, including:

OA preparation
Mock technical interviews
System design coaching
Behavioral interview preparation
Communication and presentation improvement
Interview strategy tailored to each company

Many candidates report significant improvements in both interview performance and offer conversion after working with our mentors.

If you're preparing for Citadel, Jane Street, Optiver, HRT, Two Sigma, or other top quantitative firms, feel free to reach out for a free evaluation. We'll build a preparation plan based on your background and target companies.

Learn more:
🌐 https://interviewshow.com

Contact us:
📩 https://interviewshow.com/contact-us/

Good luck with your interviews, and we hope to see you land your dream offer soon!

Two Sigma OA 2026 Experience | 105-Minute Assessment with 3 Real Questions (Passed)

interviewshow-cs — Wed, 22 Jul 2026 14:28:21 +0000

I recently completed the Two Sigma Online Assessment (OA) and successfully passed it. The biggest takeaway from this assessment is that Two Sigma's OA style is quite different from typical big tech coding tests. It is not just about grinding LeetCode problems — it focuses heavily on statistics, mathematical modeling, and implementing analytical solutions correctly.

The assessment lasted 105 minutes with 3 questions. The first two questions were labeled as "approximation solutions," while the third one was categorized as coding. However, the third question was also not a typical LeetCode-style programming problem. Instead, it focused on linear regression, OLS models, and incremental data processing.

The first two questions were relatively familiar if you have reviewed previous Two Sigma OA experiences. Knowing the question patterns beforehand can save a lot of time during the test.

Question 1: Linear Interpolator — Handling Interpolation, Extrapolation, and Edge Cases

The first question was about implementing a Linear Interpolator. Given a set of data points (x, y), the task was to build an interpolation function that returns an estimated y value for any given x.

Although the concept is simple, the problem contains several important edge cases. The solution needs to handle three scenarios:

Interpolation when the query x falls between existing data points
Extrapolation when x is outside the given range
Multiple data points sharing the same x coordinate

My approach was to first sort all points by their x values. During each query, I used binary search to locate the correct position and then handled different cases separately.

For interpolation, the formula is:

y = y₁ + (x - x₁) / (x₂ - x₁) × (y₂ - y₁)

For extrapolation, the slope between the two boundary points was extended beyond the existing range.

The trickiest part was handling duplicate x values. Many solutions only consider normal cases and forget that multiple points can have identical x coordinates. This type of edge case is exactly where hidden test cases usually fail.

Question 2: Linear Regression — Mainly Testing Statistical Formula Knowledge

The second question was Linear Regression. Compared with traditional algorithm questions, this problem was closer to a statistics implementation exercise.

The question contained multiple subparts, including:

Standard deviation calculation
Median calculation
Linear regression slope calculation
Finding the best single feature predictor
Finding the best pair of features
Selecting the best multi-variable feature set

The main challenge was not algorithm design, but remembering the formulas correctly.

For linear regression, the key formula is:

β = Cov(X,Y) / Var(X)

The feature selection parts were mainly based on comparing correlation values or model performance metrics such as R².

This problem does not require advanced data structures. The most common mistakes are incorrect formulas, missing preprocessing steps, and floating-point precision issues.

Question 3: Univariate OLS Regression — OLS Formula and Incremental Updates

The third question was Univariate OLS Regression, which was the most coding-oriented problem in the assessment. However, it was still heavily based on statistics.

The first part provided two dataframes and required calculating regression coefficients between different column pairs.

The standard Ordinary Least Squares (OLS) formulas were:

β = Σ(xᵢ - x̄)(yᵢ - ȳ) / Σ(xᵢ - x̄)²

α = ȳ - β × x̄

The second part was more interesting. Instead of receiving all data at once, new batches of data arrived continuously. The task was to update the regression parameters without recalculating everything from scratch.

The key idea was maintaining cumulative statistics:

n — number of samples
ΣX — sum of all X values
ΣY — sum of all Y values
ΣX² — sum of squared X values
ΣXY — sum of X multiplied by Y

After each batch arrived, these values were updated and the regression parameters were recalculated:

β = (n·ΣXY - ΣX·ΣY) / (n·ΣX² - (ΣX)²)

α = (ΣY - β·ΣX) / n

This approach allows each new batch to be processed efficiently without rerunning the entire dataset, which matches real-world streaming data scenarios.

Final Thoughts: Two Sigma OA Tests More Than Just Coding Skills

Overall, the biggest challenge of Two Sigma's OA is not algorithm complexity. The assessment focuses more on whether you can correctly translate mathematical models into working code.

The first two questions become much easier if you are familiar with statistics formulas beforehand. For the third question, understanding OLS and incremental computation is the key.

If you are preparing for quantitative companies like Two Sigma, Citadel, or Jane Street, do not only focus on LeetCode. Reviewing probability, statistics, regression models, and data processing concepts can make a huge difference.

Many problems may look like coding questions, but they are actually testing whether you can implement mathematical ideas accurately.

Preparation Resources

For candidates preparing for quantitative company OAs, I also used interviewshow for mock practice and interview preparation. The simulations helped me become more familiar with the question style and identify weak areas before the actual assessment.

Hopefully this Two Sigma OA 2026 experience helps others preparing for similar quantitative interviews. Good luck with your OA and future offers!

Akuna Capital OA Experience: Communications Handler + React AI Coding Challenge

interviewshow-cs — Tue, 21 Jul 2026 08:50:33 +0000

Before receiving my Akuna Capital OA invitation, I went through quite a few interview experiences and noticed one thing: Akuna’s question pool is relatively small, and repeated questions happen quite often.

This OA was 75 minutes with two coding problems. The first problem was a traditional programming question and I solved it in around 7–8 minutes on the first try. The second problem was an AI coding style challenge, and I spent almost 25 minutes debugging before getting all test cases accepted.

Important Note: Akuna Capital’s AI Coding Detection

One thing worth knowing before taking the OA: Akuna has implemented AI usage detection mechanisms. They analyze code structure, compare patterns against typical AI-generated outputs, and check similarities between candidates’ submissions. They also design some problems with AI traps, where blindly generated AI solutions can fail in specific ways.

The best approach is still to write the code yourself. The problems themselves are not extremely difficult, but understanding the requirements and debugging carefully are much more important than trying to generate a solution automatically.

Question 1: Communications Handler

The first problem was a communication management system. A communication line can only support two users at the same time, and you need to implement a handler that manages three operations:

Establish a connection
Hang up a connection
Clear all connections

There were two classes involved:

CommsHandler

The class required three methods:

connect: Create a connection between users. Connecting a user to themselves should throw an exception. If the line is already occupied, it should also throw an exception. Otherwise, establish the connection and return the expected message.
hangup: Disconnect a user. Hanging up with themselves should throw an exception. If the two users are currently connected, remove the connection. Otherwise, throw an exception.
clear_all: Remove all active connections unconditionally.

CommunicationException

This was simply a custom exception class. The constructor receives an error message string.

The Caller class was already implemented, so there was no need to modify it.

Solution Approach

The logic was straightforward: maintain an active_connections variable storing the two currently connected users, then handle each operation by checking the conditions from the prompt.

However, there was one easy-to-miss trap:

The variable name had to be exactly active_connections.

The boilerplate main function already referenced this variable name. I initially created my own variable name, which caused runtime errors. After changing it back, everything passed.

This was probably the most common mistake in this problem. Before writing code, carefully check the provided framework and keep all predefined variable names unchanged.

Another important detail: exception messages must match the requirements exactly. Even a small difference in capitalization, punctuation, or spacing can fail hidden test cases.

Question 2: Password Strength Checker (React AI Coding)

The second problem was an AI coding style challenge. You were given a complete React project with files such as:

App.tsx
PasswordChecker.tsx
PasswordStrength.tsx

The task was to implement a password strength checker that dynamically displays password strength text and background color based on how many requirements the password satisfies.

Component Responsibilities

PasswordChecker.tsx

This component was responsible for:

Maintaining the input state
Managing the input value and onChange handler
Handling the clear/reset action
Passing the current password value to PasswordStrength

PasswordStrength.tsx

This component received the password as a prop and handled:

Checking how many password requirements were satisfied
Mapping the score to a strength level such as Weak, Fair, or Strong
Rendering the corresponding text and background color

How I Approached the AI Coding Challenge

The difficult part was not the implementation itself. The time was mainly spent running tests, checking failures, and making targeted fixes.

My approach was:

First read the test case names carefully.
Use the tests as the real specification.
Implement one behavior at a time.
Run tests repeatedly until everything passed.

For example, test names like:

should be empty initially
should have value of Weak

were basically telling me exactly what behavior was expected. Reading the tests was much faster than only relying on the written prompt.

Final Thoughts

Both problems were not particularly difficult. The biggest time sink was the debugging process in the React AI coding challenge and the variable naming issue in the first question.

If you know these patterns beforehand, 75 minutes is more than enough time.

Akuna’s OA question pool is relatively small, and many of these problems appear repeatedly. Practicing similar questions in advance can make the actual assessment much smoother.

InterviewShow’s quantitative interview track includes preparation materials for companies like Akuna, Jane Street, and DRW, covering OA practice, problem-solving strategies, and VO preparation. More information is available at: interviewshow.com/services.

OpenAI SWE Interview Questions: Plant Infection Coding & System Design Breakdown

interviewshow-cs — Mon, 20 Jul 2026 11:39:54 +0000

OpenAI SWE interviews are different from most software engineering interviews. The difficulty is not mainly about algorithms — it is about how well you handle evolving requirements. The problem keeps changing, constraints get added, and your code and design need to survive those changes without constant rewrites.

The full interview process usually includes coding and system design screens, followed by Virtual Onsite rounds covering coding, design, project deep dive, and behavioral questions.

Here is a complete breakdown of the rounds and what was actually tested.

Screen Coding: Plant Infection Problem (Five Progressive Rounds)

This was the most revealing problem in the entire process. It represents the style OpenAI often looks for: not just solving the first version of a problem, but building a solution that can continuously adapt as requirements evolve.

Round 1: Basic Infection Spread

The first question was a warm-up.

You are given a 2D grid where plants can either be healthy or infected. Every day, infected plants spread to neighboring plants in four directions. The task is to calculate how many days it takes for all reachable plants to become infected.

The standard solution is multi-source BFS. Start from all infected cells, process the grid level by level, and count the number of days required for the infection to spread.

Round 2: Add Immune Plants

The second requirement introduced immune plants. Immune plants cannot become infected.

The BFS modification is simple: skip immune cells during propagation. However, this round tests whether your initial implementation was designed with future extensions in mind.

Round 3: Plants Recover and Gain Immunity

This was the first real turning point.

A plant recovers after being infected for D days and becomes immune afterward. Now each cell needs additional state information, such as the time infection started.

The order of daily operations becomes critical:

Does infection happen before recovery?
Does recovery happen before new infections spread?
What happens when multiple events happen on the same day?

Clarifying these rules before coding is essential. Many candidates fail because they start implementing without confirming the event sequence.

Round 4: Add Death State

The fourth requirement added another lifecycle state. Plants die after being infected beyond a certain number of days.

The solution now requires tracking more time-based transitions. The challenge is no longer BFS itself, but managing a growing state machine cleanly.

Round 5: Burn One Row or Column

The final version was the hardest.

On day zero, you can burn one entire row or one entire column. Burned plants become dead and cannot spread infection. The goal is to minimize the final number of dead plants.

A possible approach is:

Enumerate every possible row and column choice.
Simulate the infection process after each choice.
Select the minimum death count.

There are O(n+m) possible choices, and simulation is acceptable. A greedy approach is not guaranteed to work.

The biggest lesson from this problem: the first version should be completed quickly, but the architecture matters. If the initial design does not support extensions, later rounds become endless refactoring.

Screen Design: Cloud IDE Sandbox

The system design question was about building an online coding environment where users can write and execute code remotely.

The basic architecture is only the beginning. The follow-up questions focused on production-level engineering decisions.

Code Execution Security

The interviewer cared heavily about untrusted code execution.

Important points to discuss:

Sandbox isolation
Container-based execution
System call restrictions
Resource limits
Network isolation

Security is especially important at OpenAI, so proactively discussing sandboxing and isolation can demonstrate strong engineering awareness.

VM Strategy

A common follow-up:

Should virtual machines be created on demand, or should you maintain a warm pool?

Cold start approach reduces idle cost but increases latency.
Warm pools improve response time but consume more resources.

Handling Runtime Failures

The interviewer may ask:

What if code runs forever?
What if memory usage explodes?
What if execution crashes?

Expected solutions include resource quotas, watchdog services, execution time limits, and forced termination.

File Upload and Sharing Design

Additional follow-ups included:

How to prevent single points of failure?
How does instant upload work?
How do you handle large-scale file sharing?

Strong answers include:

Content hashing for deduplication
Chunked uploads with retry mechanisms
CDN and caching strategies
Metadata indexing systems

VO Coding: Two Problems

Social Network Snapshot

The task was to implement:

follow()
unfollow()
snapshot()
Query relationships from historical snapshots

The key trade-off:

Deep copy the entire graph at every snapshot: simpler queries but high storage cost.
Store relationship history: saves space but increases implementation complexity.

Explaining this trade-off clearly is often more valuable than immediately writing code.

Versioned Array

Implement:

set(index, value)
snap()
get(index, version)

The optimal approach is not storing every full snapshot. Instead, store modification history for each index and use binary search during retrieval.

For follow-up questions like “How would you optimize for millions of operations?”, sparse snapshots and binary search are the standard direction.

VO System Design: Payment Processor

The payment system design started with the normal payment flow, but the real evaluation came from production scenarios.

Important Follow-ups

Idempotency: How to handle duplicate payment requests.
Ledger and state machine: Ensure transaction states are consistent and unambiguous.
Third-party callbacks: Handle retries with idempotent processing and exponential backoff.
Stuck payments: Timeout detection and rollback mechanisms.
Settlement consistency: Maintain correctness during batch processing.
Worker failures: Persist task state and support at-least-once execution.

These questions are designed to test whether you have dealt with real production reliability problems.

Project Deep Dive and Behavioral Questions

The project deep dive focuses heavily on technical decisions:

Why did you choose this architecture?
What trade-offs did you consider?
What would you redesign today?

Compared with Amazon leadership principle interviews, OpenAI discussions are more technically focused.

Common Behavioral Questions

Why OpenAI?
What is your view on AI safety?
How do you handle cross-team conflicts?
Have you ever built something that you decided not to launch?

The “Why OpenAI” question requires real preparation. They care about whether candidates genuinely understand and connect with the mission.

Key Takeaways

Do not rush into coding. Spend time clarifying states and event order.
The first question is only the entry point. The real evaluation starts when requirements change.
OpenAI strongly values security thinking, especially around sandboxing and reliability.
Behavioral answers need to show engineering judgment, not generic leadership stories.

While preparing, I realized that OpenAI’s progressive coding style is difficult to practice by only solving LeetCode problems. You need practice where someone continues adding requirements after your first solution and forces you to extend existing code instead of rewriting everything.

I prepared with InterviewShow’s OpenAI SWE interview preparation program. Their team members come from leading North American technology companies and have experience with interview processes at companies including OpenAI, Meta, and Stripe. They cover progressive coding problems like Plant Infection, system design scenarios like Payment Processor, and provide preparation from OA strategy to VO mock interviews.

Amazon SDE Interview Experience 2026 | Seattle Onsite, Team Match, Coding & GenAI Questions

interviewshow-cs — Sat, 18 Jul 2026 14:57:04 +0000

I finished my Amazon SDE interview at the end of April and received my offer on May 5th. There was also a stressful moment in between: my original team suddenly lost headcount, so I had to go through a new team matching process after passing the interviews.

Instead of writing a traditional round-by-round interview log, I want to share the things that actually surprised me, the questions that caught me off guard, and the lessons I only understood after the process was over.

Amazon SDE Interview Timeline

November: Applied for Amazon SDE position
Early March: Received OA invitation
April 1: Received onsite interview invitation
April 20: Seattle onsite interview
April 22: Received email saying I passed interviews, but original team no longer had HC
April 28: Matched with a new team and scheduled HM call
May 1: HM interview
May 5: Offer received

The most stressful moment was definitely the April 22 email. Passing the interviews but losing the team because of headcount was a strange feeling. Fortunately, the team matching process only took about a week.

All Three Interviewers Were Indian — Does It Matter?

A lot of people worry about interviewer background, especially accents. In my case, all three interviewers were Indian. But after going through the entire process, my biggest takeaway was:

Their follow-up questions were very reasonable. If you cannot explain something clearly, you simply cannot explain it — it has nothing to do with the interviewer's accent.

They focused heavily on details, tradeoffs, and the reasoning behind my decisions.

Round 1: Hiring Manager Interview

The first interviewer was the most extroverted one. Before the official interview started, we spent several minutes casually chatting.

Behavioral Questions

The behavioral questions were standard Amazon-style questions:

Tell me about a time you had a tight deadline.
How did you debug a difficult issue?
How do you use AI tools in your daily development?

The AI question was especially interesting because it has become a much more common topic recently. The interviewer followed up with:

"How do you reduce AI hallucinations?"

I answered based on my real experience using Copilot, including:

Reviewing AI-generated code carefully
Identifying situations where AI output is unreliable
Using tests and documentation to validate suggestions

If you have never actually used AI coding tools, this question is difficult to answer naturally.

Coding Question

The coding problem was not from LeetCode. Amazon created a business scenario:

Given the latest 1,000 orders, determine whether a new order was created by an abusive user.

The algorithm itself was easy, around LeetCode Easy level. The difficult part was that the requirements were vague.

I spent around five minutes clarifying requirements before coding. The implementation itself only took about ten minutes.

My biggest lesson:

For Amazon business-style coding questions, clarification is often more important than coding speed.

Round 2: Applied Scientist Interview + Shadow Interviewer

The second interviewer was an Applied Scientist. There was also a remote shadow interviewer in the meeting.

At first, I thought the shadow interviewer was only observing. However, he actually asked follow-up questions later.

This was something I had rarely seen mentioned in previous interview experiences.

Behavioral Questions

The behavioral questions included:

A time you failed and what you learned
A time you gained someone's trust
A time you improved an existing system
A time you learned something new

The conversation felt a little messy because the interviewer jumped between topics, and my answers were not as structured as I wanted.

Coding Question

The coding problem:

Given a list of unordered timestamps and a maximum gap value, two timestamps belong to the same interval if they are within the allowed gap. Return all valid intervals.

The solution was straightforward:

Sort timestamps
Scan linearly
Merge intervals

After I finished, the shadow interviewer asked:

"How can you optimize this further?"

I got stuck because I believed my solution was already optimal.

The lesson:

Shadow interviewers are not just observers. They may challenge your solution too.

Round 3: Hiring Manager Interview — Almost Pure Conversation

The third interviewer was another Hiring Manager. Surprisingly, there was almost no technical content.

The conversation focused on:

Why I transferred schools
What CS courses I was taking
My favorite CS subject
The business logic behind my startup experience
My experience with GenAI

The entire interview lasted around 40 minutes, including three questions from me.

The funny thing:

The interviewer became more and more expressionless toward the end. I thought I failed.

Four days later, I received the offer.

Do not judge your result based on the interviewer's facial expression.

Amazon SDE Interview FAQ

Was coding on a whiteboard or computer?

I wrote code on a whiteboard in the meeting room. I brought my laptop, but we did not use it.

Did the HM evaluation call include coding?

No. It was purely conversational.

Was there resume deep dive?

Yes. Most behavioral questions were actually deep dives into my resume experiences.

How long did it take to confirm the interview schedule?

About two days.

What GenAI questions were asked?

Mainly:

How do you use AI tools?
How do you reduce AI hallucinations?

What I Would Prepare Differently Next Time

Looking back, there were several things I underestimated.

1. Coding Questions Were Business Scenarios, Not LeetCode

All three coding rounds were custom Amazon-style business problems. The difficulty was not high, but the requirements were unclear.

Clarification skills mattered more than memorizing algorithms.

2. GenAI Has Become a Real Interview Topic

Two rounds asked about AI usage. Interviewers were not only asking whether you use AI, but whether you understand its limitations.

3. Behavioral Preparation Still Matters

The first two rounds heavily focused on behavioral questions. However, my third HM round was almost completely casual conversation.

4. Passing Interviews Does Not Always Mean Immediate Offer

Team availability and headcount can still affect the final result. My team match experience was a reminder that the process is not always linear.

Final Preparation Advice

The two things I was least prepared for were:

"How do you reduce AI hallucinations?"
"Can you optimize your solution further?"

These are not questions you can solve by grinding LeetCode.

For AI questions, you need real experience using AI tools and understanding their failure modes.

For optimization questions, you need practice explaining tradeoffs after reaching a standard solution.

During my preparation, I used InterviewShow for Amazon mock interviews. The biggest improvement came from repeatedly being pushed with questions like "Can you optimize this?" until thinking about alternatives became automatic.

When the real interview happened, I was much calmer because I had already experienced that pressure during practice.

Conclusion

Amazon SDE interviews are not just about solving coding problems. The interviewers care about how you clarify requirements, explain decisions, handle ambiguity, and think about engineering tradeoffs.

Good luck to everyone preparing for Amazon interviews. Hope everyone gets the offer they want.

Akuna Capital OA Checks Whether You Used AI — Even Analyzing Your Syntax Tree

interviewshow-cs — Thu, 16 Jul 2026 12:00:36 +0000

I just finished Akuna Capital's OA on HackerRank. The format was three coding problems in two hours.
The problems themselves were not extremely difficult, but there is one thing worth mentioning first.

Akuna Capital clearly states in the OA instructions that AI tools are not allowed.
Their detection methods are also quite advanced: they analyze the code's abstract syntax tree (AST),
compare patterns against typical AI-generated solutions, and check for similarities between candidates'
submissions.

Even more interestingly, they have reportedly included AI trap questions.
Some problem statements contain details designed to mislead AI reasoning, causing generated solutions
to fail in specific ways. For candidates who rely heavily on vibe coding, this OA is definitely not
something to gamble on.

The good news is that Akuna's question pool is relatively small. There are only around a dozen recurring
problems, and the overlap rate is quite high. Here is a breakdown of the three questions I encountered.

Question 1: Minimum Swaps

A store wants to arrange products from highest popularity to lowest popularity. Each product has a unique
popularity value. In one operation, you can swap any two products. Find the minimum number of swaps needed.

Example:
[3, 4, 1, 2]

Swap 3 and 4:
[4, 3, 1, 2]

Swap 1 and 2:
[4, 3, 2, 1]

The answer is 2 swaps.

Solution Approach

This is a classic permutation cycle problem.
Map every element's current position to its correct position after sorting.
The array can then be divided into several cycles.
A cycle of length k requires exactly k - 1 swaps to fix.

Therefore, the answer is:

number of elements - number of cycles

Implementation steps:

Find the target index of each element after sorting in descending order.
Use a visited array to detect cycles.
For each cycle of length k, add k - 1 to the answer.

Question 2: Delivery Management System

A company delivers packages between cities. Cities are connected by bidirectional roads, and some cities
may not be reachable.

Delivery order follows these rules:

Visit cities with smaller distance from headquarters first.
If two cities have the same distance, visit the city with the smaller index first.

Example:

Four cities with roads:
1-2, 2-3, 2-4

Starting from city 1:
[2, 3, 4]

Solution Approach

Use BFS to calculate the shortest distance from the starting city because all edges have equal weight.
Then sort reachable cities by:

(distance, city_number)

Important edge cases:

The headquarters city should not appear in the result.
Ignore unreachable cities.
Be careful with 1-based indexing.

Question 3: Movie Ratings

Alex has a sequence of movie ratings, including positive and negative values.
He wants to select a subsequence with the maximum possible total rating.
However, he cannot skip two movies consecutively.

Example:

For:
[-1, -3, -2]

The second movie must be selected, or both the first and third movies must be selected.
The maximum score is:
-3

Another example:

[-3, 2, 4, -1, -2, -5]

The optimal selection is:
[2, 4, -2]
with a total score of 4.

Solution Approach

This is a linear dynamic programming problem.
The rule "cannot skip two consecutive movies" means the distance between any two selected indexes
cannot exceed 2.

Define:

dp[i] = maximum score when selecting the i-th movie.

Transition:

dp[i] = ratings[i] + max(dp[i-1], dp[i-2])

The final answer is:

max(dp[n-1], dp[n-2])

because skipping the last movie is allowed, but skipping the last two movies is not.

Final Thoughts

These three problems test three classic patterns:

Permutation cycles for minimum swaps
BFS shortest path with custom sorting
Linear dynamic programming

The common theme is that the problems are wrapped in business scenarios:
product ranking, delivery systems, and movie festivals.
Once you remove the story, they are standard algorithm patterns.

The most valuable information is that Akuna's OA question pool is relatively small and has a high overlap
rate. Reviewing the recurring patterns before the assessment can significantly improve your preparation
efficiency.

I collected these verified Akuna OA questions and similar quantitative interview materials through
InterviewShow.
For companies with smaller question pools, practicing representative problems before the OA usually
provides the highest return.

Good luck with your interviews!

PayPal Software Engineer Interview Experience 2026: Coding, System Design & Fintech Engineering Focus

interviewshow-cs — Wed, 15 Jul 2026 11:38:25 +0000

Just finished my PayPal SDE interview, and honestly, it felt completely different from typical big tech interviews.

The short version: the coding questions were not difficult, around LeetCode Medium level. But if you approach it like a pure algorithm contest, you are likely to fail. PayPal was not mainly evaluating how clever your algorithm was. They cared much more about whether your edge cases were handled properly, whether your data consistency was guaranteed, and whether you had strong awareness of security and reliability.

The reason is simple: in fintech, a single production bug can potentially cause millions of dollars in losses. That engineering mindset shaped the entire interview.

PayPal SDE Interview Process

The interview process consisted of three stages:

Recruiter Screen
Technical Interview 1: Coding
Technical Interview 2: System Design + Behavioral Questions

The entire process was fast. From application to offer decision, it took around three weeks.

My technical interview lasted around 45–50 minutes. The format was very compressed:

First 20 minutes: System Design
Next 30 minutes: Coding

The interviewer moved quickly and expected concise answers rather than long explanations.

PayPal System Design Interview: Security and Reliability Over Scalability

The system design round was the most surprising part. Instead of giving me 20 minutes to slowly design a complete architecture, the interviewer asked around five rapid-fire questions within 20 minutes.

The best strategy was simple: answer what you know, explain your reasoning clearly, and move on quickly if you get stuck. Spending too much time on one detail can easily hurt you.

Password Management System Design Question

The main design question was a password management system with requirements including:

Password length between 8 and 16 characters
Must contain uppercase letters, lowercase letters, and special characters
Cannot contain English dictionary words

The discussion focused heavily on how to implement banned word filtering.

The interviewer asked whether I would use a Trie or HashSet.

My answer:

HashSet: Provides O(1) lookup for exact matching, but does not support prefix search or flexible extensions.
Trie: Better for prefix matching, dynamic updates, and large dictionaries where words are frequently added or removed.

I also mentioned using a dynamically updatable Trie structure, which helped demonstrate scalability thinking.

Follow-up questions focused on:

How to control Trie memory usage
What happens when the dictionary becomes extremely large
Validation time complexity
Password generation strategy
Regex-based validation design

This is very typical PayPal style. The solution itself is not the only thing that matters. They want to know whether you understand the trade-offs behind your design decisions.

PayPal Coding Interview Experience

The coding round included two problems in about 30 minutes. Both were based on financial scenarios.

Question 1: Maximum Balance Day

Given an initial balance and a list of transactions (deposit / withdraw), return the day when the account balance was highest.

The algorithm was straightforward:

Iterate through transactions
Maintain current balance
Track maximum balance and corresponding date

However, the real evaluation was not the algorithm. It was edge case handling:

How should multiple transactions on the same day be handled?
If multiple days have the same maximum balance, which date should be returned?
What happens if withdrawal exceeds available balance?

If you immediately start coding without clarifying these cases, this is exactly where mistakes happen.

Question 2: Family Tree Asset Calculation

The second problem involved a family tree structure. Each person had personal assets, and the goal was to calculate inherited family assets from parents and children relationships. For multiple children, the assets needed to be divided equally.

The solution required DFS with post-order traversal.

The key idea:

Calculate all child values first
Aggregate the result back to the parent node
Find the richest ancestor

My advice: draw the tree before coding. Family relationship problems can become confusing quickly if you only visualize them mentally. A simple diagram usually makes the solution obvious.

Important Coding Interview Tip

Explain your thinking while coding.

This round clearly prioritized reasoning over completion speed. A candidate who explains assumptions, trade-offs, and edge cases can perform better than someone silently typing perfect code.

PayPal Behavioral Interview: Fintech Engineering Mindset

The behavioral questions were strongly connected to engineering responsibility.

Examples:

"How do you test your code?"
"What quality makes an engineer successful?"

These questions were not casual conversations. They were evaluating engineering maturity.

Strong answers should focus on:

Testing coverage
Code review process
Monitoring
Gradual deployment
Rollback strategies

In fintech, reliability and risk awareness matter much more than simply saying "I am passionate about technology."

My Key Takeaways From PayPal SDE Interview

1. Coding Difficulty Is Not the Main Challenge

The problems were around LeetCode Medium level, but the evaluation was completely different from traditional algorithm interviews.

The focus was:

Clean edge case handling
Correct financial calculations
Data consistency
Engineering judgment

2. System Design Focuses on Security and Reliability

For payment-related systems, security and reliability come before beautiful architecture diagrams.

A scalable design that ignores security is not a good answer for PayPal.

3. Fast Follow-ups Test Decision Making

The interviewer moved quickly and asked many follow-up questions.

The ability to prioritize, communicate clearly, and avoid getting stuck mattered a lot.

4. Thinking Process Matters More Than Perfect Code

Throughout the coding round, explaining assumptions and reasoning was more important than typing silently and finishing every line.

How I Prepared for PayPal Interview

Looking back, the biggest advantage was understanding what PayPal was actually evaluating.

Knowing that system design focused on security rather than pure scalability helped me spend preparation time on the right topics.

Knowing that coding emphasized reasoning and edge cases allowed me to focus on communication instead of rushing implementation.

Knowing that follow-up questions would focus on trade-offs helped me prepare complexity analysis and engineering decisions beforehand.

This kind of interview insight is difficult to find from standard question lists. Real interview experiences and structured preparation resources often provide information beyond just the problems themselves.

I found these insights through the Fintech interview preparation resources from InterviewShow, which covers the full preparation process from OA practice to technical interview simulations.

Understanding the interviewer's evaluation criteria can sometimes matter more than memorizing another hundred coding questions.

Good luck to everyone preparing for PayPal and other fintech engineering interviews.

Scale AI SDE Interview Experience | Coding Round, System Design & Full Process Breakdown (2026)

interviewshow-cs — Tue, 14 Jul 2026 13:02:07 +0000

I recently finished my Scale AI SDE interview process and wanted to share a complete breakdown.
Looking back, the most important thing was not a specific coding problem, but one small detail:
when the recruiter contacted me before the interview, they specifically recommended using Python.
They mentioned that Java could be difficult to finish within the time limit.

At first, I didn't think much about it. After completing the interview, I realized that this was
actually a lifesaving reminder. Scale AI has a strict rule: your solution needs to pass every single
test case to be considered successful. Missing even one hidden test case means failure.
Combined with the tight time constraint, using Java can make it extremely difficult to finish,
debug, and achieve a fully accepted solution.

This requirement basically defines the personality of Scale AI's interview process:
the challenge is not only algorithm difficulty, but speed + accuracy under pressure.
Below is my complete interview experience, including the process, coding questions, system design,
and preparation tips.

Scale AI SDE Interview Process

The typical Scale AI Software Engineer interview process usually includes:

Recruiter Screen
Phone Screen (multiple technical interviews)
Virtual Onsite (System Design, Coding, Behavioral, Hiring Manager rounds)

My phone screen consisted of two back-to-back rounds:

One coding interview
One Hiring Manager behavioral screen

Scale AI Coding Interview: What They Usually Test

Scale AI coding interviews have a very recognizable style. Instead of purely asking textbook
LeetCode questions, they often combine coding problems with real-world scenarios.
A single problem may contain multiple parts and follow-up questions.

The interview mainly tests whether you can quickly understand requirements, handle edge cases,
and build a correct solution under strict time pressure.

Common topics include:

Interval Processing: For example, managing party schedules, merging overlapping time ranges, and calculating available gaps. The key skills are sorting and interval merging.

<li>
  <strong>Data Structure Design:</strong>
  Using HashMap-based designs such as ID → timestamps or community → event lists.
</li>

<li>
  <strong>Time Format Conversion:</strong>
  Converting between AM/PM formats and integers. String processing mistakes are common here.
</li>

<li>
  <strong>Poker Hand Evaluation:</strong>
  A problem involving wildcard cards, which was the main challenge in my interview.
</li>

<li>
  <strong>System Design:</strong>
  Designing asynchronous workflows, such as splitting large tasks into smaller pieces,
  processing them in parallel with LLM services, and returning results through notifications.
</li>

My Coding Round: Poker Hand Evaluation

My coding problem was a poker hand validation system. The problem had three progressive parts.

Part 1: Validate Poker Hands

You are given a Card class containing rank and suit information.
Implement a method that receives five cards and determines whether they match one of six
valid poker hand categories.

If the hand matches a valid category, return True.

Part 2: Add Joker Wildcards

The second part introduced JOKER cards.
JOKER can represent any card, meaning it works as a wildcard.

The tricky part was that the second implementation needed to remain backward compatible:
it had to pass all previous test cases from part one as well.

The key challenge was finding whether the wildcard cards could complete the strongest possible
poker hand. The solution required either:

Enumerating possible wildcard replacements
Or calculating what cards were missing to form each poker category

Part 3: Compare Two Hands

The final part provided two players' hands and required determining the winner.
The solution was:

Assign priorities to poker categories
Find the strongest possible category for each hand
Compare category rankings and card values

The Card class and test cases were already provided. The main task was implementing the logic.

The Most Important Tip: Use Python

I want to emphasize this again: Python matters for Scale AI interviews.

Because every test case needs to pass, the goal is not just writing a theoretically correct
solution. You need enough time to implement, debug, and verify everything.

Python's built-in support for strings, dictionaries, and sets can save a significant amount of time.
Even if you are more comfortable with Java, forcing yourself to use it may create unnecessary
pressure during this interview.

This interview is not testing your ability to write Java syntax. It is testing whether you can
deliver a fully accepted solution within a strict time limit.

Hiring Manager Screen: Behavioral Interview

The second round was a pure behavioral interview.
The questions were standard and focused on ownership, leadership, and personal growth.

The questions included:

Tell me a time you made a hard decision. Talk about the trade-off.
Tell me a time you failed a project and needed to redo it. What hard work did you do?
What do you see yourself doing in 3 years?

Preparing STAR-format stories is highly recommended.
Strong answers should include measurable impact, technical challenges,
and examples of taking ownership.

Virtual Onsite Experience

The onsite rounds are more focused on system design and real engineering scenarios.

Common topics include:

CSV/JSON processing
Calling external APIs
Debugging multi-file projects
Designing scalable backend systems

For system design, one topic worth preparing is asynchronous parallel LLM processing:

Split a large task into multiple smaller pieces
Process pieces in parallel
Collect results asynchronously
Notify users when the final result is ready

This type of design is closely related to Scale AI's business and tests distributed system thinking.

Final Thoughts After Passing Scale AI SDE Interview

The hardest part of Scale AI's interview is not necessarily the algorithm itself.
The real challenge is the combination of:

Tight time limits
Strict all-test-case acceptance requirements

Practicing alone with LeetCode cannot fully simulate this pressure.
When you practice by yourself, there is always a relaxed feeling of "I finished the solution."
The real interview environment is different: someone is watching your progress,
time is running, and one missed edge case can decide the result.

In my case, the recruiter’s Python recommendation helped a lot.
Another important factor was practicing under realistic interview pressure before the actual round.

I used

InterviewShow

during preparation.
The main reason was saving time instead of manually collecting interview patterns from different
sources. For candidates with limited preparation time, having a structured way to practice
realistic interview scenarios can be very useful.

Everyone prepares differently, but one thing I strongly recommend:
before the real interview, practice writing fully accepted solutions under pressure.
Being able to finish and pass all tests matters much more than simply knowing more problems.

Good luck to everyone preparing for Scale AI SDE interviews!

Snowflake SDE OA Experience: Two HackerRank Questions Breakdown (2026)

interviewshow-cs — Mon, 13 Jul 2026 11:59:46 +0000

Just finished this Snowflake SDE OA, and honestly the two HackerRank problems were not the type that require crazy algorithms. The ideas came pretty quickly, but most of the time was spent carefully implementing the details.

However, I do not want to only share solutions. There is one thing about Snowflake OA that many candidates underestimate: you need to pass all test cases to move forward to the VO stage.

Because the problems look straightforward, they are actually more dangerous. Many candidates think "the logic is simple, I can just code it quickly", but one missing edge case or one incorrect condition can fail hidden test cases and end the process.

For Snowflake OA, the mindset is different from many other companies: it is not just about solving the problem. It is about getting a perfect score with zero failed cases.

Snowflake OA Format and Rules

The assessment platform is HackerRank. There are two coding questions, usually around medium difficulty. One is typically easier with an important observation, while the other requires more careful dynamic programming or counting logic.

The input sizes are not always huge, but each problem has a key insight. Once you identify the pattern, the implementation is relatively short.

The most important thing is the all-pass requirement: every hidden test case matters. Missing even one edge case can prevent you from reaching the interview stage.

Question 1: Generating Login Codes (Two Pointer + Segmentation)

Problem Description

You are given two arrays:

initialLogin with length n
standardLogin with length m

You can repeatedly perform an operation:

Choose any continuous subarray from either array and replace it with the sum of its elements.

For example:

[1,5,6,8,2]

can become:

[12,8,2]

by replacing [1,5,6] with [12].

The goal is to make the two arrays equal after operations, while maximizing the final length. If it is impossible, return -1.

Example:

initialLogin = [2,4,3,7,10]
standardLogin = [6,5,5,10]

Answer = 3

Solution Approach

The first observation is the impossibility condition:

Replacing segments with their sums changes only the segmentation, not the total sum. Therefore, if the total sums of the two arrays are different, the answer must be -1.

When the sums are equal, use a two-pointer segmentation strategy.

Maintain two pointers and accumulate segment sums from both arrays. Whenever one side has a smaller current sum, continue extending that side. Once both segment sums become equal, we have found one matching segment. Increase the answer count and continue from the next positions.

The final number of matched segments is the maximum possible length.

Time complexity:

O(n + m)

Common Edge Cases

Do not forget the total sum comparison. Different sums must immediately return -1.
Carefully handle the final unmatched segment.
Make sure both pointers finish correctly.

Question 2: String Formation (Dynamic Programming Counting)

Problem Description

You are given an array of strings where every string has the same length, and a target string.

You need to build the target string by selecting characters from the given words. The selected character positions must be strictly increasing.

Different solutions are counted separately if:

The selected index sequence is different.
The same index is selected but the character comes from a different word.

Return the number of ways modulo 10^9 + 7.

Example:

words = ["adc","aec","efg"]
target = "ac"

Answer = 4

Solution Approach

This is a classic dynamic programming counting problem.

Define:

dp[j] = number of ways to match the first j characters of target

Initialize:

dp[0] = 1

Before running the DP, preprocess:

For each position i and character c, count how many words have character c at position i:

cnt[i][c]

Then iterate through positions from left to right. For each position, update the DP array from right to left:

dp[j] += dp[j-1] * cnt[i][target[j-1]]

The backward update is critical because it prevents using the same index multiple times.

The final answer is:

dp[target.length]

Common Edge Cases

Always apply modulo operations to avoid overflow.
Update DP from right to left, not left to right.
Remember that selected indexes must be strictly increasing.

Why Many Candidates Fail Snowflake OA

The biggest trap with Snowflake OA is that the problems often look easier than they actually are.

The algorithms are not necessarily the hardest part. The challenge is writing completely correct code under a strict all-test-case requirement.

For these two questions, the most common mistakes are:

Missing the total sum check in the segmentation problem.
Forgetting modulo operations in DP counting.
Updating DP in the wrong direction.

Your solution can be 90% correct and still fail the OA because of one hidden case.

My recommendation for Snowflake OA preparation is simple: even when the idea feels obvious, reserve time to manually test edge cases before submitting.

Preparation Resources

The good news is that Snowflake OA questions have a relatively high repetition rate. These patterns, including two-pointer segmentation and DP counting, appear frequently.

During preparation, I used InterviewShow to review frequently asked OA questions and solution patterns. For an assessment where every test case matters, reviewing common templates and edge cases beforehand can save a lot of time during the real test.

Good luck to everyone preparing for Snowflake SDE OA. Hope you all get a perfect score and move on to the VO stage.

Amazon SDE OA Experience (2026) | New AI Coding Round + Real Questions Breakdown

interviewshow-cs — Sat, 11 Jul 2026 13:39:39 +0000

I just finished this Amazon SDE OA, and I had to write it down because it was completely different from every Amazon OA I've taken before. The first problem was the familiar algorithm question, but the second one took me by surprise—it dropped me into a fully working Django project and asked me to fix and extend existing functionality. Looks like Amazon has officially embraced the AI Coding interview trend.

The entire assessment lasted 90 minutes. I spent about 35 minutes on the first problem and devoted almost all of the remaining time to the second. My biggest takeaway is this: this new format is much less friendly for candidates who only grind LeetCode. If you've actually built projects before and are comfortable reading other people's code, this section can actually be easier than another hard algorithm question. Below is a complete breakdown of both questions.

What Is Amazon AI Coding?

I had never seen this format before, so it caught me off guard. Instead of writing a function from scratch, Amazon provides a complete runnable project with dozens of files. Your job is to understand the existing codebase, fix bugs, implement missing features, and pass all the provided pytest cases.

The biggest differences compared with the traditional OA are:

Instead of an empty function, you work inside a real project repository.
Your score depends entirely on passing the test suite rather than code style.
The environment is a full IDE where you can edit multiple files and run tests during the assessment.

If you're interested in Amazon's official explanation of this new format, you can check their preparation materials for the SDE Online Assessment.

OA Structure

The assessment runs on Amazon's own platform. There are two sections (S1 and S2), each containing one problem. The timing felt generous. After finishing the first algorithm question, I still had nearly an hour left for the AI Coding section.

Question 1: Traditional coding problem with hidden test cases.
Question 2: Complete Django project with multiple files, file explorer, and built-in test runner.

Question 1 — Grid Distribution Centers

You are given a binary grid where every 1 represents an existing distribution center. Distance is measured using the Chebyshev distance:

max(|dx|, |dy|)

You may convert at most one 0 into a new distribution center. The objective is to minimize the maximum distance from every cell to its nearest distribution center. Return that minimum possible value.

My Solution

First, perform a multi-source BFS starting from every existing distribution center to compute the distance from each cell to its nearest center. Since Chebyshev distance allows movement in eight directions, the BFS expands in all eight neighboring cells. A two-pass dynamic programming solution also works.

The answer is monotonic. If distance d is feasible, then every larger distance is also feasible, which naturally leads to binary search.

Inside the feasibility check, collect every cell whose current distance exceeds the candidate value. Each of these cells must be covered by the newly added center. Under Chebyshev distance, the coverage area becomes a square with side length 2 × mid + 1. The problem therefore reduces to determining whether all these coverage squares share a common intersection containing at least one valid grid cell. Maintaining the maximum and minimum x/y coordinates is sufficient to test whether the intersection exists.

The geometric property of Chebyshev distance makes this solution especially clean because every coverage region is a square rather than a circle. Overall complexity is approximately O(nm log(max(n,m))).

Question 2 — Fixing Recurring Wallet Transfers

The second problem provided a complete Django wallet application. The recurring payment functionality was intentionally broken. The task was to repair it while supporting recurring transfers, request validation, and scheduled execution.

My Solution

Most of the work happened inside the serializer. I added several validation rules:

The start date cannot be later than the end date.
The transfer amount must be greater than zero.
The sender and receiver cannot be the same wallet.
Invalid requests immediately raise ValidationError.

When executing a recurring payment, I checked:

Whether the payment is still active.
Whether the current date has reached the next scheduled payment time.
Whether the sender has sufficient balance.

The easiest bug to miss was updating last_payment_date. Without updating this field after a successful transfer, the same payment would execute repeatedly during the same cycle. One of the hidden tests specifically checked this scenario, and I failed it on my first attempt.

I also wrapped the transfer inside a database transaction so that deducting the sender balance and crediting the receiver remain atomic.

Compared with the algorithm question, this task rewarded code-reading skills much more than coding speed. Instead of reading every file from top to bottom, I strongly recommend starting with the README and the failing test cases. Working backward from the tests dramatically reduces the amount of code you need to understand.

Final Thoughts

Overall, I'd rate the OA as moderate difficulty. The first problem becomes manageable once you identify the binary search plus intersection approach. The second problem is mostly about careful debugging and understanding an unfamiliar codebase.

One more preparation tip. Traditional LeetCode practice does not prepare you for this new AI Coding format. You'll need experience working inside real projects, reading existing code, and fixing failing tests.

I've been practicing with the Amazon AI Coding question bank on InterviewShow, which now includes project-based AI Coding exercises similar to the new OA. They also offer Amazon mock interviews before VO rounds, making it easier to adapt to the latest interview style. If you've recently received an Amazon OA invitation and aren't sure what to expect, it's worth checking out.

Good luck, and I hope everyone gets that green "All Tests Passed."

Amazon 2026 New Grad VO Experience | Three Moments That Changed My View of the Interview

interviewshow-cs — Thu, 09 Jul 2026 11:23:18 +0000

I finally wrapped up all four rounds of Amazon's 2026 New Grad Virtual Onsite interview last month. It took me a while to recover before writing this recap. Instead of another round-by-round interview diary—which there are already plenty of online—I wanted to focus on the three moments that stood out the most. Each of them reflects a real change in how Amazon interviews candidates this year. I'll still mention the coding questions, but the emphasis is on the parts that most interview reports rarely talk about.

Moment 1: "What if AI-generated code ships with a bug?"

The third interview was with a senior engineer who appeared very approachable. The conversation started with standard Leadership Principle questions before he casually asked:

"Do you use GenAI tools?"

I assumed it was simply small talk and mentioned that I regularly use GitHub Copilot. That turned out to be only the beginning.

The follow-up questions became increasingly specific:

What are the limitations of large language models?
How do you deal with hallucinations?
What do you focus on when reviewing AI-generated code?
If AI-generated code passes review but later causes production issues, how would your debugging process differ from debugging human-written code?
If you had to design a metric to evaluate AI-assisted software development, what would you measure?

I can confidently say these are not questions you can memorize from interview guides. My answers came entirely from personal experience. When reviewing AI-generated code, I explained that I pay extra attention to edge cases and hidden assumptions because AI tends to make mistakes differently than human engineers.

For the productivity metric, I suggested measuring the reduction in repetitive boilerplate code, since that is one of the first areas where AI changes engineering workflows. The interviewer became genuinely interested, and we spent nearly fifteen minutes discussing the topic.

Looking back, the message was clear: Amazon is beginning to treat AI-assisted software development as an interview topic rather than a casual conversation.

My advice is simple: Don't just read about AI tools—actually use them for several weeks before interviewing. Real experience is very difficult to fake once interviewers begin asking follow-up questions.

The coding question in this round was LeetCode 31 — Next Permutation. The standard three-step solution (find the pivot, swap, reverse the suffix) was sufficient.

Moment 2: I Got Stuck on an Elevator Scheduling Problem

The final Bar Raiser interview included an elevator scheduling problem.

This was not an object-oriented design interview. There were no UML diagrams or class hierarchies. Instead, it was a pure implementation problem centered around scheduling logic.

The interviewer defined several core functions with fairly realistic constraints:

Passengers inside and outside the elevator can request floors simultaneously.
Arriving at a floor should trigger arrival events.
The scheduler must determine travel direction dynamically.
Multiple passengers may be waiting on the same floor.

I implemented a scanning algorithm using separate priority queues for upward and downward requests. Everything went smoothly until I reached the transition logic: What should the elevator do when it is moving upward but no requests remain above its current position?

I froze for over a minute because I had two competing approaches in mind.

What ultimately saved me was the interviewer's hint. After following his suggestion, I adjusted the scheduling logic and finished the implementation successfully. His final feedback was that the overall structure was clean and that I handled edge cases well.

This experience taught me something important: Getting stuck isn't what hurts your interview. What matters is what you do while you're stuck.

Instead of remaining silent, I openly explained both candidate solutions and discussed the trade-offs between them. That transparency encouraged the interviewer to guide me in the right direction.

If you freeze during an interview, verbalizing your thought process is far better than silently trying to appear confident.

Interestingly, I had encountered a similar scheduling problem during a mock interview on InterviewShow. That previous experience didn't help because it predicted the exact question—it helped because I'd already experienced the feeling of getting stuck and recovering under pressure. That's the real value of mock interviews.

This round also included a variation of the Celebrity Problem, solved using the standard elimination approach.

Moment 3: One Leadership Principle Story Was Questioned Five Levels Deep

The behavioral section of the Bar Raiser interview focused on one of my strongest Ownership stories. I thought I had prepared it extremely well.

Then the follow-up questions started.

"What happened next?"
"Was that decision made by you or by the team?"
"You said the result was successful. Do you have numbers?"
"If you could do it again, what would you change?"
"What permanent process improvements came out of this project?"

By the third follow-up, I had exhausted my rehearsed version of the story. Everything afterward relied entirely on my genuine memory of the project.

That, in my opinion, is exactly how Bar Raisers evaluate candidates. They aren't listening for polished storytelling. They're stress-testing authenticity. Fabricated experiences rarely survive five layers of detailed questioning because people simply don't invent operational details like long-term process improvements.

Because of that, my preparation advice differs from most interview guides. Instead of preparing ten perfectly polished stories, prepare four or five projects that you genuinely understand. Review every decision, every metric, every trade-off, and the complete timeline.

I also strongly recommend reading Amazon's official Leadership Principles in English. The behavioral descriptions beneath each principle are effectively the grading rubric. For example, Ownership includes the mindset of never saying "that's not my job," and many follow-up questions naturally evolve from that expectation.

The Other Interview Rounds

The first two rounds were relatively standard.

Round 1: LeetCode 994 — Rotting Oranges (multi-source BFS).
Round 2: Course Schedule (topological sorting) followed by a harder Trie-based Search Suggestions System variant that required returning the lexicographically smallest three suggestions for every prefix.

Time was tight during the Trie implementation, so I continuously explained my data structure choices while coding. All test cases passed in the end.

Final Thoughts

If I had to summarize Amazon's 2026 interview process in one sentence, it would be this: Grinding LeetCode only prepares you for half of the interview.

Coding skills are still essential, but interviews increasingly evaluate three additional abilities:

Real experience working with AI development tools.
The ability to withstand deep Leadership Principle questioning.
Your recovery process when you become stuck during a live interview.

The two most difficult moments in my interview—the elevator scheduling problem and the five-level LP interrogation—were manageable only because I had already experienced similar situations during mock interviews on InterviewShow. Their Amazon-focused mock interviews, especially the Leadership Principle coaching and realistic Bar Raiser follow-up sessions, were the parts I found most valuable.

Everyone prepares differently, but I strongly believe in one principle: Get challenged in a realistic mock interview before facing the real one.

Optiver 2027 OA Questions Sharing: Complete Breakdown of the New Quant Assessment

interviewshow-cs — Wed, 08 Jul 2026 08:09:07 +0000

I recently helped several mentees complete the latest Optiver 2027 Online Assessment, and compared with last year's question pool, the Quant OA has changed significantly. Three brand-new assessment modules have been introduced, the overall workload has increased, and the difficulty is noticeably higher.

Most guides currently available online are based on older versions of the assessment, so this article summarizes what candidates are actually seeing in the latest Optiver OA and explains what each new section is really testing.

Overall Changes

This year's Optiver OA is split into two tracks:

Software Engineer (SWE) — still mainly consists of coding problems plus the reaction-speed game.
Quant — significantly redesigned with several completely new assessment modules.

The biggest change isn't that the questions became harder mathematically. Instead, Optiver now focuses much more on evaluating how quickly candidates can make good decisions under uncertainty rather than performing exact calculations.

The Six Quant OA Modules

The first three sections remain familiar to most candidates:

Beat the Odds
NumberLogic
Zap-N

The three newly introduced modules are where most candidates struggle.

New Module #1 — Likelihood-list

15 Questions • 90 Seconds Each

Each problem presents historical observations and asks you to predict or rank future probabilities based on the available data.

For example, you may see several previous outcomes and need to determine which future event is most likely, ranking multiple possibilities from highest probability to lowest.

These questions rarely have one exact numerical answer. Instead, they're designed to evaluate whether you can quickly recognize statistical patterns from limited samples.

Suppose previous values are:

60 → 70 → 75 → 80 → 85

Rather than calculating complicated models, you simply recognize an upward trend and infer that the next value is more likely to continue increasing than suddenly return to 60.

This section mainly evaluates:

Statistical intuition
Pattern recognition
Fast probabilistic reasoning
Decision-making with incomplete information

Preparation Tips

Practice identifying distributions from small samples.
Avoid overthinking every scenario.
Focus on recognizing dominant trends instead of searching for perfect mathematical proofs.

New Module #2 — Intervals

18 Questions • 60 Seconds Each

This module tests estimation rather than calculation.

Each question asks you to provide a lower bound and upper bound for an unknown quantity, probability, or expected value.

Typical examples include:

Estimating probabilities from large visual grids
Sampling without replacement
Expected values
Approximate ratios

One representative question asks:

A large grid contains squares, circles, and triangles. Two shapes are randomly selected without replacement. Estimate the probability of drawing exactly one square and one circle.

Since manually counting over one hundred objects isn't realistic within one minute, successful candidates estimate quantities visually, calculate an approximate probability mentally, and submit a reasonable interval.

Another simpler example:

Two people each roll one fair six-sided die. What is the probability that your friend's roll is higher than yours?

There are 36 equally likely outcomes, with 15 favorable cases.

15 ÷ 36 ≈ 42%.

Instead of entering exactly 41.67%, you might confidently submit:

Lower Bound: 40
Upper Bound: 43

The emphasis is on being approximately correct—not perfectly precise.

Preparation Tips

Practice mental estimation every day.
Improve arithmetic speed.
Learn to judge the correct order of magnitude quickly.
Accept "good enough" estimates rather than chasing exact answers.

New Module #3 — Orderbooks

20 Questions • 8 Minutes Total

This is arguably the most interesting addition because it closely resembles actual trading work at Optiver.

Each question presents an order book containing bids and asks. Your objective is to identify profitable trades or inventory-clearing opportunities as quickly as possible.

With only about 24 seconds per question, candidates don't have time for deep analysis.

The key skill is immediately spotting situations such as:

Crossed markets
Obvious arbitrage opportunities
Safe inventory reductions
Profitable execution sequences

This section primarily evaluates:

Market intuition
Reaction speed
Understanding of bid/ask mechanics
Basic trading logic

Preparation Tips

Understand how order books work.
Learn bid, ask, spread, and market making fundamentals.
Train yourself to scan for obvious pricing opportunities instead of overanalyzing every card.

Question Volume and Passing Strategy

These three new modules alone contain:

Likelihood-list: 15 questions
Intervals: 18 questions
Orderbooks: 20 questions

That's a total of 53 new questions, all completed under significant time pressure.

Most candidates report having virtually no time to review answers. Success depends on making strong first-pass decisions.

Although Optiver does not publish official scoring criteria, candidate feedback suggests that the assessment rewards reasonable judgment rather than perfect precision.

A sensible probability interval, a safe arbitrage decision, or a statistically reasonable prediction is often more valuable than spending extra time searching for an exact solution.

How the 2027 OA Differs from Previous Years

Earlier versions of the Quant OA rewarded extensive practice with known question patterns.

The 2027 assessment shifts toward evaluating practical thinking.

Instead of asking whether you've memorized certain question types, Optiver now wants to know whether you can make sound decisions when:

information is incomplete,
time is extremely limited, and
exact answers are impossible.

Viewed from this perspective:

Likelihood-list measures decision-making under uncertainty.
Intervals measures estimation ability.
Orderbooks measures market intuition.

Understanding these underlying skills is far more valuable than memorizing individual questions.

Preparation Advice

Focus on mastering the reasoning behind each module instead of blindly grinding question banks.
Build strong statistical intuition for Likelihood-list.
Practice mental math and estimation for Intervals.
Learn order book mechanics and market making concepts before attempting Orderbooks.
Simulate the real time pressure during practice sessions.

Final Thoughts

The latest Optiver Quant OA is not necessarily more mathematically difficult, but it is considerably more demanding in terms of speed, judgment, and decision-making under uncertainty.

The assessment closely mirrors the mindset required in quantitative trading: making fast, reliable decisions without having perfect information.

If you prepare with that objective in mind rather than simply memorizing question types, you'll be much better positioned for success.