DEV Community: interview-aid-Etesis Elay

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

interview-aid-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-aid-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

HubSpot NG Backend Software Engineer Interview Experience 2026 | OA, System Design & Offer Timeline

interview-aid-Etesis Elay — Fri, 12 Jun 2026 07:53:23 +0000

I recently received a verbal offer for the HubSpot New Grad Backend Software Engineer position and wanted to share a detailed breakdown of the entire interview process. When I was preparing, I found surprisingly few recent reports covering HubSpot's newer interview format, especially for backend candidates. Hopefully this recap helps anyone preparing for HubSpot, Atlassian, Canva, Stripe, and other engineering-focused companies that value practical software development skills beyond pure algorithm practice.

Overall, HubSpot's interview process felt very different from many traditional Big Tech interviews. While coding ability is certainly important, the company places significant emphasis on engineering judgment, system design fundamentals, communication skills, and the ability to discuss real-world projects. If your preparation strategy consists entirely of grinding LeetCode, some portions of the process may feel surprisingly challenging.

Application Timeline

My interview process moved fairly quickly compared to many large technology companies:

Applied online
Received Online Assessment the next day
Recruiter reached out approximately two weeks later
Formal interviews scheduled about two weeks after recruiter screening
Coding and System Design interviews completed on the same day
Verbal offer received roughly ten days later

From application submission to verbal offer, the entire process took around six weeks. Compared with companies whose hiring processes can stretch over several months, HubSpot's process felt efficient, organized, and candidate-friendly.

Online Assessment (OA)

Based on conversations with other candidates and reports online, HubSpot appears to use two different Online Assessment formats.

Version 1: Billing System Problem

This is the assessment format that appears most frequently in older interview reports.

The problem revolves around a resource billing system where users consume resources across multiple time intervals, and candidates must accurately calculate final charges. Beneath the business context, the core challenge involves handling overlapping intervals and determining peak concurrent resource usage.

Candidates familiar with the following concepts will likely recognize the underlying pattern:

Sweep Line Algorithms
Event Processing
Interval Merging
Concurrency Counting
Timeline Simulation

Although the business scenario initially appears complicated, the algorithmic concepts become relatively straightforward once the problem is modeled correctly.

Version 2: CodeSignal In-Memory Database Assessment

This was the version I personally received.

The entire assessment focused on building an in-memory database system and was divided into four progressively more complex levels. Unlike many traditional coding assessments, the emphasis was placed on software engineering fundamentals and extensible architecture rather than advanced algorithms.

Level 1

The first level required implementing basic storage functionality and establishing the foundation of the database system.

The requirements were relatively straightforward, but this stage was more important than it initially appeared because every subsequent level built directly upon the original implementation. Candidates who rushed through the first stage without considering extensibility often encountered difficulties later.

Level 2

The second level introduced additional database operations:

Insert
Update
Delete
Query

The technical difficulty remained low, but code organization and maintainability became increasingly important.

Level 3

The third level introduced more production-oriented requirements, including:

TTL (Time-To-Live)
Expiration Handling
Time-Based Queries
Conditional Retrieval

This stage tested whether the original design could accommodate additional functionality without requiring major refactoring.

Level 4

The final level expanded the system further with more business logic and advanced operations.

Candidates who built flexible architectures early could typically complete the final level with a few additional methods and minor modifications. Those who implemented rigid solutions often found themselves rewriting large portions of their code.

What makes the HubSpot OA particularly interesting is that the challenge is not algorithmic complexity. Instead, it evaluates your ability to design maintainable software and think about abstraction, extensibility, data modeling, and clean architecture from the beginning.

Recruiter Screening

The recruiter conversation was primarily informational and covered topics such as:

Graduation timeline
Work authorization status
Preferred office location
Interest in HubSpot
Career goals
Overview of the interview process

There were no technical questions during this stage. The recruiter was mainly assessing overall fit and ensuring that both sides were aligned on logistics and expectations.

Behavioral Interview

One thing that surprised me was how much emphasis HubSpot places on behavioral discussions.

The questions themselves were fairly standard, but interviewers frequently asked multiple layers of follow-up questions. Superficial answers generally did not go very far.

Most Challenging Project

A significant portion of the conversation focused on project experience:

Most technically challenging project
Largest engineering obstacle
Decision-making process
Trade-offs considered
Project outcome
Lessons learned

I strongly recommend preparing at least one project that you can discuss comfortably for ten to fifteen minutes. Interviewers seemed much more interested in depth than breadth.

AI and Machine Learning Experience

This topic generated significantly more discussion than I expected.

The interviewer asked detailed questions regarding:

AI tools used
Project integration
Architecture decisions
Evaluation methodology
Business impact
Technical trade-offs

Candidates should be prepared to discuss AI-related work beyond simply mentioning tools such as ChatGPT or GitHub Copilot.

A strong answer should include:

Business problem
Technical solution
Implementation details
Evaluation metrics
Final outcome

Future Team Expectations

One question that initially sounded simple turned out to be more meaningful than expected:

What are you looking for in your future team?

While it initially felt like a culture-fit question, the follow-up discussion suggested they were evaluating long-term alignment, growth expectations, and team compatibility.

System Design Interview

The System Design and Coding interviews were scheduled on the same day. Each session lasted approximately one hour, making the overall experience fairly intense.

One lesson I learned during preparation is that requirement gathering should be efficient. Many candidates spend too much time repeatedly clarifying requirements. While asking questions is important, excessive clarification can become costly during a limited interview window.

My recommendation:

Confirm primary requirements
Clarify scale assumptions
Identify key user workflows
Move quickly into architecture design

Topics that commonly arise include:

API Design
Service Architecture
Database Selection
Caching Strategies
Horizontal Scaling
Data Consistency
Failure Handling
Bottleneck Analysis
Trade-Off Discussions

The interviewer seemed most interested in architectural reasoning and decision-making rather than arriving at a perfect final design. Whenever presenting a design choice, be prepared to explain why you selected one approach over another.

Coding Interview

The coding round was moderate in difficulty.

The problems felt much closer to practical software engineering tasks than traditional competitive programming questions.

Interviewers evaluated far more than whether the solution eventually passed all test cases. Areas of focus included:

Communication
Problem-Solving Approach
Code Readability
Edge-Case Handling
Testing Strategy
Iterative Improvement

One thing I noticed was that interviewers appreciated candidates who continuously explained their reasoning while coding. Even when I was uncertain about implementation details, discussing my thought process helped keep the conversation collaborative.

HubSpot appears to value how engineers work through problems, not simply whether they can instantly produce the optimal solution.

Overall Impression

If I had to summarize HubSpot's interview process in a few key points:

Moderate algorithm difficulty
Strong focus on engineering fundamentals
Significant System Design emphasis
Growing interest in AI-related project experience
Communication skills matter a lot
Practical software development mindset is highly valued

Candidates who prepare exclusively through LeetCode may find themselves underprepared for behavioral and system design discussions. On the other hand, candidates with internship experience, backend projects, or real-world engineering exposure will likely find many aspects of the process approachable.

Compared to companies that focus heavily on algorithmic trick questions, HubSpot's interview process felt much closer to evaluating actual software engineers.

Interview Preparation Resources

Many candidates preparing for HubSpot, Stripe, Canva, Atlassian, Datadog, Snowflake, and similar companies discover that the biggest challenge is not necessarily solving interview questions themselves. The real challenge is performing consistently under pressure while balancing coding, behavioral discussions, and system design interviews.

For candidates looking for additional preparation support, check out:

Interview Aid VO & Interview Preparation Services

Their team provides support for Online Assessments, technical interviews, mock interviews, System Design preparation, and Virtual Onsite coaching. Many candidates use these resources to better understand company-specific interview expectations and improve confidence before major interview loops.

Bloomberg 26 NG SWE Interview Experience | From Last Year's Near Miss to Offer

interview-aid-Etesis Elay — Thu, 11 Jun 2026 15:24:03 +0000

Last year, I interviewed for the BBG Intern position and made it to the final round, but the experience was tough and I ultimately didn't get the offer. This year, applying for the 26 NG SWE role was much smoother. The entire process was on-campus and very fast-paced.

The interview style this year was completely different from last year. Previously, coding rounds were just two pure LeetCode problems. This year, each round had only one problem, but each included OOP, design, and algorithm components, with multiple follow-ups. Overall, the difficulty felt higher. The EM round, however, was still great—mostly discussing my resume and projects. Bloomberg’s EM interviews have a really positive vibe.

Bloomberg Timeline

10/4: Applied for Bloomberg 26 NG SWE
12/24: Phone interview invitation
1/6: Phone interview
1/20: VO invitation
1/30: VO
2/5: EM interview
3/3: Offer call

Coding 1: Crash Process Root Cause

Given a set of crashed processes (including each process's children), find the process ID with no crashed parent (the root cause of the crash propagation).

Solution Approach

Put all crashed process IDs into crashedSet and all children of crashed processes into childSet. The root cause is then crashedSet - childSet. Simple idea, but careful handling of input formats and empty sets is necessary.

Coding 2: Wordle Scoring Implementation

Implement Wordle scoring: given a target word and a guess word, return a score string like "-+-*–".

Correct letter and position → "*"
Letter exists but wrong position → "+"
Letter not in target → "-"

Solution Approach

Use a two-pass approach with frequency counting. First pass for exact matches ("*") and reduce the frequency count. Second pass for partial matches ("+"), ensuring repeated letters are handled correctly with the frequency table.

HR & EM Interview

The HR round mainly covers self-introduction, knowledge about BBG, and typical behavioral questions. It's best to answer from the perspective of company values. EM rounds focus more on technical depth and business impact. Highlight how your projects contributed to the business.

Tips:

Communicate clearly; avoid unnecessary repetition.
Familiarize yourself with BBG culture and the job description in advance.
When discussing projects in the EM round, emphasize technical decisions and business impact.

Final Thoughts

This year, Bloomberg's interview pace and focus points differed significantly from last year. It's important to practice design-oriented coding questions in preparation. During my preparation, I realized that having access to the right information is crucial. Through a friend’s recommendation, I learned about Interview Aid, which provided guidance on Bloomberg-style questions and interview prep, helping me prepare with confidence.

If you are preparing for Bloomberg 26 NG or other fintech company interviews, you can check out Interview Aid. They specialize in OA and VO support for North American tech roles. Wishing everyone success in landing your dream offer!

DoorDash Data Scientist Interview Experience | Technical Screen, Case Study & Onsite Breakdown

interview-aid-Etesis Elay — Wed, 10 Jun 2026 13:25:47 +0000

I recently completed the full interview process for a Data Scientist role at DoorDash and wanted to share my experience while the details are still fresh.

I applied through LinkedIn, although DoorDash recruiters are also known for actively reaching out to candidates whose backgrounds match their openings. Compared to many other tech companies, the hiring process moves at a fairly reasonable pace. After passing the resume screen, candidates are often contacted within one to two weeks for the first interview, and the entire process typically takes around three to five weeks.

HR Screen

The first round was a recruiter conversation focused entirely on behavioral questions and background fit.

There were no SQL questions, coding exercises, or analytics cases. Instead, the recruiter spent most of the time discussing my previous data-related projects, my interest in DoorDash, and my understanding of a three-sided marketplace involving customers, merchants, and Dashers.

The recruiter also walked through the overall interview process and expectations for each stage.

This round did not feel particularly selective from a technical perspective, but candidates who know little about DoorDash's business model or marketplace dynamics may struggle. Having a basic understanding of the company's products, growth strategy, and operational challenges can make a noticeable difference.

Technical Screen

The technical screen was conducted by a Data Scientist and consisted of two major components.

SQL Portion

The SQL section focused heavily on real-world business scenarios rather than textbook exercises.

Common topics included:

Window functions
Multi-table joins
Common Table Expressions (CTEs)
Aggregations and KPI calculations
User behavior analysis

Most questions were framed around marketplace operations, delivery performance, merchant metrics, or customer engagement.

Product Analytics Case

The second half focused on analytical thinking.

Typical discussion areas included:

Metric definition
Root cause analysis
Experimentation fundamentals
Business trade-off evaluation

For example, I was asked how I would evaluate the impact of an increase in delivery time on overall marketplace performance and customer experience.

The interviewer cared much more about structured thinking and communication than arriving at a single "correct" answer.

Take-Home Case Study

After passing the technical screen, I received a take-home assignment based on marketplace data.

The dataset contained information related to orders, merchants, and delivery partners.

The project required three major components:

SQL analysis
Data visualization
Business recommendations

The most important skills evaluated were:

Data cleaning and exploratory analysis
KPI selection and definition
Ability to connect data insights to business decisions

This was not a competition to build the most sophisticated model. Instead, the emphasis was on producing practical recommendations that stakeholders could actually implement.

After submission, I participated in a walkthrough session where interviewers spent approximately 20 minutes discussing my methodology, assumptions, and conclusions.

Expect follow-up questions challenging every major decision you make.

Virtual Onsite

The onsite consisted of multiple rounds with Data Scientists, hiring managers, Product Managers, and senior stakeholders.

Each round lasted approximately 45–60 minutes.

Product Analytics Case

This was the most important component of the onsite.

Cases typically revolved around core DoorDash metrics such as:

Delivery duration
Order volume
Average order value
Merchant retention
Customer engagement

Interviewers expected a structured framework covering:

Metric definition
Hypothesis generation
Segmentation
Root cause analysis
Business recommendations

SQL Coding

The SQL questions were slightly more challenging than those in the technical screen.

Topics included:

Complex joins
Nested aggregations
Funnel analysis
Retention metrics
Marketplace performance measurement

Strong SQL fundamentals were essential.

Machine Learning Concepts

The level of ML discussion depended on the specific role.

Core Data Scientist positions typically involved deeper conversations around:

Model evaluation
Bias-variance trade-offs
Feature engineering
Experimentation

Analytics-focused positions tended to place greater emphasis on causal inference and A/B testing.

Behavioral Interview

Behavioral rounds focused heavily on:

Cross-functional collaboration
Ambiguous problem solving
Stakeholder management
Project failures and lessons learned
Ownership and influence

Having several STAR-format stories prepared in advance was extremely helpful.

Case Questions I Encountered

Case 1

After updating the estimated delivery time displayed in the app, overall order volume increased by 3%, but average order value dropped significantly.
How would you analyze this change?

Case 2

Delivery duration has increased across multiple cities over the past few weeks.
How would you decompose potential causes, identify key metrics, and conduct your analysis?

Case 3

Design an A/B test to determine whether offering a 20% discount coupon can generate net profit growth for merchants.
What experiment design would you use, and which evaluation metrics would you track?

Overall Thoughts

DoorDash's Data Scientist interview process is significantly more business-focused than many candidates expect.

SQL skills are important, but success depends even more on marketplace intuition, metric design, experimentation knowledge, and the ability to translate analytical findings into business decisions.

For candidates preparing for DoorDash DS interviews, I would recommend focusing on:

Advanced SQL
Product analytics frameworks
Marketplace business models
A/B testing and experimentation
Structured communication

One thing that helped me prepare efficiently was practicing cases and mock interviews with Interview Aid. Their real-time guidance and interview-focused preparation helped me improve both my analytical frameworks and communication under pressure, especially for open-ended product cases and stakeholder-style discussions.

If you're targeting DoorDash, Uber, Instacart, Airbnb, or other marketplace-focused companies, investing time in product analytics and business case preparation will likely provide a much higher return than spending all your time on coding alone.

Optiver SDE Interview Experience | 2026 Full Recap (Offer Received)

interview-aid-Etesis Elay — Tue, 09 Jun 2026 14:57:21 +0000

I recently received an SDE offer from Optiver. The interview process was fast-paced, in-depth, and fair. Below is a detailed breakdown of my experience from OA to Onsite for those preparing for Optiver, Jane Street, Citadel, or other quant trading companies.

OA (Online Assessment)

Optiver’s OA typically consists of several sections and lasts 2–3 hours, including Coding + MCQ + Logic/Probability questions.

Coding Section (about 90 minutes)

2–3 questions, medium to hard difficulty.
High-frequency topics: OOP design, memory calculations, order allocation simulation, dynamic programming, etc.
Strong emphasis on code quality, engineering practices, and time/space complexity analysis.

MCQ + Logic Questions

Computer fundamentals (OS, Network, DSA)
Probability and logical reasoning questions (Optiver specialty)

Takeaway: OA is not something you can pass by grinding pure LeetCode. It’s recommended to familiarize yourself with quant-related scenarios and OOP design in advance.

Onsite Technical Interviews (3 Rounds, 45–60 Minutes Each)

Optiver’s technical interviews are very practical, emphasizing engineering communication and quantitative thinking. Interviewers are generally professional and friendly, discussing problems like colleagues.

Round 1: Technical Interview (45 Minutes)

This round focuses on deep-diving into your project experience and engineering capabilities.

Common Questions:

Walk me through your most technical project (from architecture to implementation)
What was the biggest technical challenge in your project and how did you solve it?
What performance optimizations or trade-off decisions have you made?

Interviewers ask very detailed follow-ups, such as specific data structures used, why you chose them, and what would happen if the data volume increased 10x. Suggestion: Prepare 2–3 in-depth project stories in advance, covering background, challenges, solutions, results, and lessons learned.

Round 2: Trading Systems & Quantitative (60 Minutes)

This is the most intense round, focusing on trading systems and quantitative reasoning.

Common Topics:

Design a simplified Order Book system supporting limit orders, matching, and cancellation.
Real-time data processing and low-latency optimization.
Probability-related deduction questions (e.g., expected value calculation, risk assessment).

Interviewers dive deep into the feasibility of your solutions in high-frequency trading scenarios, such as latency, memory usage, and concurrency safety. This round heavily tests your practical understanding of quantitative systems — pure theory is easily exposed.

Round 3: System Design / Behavioral

This round combines system design and behavioral questions.

Common Topics:

Design a high-concurrency real-time market data feed system.
How to handle large-scale order matching and risk control.
Behavioral: Describe a time you collaborated across teams to solve a complex problem, or how you handled a production incident.

Interviewers like to hear concrete numbers and trade-off analysis, for example, “How many QPS can this solution handle? What if traffic suddenly increases 5x?”

Preparation Tips

Programming Language: C++ / Java / Python are all acceptable — use the one you are most comfortable with.
Key Areas: OOP design, memory management, low-latency systems, probability and logic questions.
Communication: Optiver places high value on how you think. Thinking aloud and clearly explaining trade-offs throughout can significantly boost your score.
Mindset: Interviewers are generally professional and friendly, more like solving problems together with colleagues rather than pure testing.

Final Thoughts

Optiver’s interview process is challenging but focuses heavily on real engineering ability and thinking style. As long as you prepare solid project stories and system design scenarios in advance, the overall experience is quite positive.

Later, through a friend’s recommendation, I discovered Interview Aid. Their latest Optiver interview guides and targeted mock sessions helped me better handle quant scenario questions and deep follow-ups.

If you’re also preparing for Optiver or other quant trading companies, I recommend practicing more engineering design questions and probability logic problems.

Best of luck landing your offer!

Square Point OA True Questions Sharing | 2026 Latest (Passed)

interview-aid-Etesis Elay — Mon, 08 Jun 2026 11:50:35 +0000

I recently completed Square Point's Online Assessment and passed it smoothly. Below is a detailed sharing of the real questions and solution ideas for those preparing for Square Point, Jane Street, SIG, or other quant/fintech companies.

OA Overview

Platform: HackerRank
Duration: 75 minutes
Questions: 2 Coding Problems
Characteristics: Business-oriented simulation and data processing, focusing on heap usage and state maintenance. Time is relatively sufficient if managed well.

Question 1: Maximize Sales Revenue

Problem Description

Given an array of product quantities, in each step you can sell the product with the current highest quantity. After selling, reduce its quantity by 1 (if still greater than 0, it remains available for future sales). Calculate the maximum total revenue, where the revenue for each sale equals the quantity sold at that moment.

Solution Idea

Put all quantities into a max-heap (Priority Queue) to always retrieve the largest quantity.
Each time, take the largest quantity and add it to the total revenue.
Decrease the quantity by 1 and push it back into the heap if it is still greater than 0.
Repeat until the heap becomes empty.

Core Concept: Greedy + Max Heap. Always sell the currently most abundant product.

Question 2: Message Delivery with Cooldown

Problem Description

Given a sequence of messages with timestamps and a cooldown time k. For the same message, if the interval between two deliveries is less than k, reject the current delivery. Otherwise, allow it. Update the last delivery timestamp regardless of whether the message is accepted or rejected.

Solution Idea

Use a HashMap to record the last delivery timestamp for each message.
For each incoming message:

If the message has not appeared before, allow delivery.
If current_time - last_timestamp ≥ k, allow delivery.
Otherwise, reject delivery.

Update the last timestamp in both accepted and rejected cases.

Core Concept: HashMap state maintenance + timestamp difference checking.

Preparation Tips

Square Point OA heavily favors business simulation problems.
Practice Heap (Priority Queue) and HashMap state-maintenance questions extensively.
Write clean code with meaningful variable names.
Pay attention to edge cases such as empty arrays, k = 0, duplicate messages, and large inputs.
Recommended time allocation:

Question 1: 20–25 minutes
Question 2: 30–35 minutes
Remaining time: debugging and verification

Final Thoughts

This Square Point OA reinforced an important lesson: clear logical thinking and effective time management are often more important than solving extremely difficult algorithmic problems.

Later, through a friend's recommendation, I discovered Interview Aid. Their latest question bank and solution discussions helped me quickly understand high-frequency topics and significantly improve my preparation efficiency.

If you're also preparing for Square Point, Jane Street, SIG, or other quantitative trading companies, I highly recommend spending extra time on heap-based simulations and state-management problems.

Best of luck with your preparation and hope you land your dream offer!

TikTok SDE Interview: Every Round Is Elimination — Here's What Actually Gets You Through

interview-aid-Etesis Elay — Fri, 05 Jun 2026 04:07:04 +0000

TikTok Interview Process Overview

TikTok's interview process moves very quickly. From application to offer, the entire timeline is often completed within a month. Most candidates go through four to five rounds, including two coding interviews, one system design interview, and one behavioral interview. Depending on the team, there may also be an additional architecture-focused or domain-specific round.

Each round is elimination-based. If a candidate does not pass a round, the process typically ends immediately rather than continuing through the remaining interviews.

Application → OA or Recruiter Screen → Technical Phone Interview → Virtual Onsite (4–5 Rounds) → Offer

All technical interviews are usually conducted on CoderPad or HackerRank and last around 45 minutes each. TikTok's hiring teams often prefer to schedule all onsite rounds on the same day. While candidates can request interviews to be split across multiple days, doing so may slow down the process and occasionally reduce competitiveness if other candidates complete the process more quickly.

Technical Phone Interview

The first technical round typically consists of a medium-level coding problem. According to candidate reports on Glassdoor, common questions include:

Longest Substring Without Repeating Characters (LeetCode 3)
Merge K Sorted Lists (LeetCode 23 Variant)

Candidates are expected to solve medium-level problems efficiently. Strong performers often complete implementation within 10–15 minutes, leaving sufficient time to discuss complexity analysis, edge cases, and alternative approaches.

Onsite Coding Rounds

There are usually two coding rounds, ranging from medium to medium-hard difficulty. Common topics include:

Dynamic Programming
Graphs (BFS / DFS)
String Processing
Linked Lists
Binary Trees
Hash Tables

One frequently reported question involves selecting videos with maximum total weight under a time constraint, which is essentially a classic 0/1 Knapsack problem.

Several patterns consistently appear during TikTok coding interviews:

Think out loud while coding.
Explain time and space complexity proactively.
Prepare for follow-up questions involving edge cases.
Consider empty inputs, single-element inputs, and unusual boundary conditions.

System Design Round

TikTok's system design interviews are highly aligned with real business scenarios. One of the most frequently reported questions is:

Design TikTok's Video Recommendation System.

A typical high-level architecture includes:

User behavior events → Kafka → Real-time feature updates
Offline training pipelines → Recommendation models
User requests → API Gateway → Retrieval Layer → Ranking Layer → Recommendation Results

Key discussion points often include:

ANN retrieval using vector databases such as Faiss or Milvus
Ranking models based on CTR, watch time, engagement, and freshness signals
Real-time feature updates using Kafka and Flink
Cold-start strategies for new users and new content

Common follow-up questions:

How would you reduce filter bubbles and recommendation homogeneity?
How would you debug recommendation quality issues?
How would you measure recommendation performance?

Behavioral Round

TikTok places significant emphasis on ownership, execution speed, and cross-functional collaboration.

Common behavioral questions include:

Tell me about a change you drove outside your official responsibilities.
Describe a project delivered under a tight deadline and how you prioritized trade-offs.
Tell me about a disagreement with teammates and how you resolved it.

The STAR framework is highly recommended. Interviewers generally expect measurable outcomes and concrete impact metrics.

Preparation Tips

Focus on Graph, DP, String, Tree, and Linked List problems.
Aim to solve medium-level LeetCode problems consistently within 15 minutes.
Prepare system design topics such as recommendation systems, video storage and distribution, and real-time data processing pipelines.
Have 5–6 behavioral stories ready covering ownership, cross-functional collaboration, and fast execution.

About Interview Aid

Before my interviews, I worked with Interview Aid for real-time virtual onsite support.

During the System Design round, I initially struggled with the cold-start strategy discussion for the recommendation system. Their mentors helped me quickly organize a practical solution framework. In one of the coding rounds involving a knapsack-style DP variation, they also provided guidance on identifying the appropriate state transition approach.

Interview Aid has been deeply involved in the North American technology industry for years and has helped thousands of candidates secure positions at Fortune 500 companies and leading tech firms. Their mentors include experienced engineers and interviewers from top-tier companies who are highly familiar with TikTok's interview style and evaluation criteria.

Services include OA assistance, OA support, VO support, mock interviews, and interview coaching.

Interview Aid · Service Details

Anthropic SDE Interview Recap | 2026 Hiring Process & Real Experience (Offer Received)

interview-aid-Etesis Elay — Thu, 04 Jun 2026 07:08:46 +0000

I recently completed the Anthropic Software Engineer interview process and was fortunate enough to receive an offer. My biggest takeaway is that Anthropic's interview style is very different from that of traditional Big Tech companies. Instead of focusing heavily on algorithm memorization, the process places significant emphasis on LLM infrastructure, AI safety, and practical engineering decisions. Candidates are expected to demonstrate strong system thinking and an understanding of real-world large-scale AI systems.

Below is a complete breakdown of the OA and four interview rounds. Hopefully it helps anyone preparing for Anthropic or other frontier AI companies.

Online Assessment (OA)

Duration: 90 minutes
Number of Questions: 4
Difficulty: Medium to Hard

Main Topics:

Efficient token batching
KV cache optimization
Request scheduling
LLM inference engineering concepts

If you're not familiar with the inference pipeline of large language models, especially the Prefill and Decode stages, the assessment can be challenging.

Round 1: Technical Phone Screen (45 Minutes)

Format: Coding + Technical Discussion

Coding Task:

Implement a simplified token batching system capable of handling variable-length requests and scheduling them efficiently.

Discussion Topics:

Major performance bottlenecks in LLM inference (Memory Bound vs Compute Bound)
Basic KV cache optimization strategies
Dynamic request merging for throughput improvement

Round 2: Coding & Optimization (60 Minutes)

Difficulty: Medium to Hard
Scenario: Real-world problems inspired by Claude inference services

Focus Areas:

Concurrency handling
Memory management
Multiple optimization approaches
Batching strategy trade-offs

The interviewer spent considerable time exploring trade-offs between different solutions, including:

Latency vs Throughput
GPU Memory Usage vs Flexibility
Engineering Complexity vs Performance Gains

Round 3: System Design (60 Minutes)

Design Question:

Design a scalable, low-latency LLM inference service for Claude.

Key Areas Expected:

Distributed inference architecture
Model Parallelism and Pipeline Parallelism
KV cache reuse strategies
PagedAttention and memory optimization techniques
Fault tolerance mechanisms
Safety isolation layers

My design framework followed this flow:

Load Balancer → Continuous Batching → Request Queue Design → Model Hot Loading → Safety Isolation Layer

The interviewer went particularly deep into fault recovery scenarios and safety-related protections. A standard distributed system design discussion alone would not have been sufficient.

Round 4: Culture & Technical Values (45 Minutes)

Main Themes:

AI Safety
Responsible Deployment
Alignment

Common Questions:

What do you believe is the biggest risk facing AI systems today?
How would you balance rapid iteration with safety requirements?
Tell me about a time you implemented engineering solutions to reduce operational risk.

This round was not a formality. Interviewers frequently connected these discussions back to your previous engineering decisions to evaluate whether safety-first thinking genuinely influences your design process.

Preparation Tips That Actually Help

1. Read Anthropic Research and Blog Posts

Constitutional AI
Red Teaming
Alignment-related publications

For additional reading, I highly recommend:

GPT-4 System Card
Claude 3 Technical Report
AI Safety and Alignment papers

2. Master Core LLM Infrastructure Concepts

KV Cache fundamentals
Memory consumption and cache reuse limitations
Continuous Batching / In-Flight Batching
PagedAttention and memory management systems

3. Integrate Safety into System Design

Don't focus exclusively on scalability and latency.

You should proactively discuss:

Output filtering
Request isolation
Rate limiting
Jailbreak prevention
Abuse detection

4. Prepare Authentic Culture-Fit Stories

Strong examples can include:

A postmortem from a production incident
A time you rejected a risky feature request
A difficult trade-off between speed and reliability

The goal is to demonstrate responsibility, long-term thinking, and sound engineering judgment.

Final Thoughts

Anthropic is not the kind of company where simply grinding LeetCode will guarantee success.

The interview process is heavily focused on whether you truly understand the engineering bottlenecks of large-scale AI systems and how you make decisions when safety and performance goals conflict.

Later in my preparation process, I was introduced to Interview Aid. Their latest interview experiences and mock interview sessions were particularly helpful for practicing deep follow-up questions and AI safety scenarios, which appear frequently throughout the process.

If you're preparing for Anthropic, OpenAI, xAI, or other frontier AI companies, I strongly recommend building real-world experience and knowledge around LLM infrastructure, system design, and safety engineering.

Good luck to everyone pursuing their next opportunity.

(2026 Anthropic SDE interview experience. Personal notes and observations. Individual experiences may vary.)

OpenAI Interview Full Process Analysis (2026 Latest)

interview-aid-Etesis Elay — Wed, 03 Jun 2026 03:31:29 +0000

Recently, I connected with a candidate who successfully received an offer from OpenAI. During the system design question, he didn’t even provide the optimal solution, yet he earned a high score for proposing an unconventional but highly creative approach. He summarized:

“In OpenAI interviews, what I learned was not how to code faster, but how to think more deeply about problems.”

OpenAI is truly looking for thought leaders, not just excellent engineers.

Complete Interview Process

Based on official OpenAI information and feedback from multiple successful candidates, the OpenAI interview typically consists of 5-6 stages:

Resume Screening (within 1 week) Focus:
- Real contributions in AI/ML (papers, open-source projects, deployed products)
- Background alignment with the role
- Whether you truly understand OpenAI’s current work directions
Recruiter Call (30 minutes) Core assessment: Mission Alignment Common questions:
- Why do you want to join OpenAI?
- Which OpenAI project or work are you most interested in?
- Why not choose Google / Anthropic?
Key lesson: Simply saying “OpenAI is awesome” will likely be rejected. You must specifically mention real work like Constitutional AI, RLHF, Scaling Law, AI Safety, etc.
Technical Screening (Coding Test) Format: CoderPad Live Coding, HackerRank asynchronous test, or Take-home Project Question features: more practical than LeetCode, e.g.:
- Large-scale log anomaly detection
- Model serving system design (supporting A/B testing, version control, real-time inference)
- Open-ended ML model optimization problems
Grading focus: clarity of thought and communication > perfect code
Virtual Onsite (4-6 rounds, 4-6 hours) The core stage includes:
- Coding (more difficult than screening)
- System Design
- Technical Deep Dive (highest differentiation)
- Behavioral Interview
Typical Technical Deep Dive questions:
- What are the fundamental limitations of the Transformer architecture? How would you overcome them?
- What is your view on the current LLM Scaling Law?
- How would you design a safer AI system?
This stage assesses whether you have genuinely thought about these questions, not just memorized answers.
Hiring Manager Round A two-way evaluation: Are you suitable for the team? Is the team suitable for you?

Top 4 Fatal Mistakes Chinese Candidates Often Make

Only practicing algorithms, ignoring depth of thinking (most common)
Not confident enough in English, unable to clearly express complex ideas
Lack of real understanding of OpenAI’s work
Being too conservative in answers, afraid to propose innovative ideas

3-Month Suggested Plan for Efficient Preparation

Month 1: Deep Learning Read OpenAI core papers and blogs, understand Scaling Law, Alignment, Safety, etc.
Month 2: Skills Training Focus on open-ended questions, system design, and technical English expression.
Month 3: Simulation and Refinement Conduct high-intensity mock interviews and prepare high-quality questions.

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Netflix Interview Experience: Coding Wasn't the Hardest Part — Culture Fit Was

interview-aid-Etesis Elay — Tue, 02 Jun 2026 02:57:30 +0000

The most difficult part of the entire Netflix interview process wasn't coding and it wasn't system design. It was the culture round.

Many candidates spend months grinding LeetCode before their Netflix onsite, only to discover that the Culture Fit interview is far from a formality. Netflix interviewers are not asking whether you're a nice person—they are systematically evaluating whether the decisions you've made throughout your career align with Netflix's values. Once you understand that, your entire preparation strategy changes.

Understanding Netflix's Engineering Culture

Netflix has a publicly available Culture Memo, and interviewers use it as a framework throughout the hiring process.

Some of the most important principles include:

Freedom and Responsibility – Engineers are given enormous autonomy and are expected to own outcomes without waiting for instructions.
Judgment – The ability to make strong decisions even when information is incomplete.
Candor – Being willing to speak honestly, even when disagreeing with managers or challenging existing decisions.
High Performance – Netflix expects exceptional performance and maintains a very high talent density.

These aren't buzzwords that can be memorized the night before. Interviewers will ask for real examples from your past experience to validate whether you've actually demonstrated these behaviors.

The Interview Process

Compared with many large tech companies, Netflix moves surprisingly fast. The entire process often finishes within three to five weeks.

Recruiter Screen (30 min)
↓
Hiring Manager Call (30 min)
↓
Technical Phone Interview (45-60 min)
↓
Virtual Onsite (4-6 rounds)
↓
Hiring Committee Review
↓
Offer

Unlike many companies where the Hiring Manager conversation is mostly procedural, Netflix uses this round to deeply evaluate your background while also introducing the team's mission and technical challenges. By the end of the conversation, you should have a clear understanding of the group's business objectives and engineering direction.

The onsite typically includes:

Two Coding Interviews
One System Design Interview
One Culture Fit Interview
Sometimes an additional Low-Level Design Interview

Technical Phone Interview: Merge Intervals

My technical phone screen consisted of a single problem: Merge Intervals.

The implementation itself was straightforward, but after finishing the solution, the interviewer immediately expanded the discussion into production-scale scenarios.

What if intervals arrive continuously as a real-time stream?
How would you maintain merged intervals efficiently?

I discussed maintaining a sorted data structure and merging overlapping intervals dynamically upon insertion.

Then came the next question:

What if there are billions of intervals and they cannot fit into memory?

That led into external sorting, chunked processing, disk storage, and multi-way merge techniques.

This style of follow-up is very characteristic of Netflix. A standard LeetCode problem quickly evolves into a discussion about real-world scalability and engineering trade-offs.

Onsite Round 1: In-Memory File System

The first onsite coding round required designing an in-memory file system supporting:

mkdir()
ls()
addContentToFile()
readContentFromFile()

I implemented the solution using a Trie-like hierarchy backed by hash maps.

After finishing the core implementation, the interviewer shifted toward concurrency:

"What happens if multiple threads read and write the same file simultaneously?"

The discussion moved into Read-Write Locks, synchronization strategies, consistency guarantees, and concurrent access patterns.

Onsite Round 2: Serialize and Deserialize Binary Tree

The second coding round focused on binary tree serialization and deserialization.

I used BFS traversal and represented null nodes with placeholder markers.

The implementation was completed in roughly twenty-five minutes. The interviewer reviewed edge cases, complexity analysis, and testing considerations before moving on.

Onsite Round 3: System Design — Netflix Video Streaming Platform

This was the deepest technical discussion of the entire interview and lasted approximately seventy-five minutes.

The prompt was straightforward:

Design a video streaming platform capable of serving millions of concurrent users.

My high-level architecture looked like this:

User Request
↓
API Gateway
↓
Authentication Service
↓
Metadata Service
↓
Manifest Retrieval
↓
CDN Delivery
↓
Adaptive Bitrate Streaming

Key Design Topics

CDN Architecture
Content Distribution
Adaptive Bitrate Streaming
Video Encoding Pipelines
Metadata Storage
Caching Strategies
Scalability and Fault Tolerance

The interviewer spent significant time exploring edge cases:

What happens when a blockbuster show launches and millions of users request uncached content simultaneously?
How do you reduce buffering for users on unstable mobile networks?
How should metadata, watch history, and recommendation features be stored?

We discussed CDN pre-warming, request coalescing, adaptive bitrate streaming, client-side buffering, distributed caching, stream processing pipelines, and large-scale storage systems.

This round rewarded preparation. Netflix publishes a large amount of engineering content, and prior familiarity with their infrastructure helped significantly.

Onsite Round 4: Culture Fit

This was the round I initially underestimated.

Looking back, it was probably the most important interview of the entire onsite.

Some questions included:

Tell me about a major technical decision you made with incomplete information.
Have you ever directly disagreed with your manager? What happened?
Why are you leaving your current company?
How would you handle discovering a serious flaw in a senior engineer's code?

Netflix isn't looking for textbook answers.

They're evaluating how you think, how you communicate disagreement, how you manage uncertainty, and whether you consistently demonstrate ownership and judgment.

The Candor principle appears repeatedly throughout this interview. Candidates who avoid conflict or attempt to provide overly polished answers often struggle because interviewers are looking for authentic examples rather than rehearsed stories.

One Thing That Felt Different

One aspect of Netflix interviews stood out compared to every other large technology company I've interviewed with.

Interviewers frequently shared their own opinions after hearing my answers.

Instead of simply saying "okay" and moving on, they would explain trade-offs, discuss how the problem would be approached internally, and challenge assumptions from an engineering perspective.

The result felt less like an interrogation and more like a genuine technical discussion between peers.

About Interview Aid

During my onsite preparation and interview process, I also worked with Interview Aid for real-time interview support.

The system design round became much smoother when discussing CDN pre-warming and request coalescing strategies, and the culture interview preparation helped me understand what Netflix interviewers were actually trying to evaluate behind each behavioral question.

Unlike generic AI-generated interview templates, Interview Aid connects candidates with experienced North American software engineers who understand how top-tier companies assess candidates in real interviews.

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VO Interview Support
Online Assessment Assistance
Resume Optimization
Technical Interview Coaching
Behavioral Interview Preparation

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Capital One OA Experience | Three Real Questions from the Assessment

interview-aid-Etesis Elay — Mon, 01 Jun 2026 13:08:07 +0000

I recently completed the Capital One Online Assessment and wanted to share some of the questions that appeared during the test. One thing worth noting is that the OA is proctored. Candidates are required to keep their camera on, share their screen, and enable their microphone throughout the assessment.

Overall, the questions were not extremely difficult from an algorithmic perspective, but they required careful implementation, strong attention to detail, and good time management under a monitored environment.

Question 1: Count Key Changes

Given an array of keyboard inputs such as:

['w', 'W', 'a', 'A', 's', 'S']

Determine how many times the pressed key changes throughout the sequence. Uppercase and lowercase versions of the same letter should be treated as identical keys.

For example, moving from 'w' to 'W' does not count as a change, while moving from 'w' to 'a' counts as one change.

This problem primarily tests array traversal and edge-case handling.

Question 2: Longest Common Prefix Between Two Arrays

You are given two arrays of positive integers, arr1 and arr2.

Choose one number x from arr1 and one number y from arr2. Compute the length of the longest common prefix shared by the decimal representations of x and y.

Return the maximum possible common prefix length among all valid pairs. If no common prefix exists, return 0.

Example:

x = 12345
y = 12399

Longest Common Prefix Length = 3

Although the problem appears straightforward, brute-force comparisons can become inefficient for larger inputs. Common optimizations include using prefix sets or Trie-based approaches.

Question 3: Maximum Expression Value in a Matrix

You are given a non-empty matrix containing:

Digits (0-9)
Plus signs (+)
Minus signs (-)

Find a valid expression within the matrix and return the maximum value obtainable from any valid expression.

This was the most challenging problem in the assessment.

The solution involves a combination of matrix traversal, DFS/backtracking, expression validation, and expression evaluation while maintaining the global maximum result.

Candidates with experience in grid search, recursive exploration, and expression parsing generally have an advantage on this question.

Final Thoughts

The Capital One OA is not necessarily designed to test the most advanced algorithms. Instead, it focuses heavily on implementation quality, problem-solving efficiency, and handling details under time pressure.

Many candidates struggle not because they lack technical knowledge, but because online assessments require quick execution, accurate debugging, and strong time management skills.

About Interview Aid

Interview Aid was founded in 2014 by a team of Silicon Valley software engineers, research scientists, and ICPC medalists. With more than a decade of North American recruiting experience, the team provides professional interview consulting services, including Online Assessment support, Virtual Onsite preparation, interview strategy coaching, and technical career guidance.

Interview Aid's mentors come from leading technology companies and top computer science programs worldwide. Since its founding, the organization has maintained a transparent and service-first approach, helping students and job seekers navigate competitive hiring processes with greater confidence and efficiency.

For candidates preparing for Capital One, Amazon, Meta, Google, JPMorgan, Goldman Sachs, and other major technology and finance companies, understanding common OA patterns and practicing under realistic time constraints can make a significant difference in overall performance.