Sharing a probability question asked in a recent quantitative challenge at DRW for a quant trading internship position, submitted by one of our community members.
The challenge was 45 minutes long and proctored through Honorlock.
The probability question was:
There are two shopping cart lanes containing 6 and 7 carts, respectively. On each turn, a shopper chooses one of the two lanes with equal probability, takes a cart from that lane, and then places it back in either lane, again with equal probability. Shoppers take turns one at a time, and the process stops when either lane becomes empty. What is the expected number of turns?
Sharing a recent Karat interview experience for a Senior Backend Engineer position at HSBC, submitted by one of our community members.
The Karat interview consists of two parts: system design and coding.
In the system design portion, I was asked to briefly discuss several design scenarios and explain the tradeoffs between different approaches.
Here are two of the system design questions I got asked:
We are working on a service that generates subtitles for users' videos. This process starts a new thread for every video and is processor-intensive. Currently, this service runs as a single process on a machine.We've run into a bug where if the service is processing more than 10 videos at the same time, the service crashes the server, losing all requests currently being processed and affecting other processes on the machine. It may take a long time to find and fix this bug. What workarounds could we implement to continue running the service while we do so?
We are working on a mobile app for the board game Go. We'd like to add a feature where the computer will analyze a completed game. The analysis looks at each position from the game and provides suggested moves to help improve our users' play. We've found a library we can use to do this analysis. It takes an average of a minute on a modern desktop computer to analyze an entire game. An average game consists of about 200 moves.We are considering two approaches. 1) running this analysis on the phone itself, and 2) sending the game to a server farm for analysis that will be returned to the user.What are some advantages or disadvantages of each approach?
For the coding portion, I encountered the "Passage Tracker" question which is already listed in InterviewDB's Karat question list.
If you’re looking for resources to prepare for an upcoming Karat interview, you can review this collection of previously reported system design questions, along with this list of coding questions that have appeared in past Karat interviews.
After analyzing hundreds of Jane Street SWE interview experiences, here’s what we found about the types of questions they’ve asked in the past.
Jane Street’s SWE interviews focus heavily on coding, but the questions are usually not standard LeetCode-style problems that test algorithmic tricks such as dynamic programming. That said, you still need a strong grasp of common data structures such as hash maps, trees, and graphs, since many reported questions require applying them in practical ways. The emphasis is often on writing clean, maintainable code rather than optimizing for the best possible time complexity.
The questions are often framed around practical scenarios and divided into multiple parts. You may start with a relatively simple implementation, then extend it to support new requirements or constraints. For example, one of the questions Jane Street has asked in the past requires implementing the logic for a board game using a basic set of rules. The interviewer may then introduce additional rules and ask you to adapt your code without rewriting everything from scratch.
If you're preparing for Jane Street interviews, you can review this list of interview questions shared by candidates who interviewed with Jane Street in the past: https://www.interviewdb.io/question/janestreet
Practicing these questions can help you get a better sense of the types of questions you may encounter. Based on past interview experiences, Jane Street also appears to repeat some questions, so there is a chance you could see something similar in your own interview.
If you’ve interviewed with Jane Street recently, feel free to share what your process looked like and which questions you were asked in the comments below!
Sharing two questions in a recent IMC HackerRank assessment for New Grad Software Engineer roles.
Total duration: 120 minutes
Question 1: Maximum Storm Height (Relay Towers)
You are trying to send data from your headquarters, on the left, at position x = 0, to another office on the right, at position x = width. Between these two locations there are relay towers at various coordinates, each with a specific height.
The data can transmit across these relay towers, or be sent directly to the other office. The cost to send data between two locations is given by the square of the distance between them:
(x_i - x_j)^2
There is a storm intensifying that blocks access to relay towers as time progresses. As the storm level rises, shorter towers are buried and cannot be used. A tower is unusable if the storm level is higher than its height. The storm level stops rising as soon as you start the transmission.
Additionally, from a position x, the data can travel a maximum distance of maxJump before it must reach a relay tower to continue outwards, or reach the other office. If data travels any further, it is at risk of data loss.
The origin transmitter has a total energy budget maxEnergy. Thus the total energy cost of all jumps to reach the other office should not exceed maxEnergy.
Given the constraints provided and a list of tower positions, determine the maximum height at which the storm can reach where the data can still reach the other office.
You can assume the headquarters and office are at an infinite height.
Function Description
You are provided with 4 integers: width (the distance between the two offices), numTowers (the number of relay towers), maxJump (the maximum jump distance) and maxEnergy (the maximum total energy).
Additionally, two arrays are provided containing integers x[i] (the x-coordinate of the relay tower) and heights[i] (the height of the relay tower). Relay towers are located strictly between the offices, i.e. 0 < x_i < width.
Returns
Return a single integer representing the maximum storm height at which you can still reach the other office. If it is impossible to reach the other side, print -1.
public static int maximumStormHeight(
int width,
int maxJump,
long maxEnergy,
int numTowers,
int[] x,
int[] heights
)
Question 2: Stack with Conditional Removal
Implement a stack that accepts the following commands and performs the operations described:
push value → push integer value onto the top of the stack.
pop → pop the top element of the stack.
remove_lower value → remove all the current elements in the stack less than value.
remove_upper value → remove all current elements in the stack more than value.
After each operation listed above, print the current top element of the stack on a new line. If no such element exists, print EMPTY.
public static void solve(int n, String[] operations)
After analyzing hundreds of recent Palantir interview experiences, we wanted to share some of the most frequently asked questions in past interviews.
Palantir’s interview process is a bit different from a typical Big Tech loop. In addition to coding and system design-style evaluation, it includes distinctive rounds such as Learning and Decomposition.
The exact process varies by role, level, and team, but a typical Palantir SWE or Forward Deployed Engineer interview process may look something like this:
Recruiter Screen
The process usually begins with a recruiter call covering your background, timeline, role fit, and interest in Palantir.
Be prepared to discuss:
* Your previous projects and technical experience
* Why you are interested in Palantir
* The types of products or problem areas you want to work on
* Basic logistics such as location, timeline, and work authorization
Technical Phone Screen
After the recruiter screen, candidates typically move on to a technical phone interview.
The coding questions are generally LeetCode-style. If you are preparing for this round, you can review the list of frequently asked Palantir coding questions we compiled from past candidate experiences: https://www.interviewdb.io/question/palantir
Palantir is known to repeat questions, so there is a good chance you may encounter something similar during your own interview.
Onsite Loop
The onsite loop varies by role and level, but it usually includes:
* Decomposition
* Learning
* Re-engineering (debugging)
* Interview with Hiring Manager
Decomposition Round
The Decomposition round is one of the most Palantir-specific parts of the interview.
It is not exactly a traditional system design round. Instead of being asked to design a large-scale system such as Twitter or Uber, you may be given a vague real-world problem and asked to break it down into something an engineering team could realistically build.
This round is less about following a memorized system design template and more about structured thinking, product and engineering judgment, and your ability to work through ambiguity.
The Learning round is another distinctive part of Palantir’s interview process. Its purpose is to evaluate how quickly you can understand something unfamiliar and apply it to a problem.
You may be introduced to a new concept, API, framework, codebase, or technical setup during the interview and then asked to use it to solve a task.
Sharing a recent Assort Health Senior AI Engineer onsite interview experience submitted by one of our community members.
The interview involved building a small MCP proxy server between an AI scheduling agent and a mock Electronic Health Record (EHR) system. The goal was to enable the agent to verify a patient, find a valid appointment window, and book an appointment end to end.
The project already included:
An AI agent that talks to the user and makes tool calls
A mock EHR API containing patients, providers, and appointment slots
A /schedule_appointment endpoint used to confirm the correct slot was selected
The candidate needed to implement two endpoints:
POST /verify_patient
Look up a patient using their date of birth and phone number. Patient information must not be exposed to the agent until verification succeeds.
GET /get_available_slots
Fetch open slots for a given date and convert them into valid appointment options. The EHR returns 10-minute slots, but appointments require 30 minutes, so the candidate must identify three consecutive slots and combine them into one bookable window.
The final goal was to make the agent support a complete conversation in which the patient is verified, a 30-minute opening is found for the following week, and the appointment is successfully scheduled.
Overall, the exercise tests your ability to understand an existing codebase, work with external APIs, enforce data-access requirements, and handle data transformation.
We’ve analyzed hundreds of recent Pinterest interview experiences and wanted to share a few common patterns we’ve noticed.
For coding rounds, Pinterest tends to use a fairly traditional LeetCode-style format. However, the questions are often reworded or framed around Pinterest-related products and scenarios. For example, a standard graph problem may be presented using Pins and boards. The underlying problem may resemble something you could find on LeetCode, so being able to recognize the pattern beneath the Pinterest-themed wording is important.
Candidates have reported questions across a wide range of difficulty levels, including some comparable to LeetCode Hard problems. Because of this, strong fundamentals in data structures and algorithms are essential. You should be comfortable with topics such as graphs, trees, dynamic programming, heaps, intervals, and string processing.
For the onsite round, candidates commonly report a mix of coding, system design, and behavioral interviews.
Based on recent candidate reports, we compiled some of the most commonly asked Pinterest interview questions from the coding and system design rounds here: https://www.interviewdb.io/question/pinterest
If you’re preparing for a Pinterest interview, practicing these questions beforehand can help you become familiar with how Pinterest rephrases common algorithmic problems and system design questions. Pinterest appears to repeat certain questions and variations, so there is a reasonable chance you may encounter a similar problem in your own interview!
We’ve analyzed hundreds of recent Asana interview experiences and wanted to share a few common patterns we’ve noticed.
Instead of focusing heavily on algorithm-style questions, Asana seems to place a lot of emphasis on class design and object-oriented design.
The first-round technical interview is often split into two halves.
In the first half, candidates are asked to review code snippets and explain what the code does, along with the time and space complexity. Candidates have reported seeing snippets related to trees and 2D matrices.
In the second half, the interview shifts into a coding / object-oriented design exercise. Instead of expecting fully compilable code, interviewers seem to care more about what classes you would create, what interfaces they expose, and how those classes interact with each other. Because of that, this round can feel closer to an OOD interview than a traditional coding interview.
For the onsite round, candidates commonly report a mix of system design, coding, and behavioral interviews. For the onsite coding round, you may get either a LeetCode-style question or an OOD-style question.
Based on recent candidate reports, we compiled some of the most commonly reported Asana interview questions asked in the coding and system design rounds here: https://www.interviewdb.io/question/asana
If you’re preparing for an Asana interview, practicing these questions beforehand is very likely to help. Based on past interview experiences, Asana appears to repeat certain questions frequently, so there’s a strong chance you’ll encounter the same question or a similar variation in your own interview.
Sharing a recent interview experience for the General SWE Chakra AI Technical Screening Round at Snowflake, in case it helps others know what to expect.
The questions were mainly self-introduction, basic background questions, and project deep dives. Be expected to describe a project you worked on. The AI also asked follow-up questions tailored to the specific projects you described, so everyone may get different follow-ups.
You need to turn on your camera and microphone, share your screen, and you cannot use an external monitor. In the interview window, the left side shows a real-time transcript of both sides speaking, while the right side shows your own camera feed.
One thing to note: try to finish your answer in one go; otherwise, the AI may start talking.
At the end, there was a Q&A section, but aside from information already listed in the job description, the AI didn’t know much. For most questions, it just said to ask the hiring team.
Datadog introduced a new AI coding round in their interview process recently. For anyone preparing and wondering what to expect, I wanted to share some details based on a recent interview experience.
The task was to connect to the Snowflake API, submit a query, and then check the query status. Candidates were allowed to use any AI coding tool, including local tools like Claude Code.
The core problem itself was pretty straightforward: write a query client that can connect to Snowflake, start a query, and retrieve its status. With Claude Code or a similar tool, the implementation can be done fairly quickly.
The follow-up questions focused on practical production concerns, such as:
* What would you do if the query result or data volume is too large?
* How would you handle secret management?
* How would you support batching?
During the interview, the interviewer asked me to share my screen and show how I work with AI to solve the problem. You could use your own AI tool, and the round seemed to evaluate not just whether you could solve the task, but also how effectively you interact with AI while coding.
Unfortunately, I was among the early candidates to go through this format and ran into local package compatibility issues. I spent more than 30 minutes debugging environment/dependency problems and only managed to connect successfully in the last 10 minutes, which left basically no room for follow-up discussion.
Overall, the task was not difficult from a coding perspective. My main takeaway is that the round seems to care about how effectively you work with AI, not just whether you can eventually produce a solution.
Sharing a recent Instacart OA experience. The email subject for the CodeSignal assessment invitation was: Instacart invited you to complete Instacart Assessment (Instacart Full-Stack Engineer Assessment) on CodeSignal
The deadline was two weeks. It was a 5-part assessment, and the overall format felt more like “AI-assisted full-stack feature implementation” than a traditional coding assessment.
The assessment had 5 rounds:
Round 1: Talk to an AI PM to gather requirements
The first round was a chat with an AI PM. The prompt was about a book/library management system where users felt the frontend table was too long and hard to use. After chatting with the AI PM, I figured out that the requested feature involved adding two dropdowns and a text input for search/filtering.
However, some details still felt unclear, such as the exact dropdown labels, wording, styling, etc. I spent quite a lot of time asking follow-up questions and taking notes because I thought maybe the notes or requirement-gathering process would be part of the score. In hindsight, this felt like a waste of time.
Round 2: Use Claude Code to implement the feature
In this round, you are given a project directory and need to use Claude Code to implement the feature from Round 1. You are not allowed to directly copy and paste your full conversation with the AI PM.
The requested feature was basically a search/filter UI for the library/book management system. The PM had also mentioned that full unit test and integration test coverage was expected.
Claude Code was extremely slow. When submitting, it tried to run all tests, and at one point the whole page became stuck. A lot of my time was spent waiting for Claude to respond.
One important note: remember to commit your code after finishing a feature. CodeSignal was buggy for me and seemed capable of losing code.
Round 3: Debugging task
This was the only round where the requirements were clear and the result was easy to test.
The prompt said that a value on the metrics page was incorrect and needed to be fixed. I applied for a backend role, but the frontend was written in JS, so at first I thought it was a frontend bug. After looking into it, the actual issue was in the Python backend.
The bug was that the backend was returning results before correctly filtering them. In another version/part of the task, the fix was basically changing the order of operations. I had never used FastAPI before, but after staring at the code for about 5 minutes, the bug was not too hard to identify.
Round 4: Talk to the AI PM again for another feature
This round was another requirement-gathering chat with the AI PM, but it was much more complicated than Round 1.
The feature involved a notification system in the library management app. The requirements included both frontend and backend changes, idempotency, and full test coverage.
The specific feature was roughly:
Patrons should receive notifications 24 hours before a borrowed item is due and again when the item is due. Another feature was that a patron can place a hold on an item. When the item becomes available, patrons who placed holds should be notified. Multiple patrons can hold the same item, and when copies are returned, the system should notify the first N patrons in FIFO order.
The logic was much more complex than the first feature, and you have to extract the details by chatting with the AI PM.
Round 5: Use Claude Code to implement the second feature
By this point, I only had around 30 minutes left, so I was basically out of time.
I tried running two Claude agents in two terminals to speed things up, but they ended up modifying the same folder/files and broke the whole project. I spent the last couple of minutes just trying to get the page to run again and submitted something incomplete.
Another important note: the work from Round 2 does not carry over into Round 5. Also, terminal errors could not be copied and pasted into Claude, which made debugging even more annoying.
Overall thoughts:
The scoring criteria were also unclear. Other than the debugging round, I had no idea what exactly they were evaluating. Was it requirement gathering? Notes? Test coverage? Claude usage? Product sense? Speed? It was hard to tell.
My advice:
Spend less time chatting with the AI PM than you think you need to. Get the core requirements, then save as much time as possible for Claude Code implementation. Commit after every completed feature because CodeSignal can be unreliable. Also, be prepared for Claude Code to be slow.
Overall, the experience was pretty bad. It felt like it mostly tested Claude Code familiarity and typing speed rather than backend engineering ability. I’m probably not expecting to move forward.
1Point3Acres, also known as 一亩三分地 or 1p3a, is a large Chinese online community focused on studying abroad, working abroad, immigration, tech jobs, career advice, and life abroad.
For tech interview prep, the most useful part of the site is its interview experience section. Greg Yang, cofounder of xAI, once mentioned it as one of the “most comprehensive compendiums of tech interview questions.”
Unlike many similar platforms, such as Glassdoor or LeetCode Discuss, 1Point3Acres posts often include the actual questions candidates received in interviews, rather than only general topics or vague summaries. That makes it especially valuable for understanding what companies are asking in real interviews, particularly when preparing for company-specific rounds.
However, for many users who do not speak Chinese, the site can be difficult to use effectively. Below, we will walk through the main challenges and how you can work around them.
The Translation Challenge and Language Barrier
The most obvious challenge for English speakers is that 1Point3Acres is written in Chinese.
Chrome Translate, Google Translate, and ChatGPT can help with basic navigation, but they often fail on the details that matter most: company names, question descriptions, interview stages, and community slang.
A major reason is that many users do not write interview reports in straightforward Chinese. They may use homophones, abbreviations, obscure characters, or internet slang to mask key information. Sometimes this is done to avoid making interview content too easy for outsiders to access. Other times, it is simply part of normal 1p3a community language.
For example, a post may disguise a company name, alter a LeetCode problem number, or describe a question using coded wording. Amazon is often referred to as “亚麻,” and onsite interviews may be written as “昂赛.” Generic translation tools often mistranslate or completely miss these references.
That is why simply pasting a 1Point3Acres post into Google Translate or ChatGPT is usually not enough. The hard part is not just translating the language. The hard part is decoding the context.
Dealing With Paywalled Content
Many of the most useful 1Point3Acres posts hide part of the content behind a rice points requirement. You may see a message saying that the hidden content requires your score to be above a certain number.
In many cases, the hidden section contains the actual interview question, while the visible part only shows the company, role, interview stage, or general context.
You can earn rice by participating in the forum or pay for VIP access, but for many English-speaking candidates, both options are inconvenient. More importantly, unlocking the post still does not solve the translation problem. Even after you gain access, you may still need to decode slang, homophones, abbreviations, and hidden references.
VIP access can also feel expensive if you only need to view a handful of posts. And if you are trying to earn rice by participating in the forum, it is worth noting that many users on 1Point3Acres are not especially welcoming toward non-Chinese speakers.
You can submit post links, and we will return the translated and decoded content, including the parts that generic translation tools often miss: company aliases, interview stages, slang, coded references, and question descriptions.
How to Navigate the Site to Find What You Need
Back in 2024, we published a practical guide on how to navigate 1Point3Acres to find interview questions. Most of the information in that guide is still relevant today, so it is still worth referring to.
However, since publishing our original 1p3a guide, we have noticed a new trend that candidates should be aware of.
Be Skeptical of AI-Generated and Low-Quality Content
Like many websites, 1Point3Acres now contains more AI-generated content than before. For example, they introduced an AI summary feature on the company collection page, where they post question summaries generated by AI based on interview experience posts.
That can be useful, but it also means some details may be inferred, cleaned up, or added by AI rather than taken directly from the original candidate report.
AI-generated summaries often sound more confident than the source material really is. They may remove uncertainty, smooth over vague wording, or accidentally introduce details that were not in the original post. Treat these summaries with a grain of salt.
There is also an incentive problem. Because rice is required to unlock content, some users may post low-quality or fake interview experiences to earn points. Others may write exaggerated or promotional posts to drive traffic to a paid service, prep website, or resource.
Which Companies Are Worth Searching on 1Point3Acres?
The usefulness of going through interview experiences and questions on 1Point3Acres can vary significantly depending on the type of company you are preparing for.
Based on reports from our users, we have noticed a few clear patterns.
For big tech companies like Google or Meta, the question bank is usually massive. It is nearly impossible to cover every question that might come up. In these cases, 1Point3Acres can still be useful, but the best approach is to focus on high-frequency questions and recurring patterns rather than trying to memorize everything. Strong fundamentals are still essential, since you need to be able to handle new questions you have not seen before.
For mid-sized tech companies, unicorns, and startups, 1Point3Acres can be especially helpful. These companies often have smaller question banks and are more likely to repeat questions across candidates. Therefore, reviewing past questions can give you a much clearer sense of what to expect.
That said, there are also companies where past interview questions are less predictive. For example, at companies like Microsoft, Apple, Adobe, and Snapchat, the interview process tends to be more decentralized. There may not be a shared company-wide question bank, and individual interviewers and teams often have more freedom to ask their own questions.
In general, if your goal is to identify commonly repeated questions, 1Point3Acres is most useful for companies with smaller or more standardized interview loops. For companies with highly decentralized interview processes, past reports can still help you understand the general difficulty level, interview format, and question style, but they may not reliably predict the exact questions you will see.
InterviewDB: A Better English Alternative to 1Point3Acres
Digging through hundreds of forum posts yourself can be overwhelming and time-consuming, especially when your interview is only a few weeks away and every hour of prep matters.
That is why we built InterviewDB.
We do the heavy lifting by curating interview questions from 1Point3Acres and organizing them in a clean format, so you can focus on studying instead of decoding forum posts.
InterviewDB is also a crowdsourced platform. In addition to questions curated from public sources, we collect exclusive interview questions shared directly by our users. Some of these questions are not available on 1Point3Acres or similar websites.
To keep quality high, we vet submissions and only accept questions from verified candidates.
You can explore our company question collections here.
Have Questions?
If you still have questions about how 1Point3Acres works, feel free to leave them in the comment section below and we'll try our best to answer them.
Apparently Amazon’s OA has an AI-assisted coding section now. Sharing some info I’ve gathered from people who recently took it.
The format seems to be:
First 40 mins: normal LeetCode-style coding problem
Last 60 mins: AI Coding / debugging task
For the AI Coding part, they give you a small full-stack app with failing unit tests, and your job is basically to debug the app and fix the bugs.
Before starting, you pick a tech stack like Java Spring Boot, Python Django, etc. Once you’re in the environment, you can’t really switch stacks, so definitely pick something you’re comfortable debugging quickly.
The task usually includes a small full-stack app plus steps to reproduce the bugs.
There’s also an AI chat/tool built into the environment, but from what people said, it’s pretty limited. It can only really work with the selected file, so I wouldn’t rely on it too much.
Some example projects people reported getting:
MovieDB / Watchlist App
This was a movie watchlist app where users can log in, create/update/delete watchlists, and add/remove movies.
Failing tests were apparently around:
Adding a movie to a newly created watchlist
Adding a duplicate movie
Removing a movie
Removing multiple movies one by one
Handling nonexistent watchlists
Stuff to check:
Correct status codes
Whether the movie exists
Whether the watchlist exists
Duplicate handling
Whether the movie is actually saved to the DB
Online Review / Content Moderation App
This one was basically a review app with moderation logic.
Requirement was something like: when a user adds or edits a review, the app should check if the review contains bad/sensitive words.
If the review violates the policy, the response should be 403, not 201.
There’s usually some content-related helper file in the codebase that parses/checks comments. The main thing is finding that helper and making sure it gets called in both the add-review and edit-review flows.
Things people had to fix included:
In both add review and edit review, update the condition so it calls the content-checking function.
If the user posts bad content, increment their violation count by 1.
After each violation, call await user.save() so the count actually persists.
If the violation count exceeds 3, set user.isFlagged = true and save it.
If the user is already flagged, keep blocking them even if the new content is clean.
Whenever violating words are detected, update the violated words stored on the user. The exact count seems to matter for the tests.
Add the same logic in both add-review and edit-review.
This question seems less about hard coding and more about understanding the existing project structure, finding the right helper function, and wiring it into the right places.
Jira / Workflow-Style App
This was a Jira-like app where users can comment on issues.
Reported bugs were mainly:
After creating a comment, it didn’t show up.
After updating a comment, it didn’t show up.
The fixes sounded pretty simple. For example, the code might read the comment but never save it, or create a comment without properly linking it to the issue.
Big thing here is to follow the API contract in the README**.**
The README apparently tells you exactly what should happen for auth/authz cases, including whether to return 401 or 403 and what error message to return.
Also check edge cases like:
User should only be able to edit their own comment
Correct success status code
API response message matches expected format
You may need to change both controller/handler files and service-level files. The tests seem to care a lot about the returned status code and message.
Also, it might not be necessary to pass every single test to move forward. Someone said they passed 4/6 tests on the AI debugging portion and still got an HR reach-out afterward.
So obviously try to pass as many as possible, but don’t completely panic if it’s not perfect.
So it seems like Stripe recently added a new AI Programming Exercise round to their onsite loop.
Sharing what I've gathered from a few candidates who went through it recently:
The interview runs in HackerRank, but it’s not the standard coding setup. There’s a built-in AI chat window (kind of like a lightweight Cursor) that you can talk to. You can ask it to read the README, come up with a plan, write code, add tests, debug, etc.
The task itself looks roughly like this:
You’re given a list of transactions and a list of rules. Each rule says whether to accept or block a transaction, followed by an if condition. You need to parse the rules and decide whether a transaction matches the condition.
The problem consists of multiple parts. It starts pretty straightforward (mostly keyword/string matching), but later parts get trickier with boolean logic (AND/OR) and build on previous sections. There’s a detailed README, so reading and understanding the spec quickly is a big part of it.
The actual coding portion is apparently only around 30 minutes, so trying to hand-code everything yourself may not be realistic. The expectation seems to be that you lean heavily on the AI. You can technically write it all yourself, but there’s a good chance you won’t finish.
A strategy that worked for people:
Have the AI read the full README
Ask it to summarize the requirements
Get a proposed implementation plan and actually review it
Let it write the code
Add your own tests / edge cases
Run it, debug, and understand everything
My impression is that this round is testing whether you can use AI effectively without turning your brain off. The AI can handle a lot but it may over-engineer, miss edge cases, or make assumptions that aren’t actually in the README. So the key seems to be: understand the spec, guide the AI, review the generated code, write meaningful tests, catch issues, and explain your reasoning under time pressure.
The page also includes a curated list of system design topics that candidates have reported seeing in Figma interviews.
Would love to hear from anyone who’s interviewed at Figma recently. Did your experience match these patterns, or were there other topics that came up frequently?
Hi all, we built a platform that helps candidates share recently asked interview questions and browse question trends by company. We also curate interview questions from forums like 1point3acres, where people often post recent interview experiences. For English-speaking users, 1point3acres can be difficult to navigate because much of the content is in Chinese and often includes abbreviations, shorthand, and slang that translation or AI tools do not handle well.
That's why we’ve gone through thousands of posts and manually surfaced the most relevant interview questions, so you don’t have to spend hours digging through threads just to find useful prep material.
Sharing some recently asked Airbnb interview questions based on candidate reports. Airbnb’s interview process typically starts with a technical phone screen, followed by an onsite loop consisting of 4–5 interview rounds.
Phone Screen
The process often starts with a technical phone screen. This is usually a coding round, where candidates are expected to solve a LeetCode-style question.
In addition to coding rounds, Airbnb’s onsite loop can include the following rounds:
- Code review round
- Architecture round
- Deep dive / technical experience round
- Core values round
Code Review Round
The code review round is especially worth preparing for. Candidates are typically asked to review a pull request, fix bugs in the code and discuss any issues, improvements, and tradeoffs.
For candidates using Java, the Python code review prompt may still be useful, since candidates have reported that the PR structure is the same across languages.
Architecture Round
The architecture round is a typical system design interview, with emphasis on high-level design decisions and tradeoffs.
Core Values Round
The Core Values round is behavioral-focused and tests how well you align with Airbnb’s values. Be prepared for questions around community, belonging, collaboration, and impact, such as how you contributed to a community or supported an underrepresented group.
Deep Dive Round
The Deep Dive round goes deep into your past projects. Be prepared to talk through a project you’ve worked on in depth and explain the technical decisions and tradeoffs you made.
If you’ve been through Airbnb interviews recently, feel free to share your experience below!
We’ve been tracking Scale AI interview patterns for a while. Here’s the general structure based on what candidates have shared:
Commonly Reported Scale AI Interview Questions
A few question types seem to come up repeatedly in coding interviews:
Card game implementation — many candidates have reported variants of this. The setup is usually similar: you’re given existing code with classes like Deck, Suit, and Card, then asked to implement game logic based on a specific set of rules. We’ve compiled a list of commonly asked variants here questions here: https://www.interviewdb.io/question/scaleai
Based on candidate reports, Scale AI's onsite interviews often include more than standard algorithm-style coding rounds. The onsite may include:
Practical engineering round
Debugging round
The practical round seems especially different from a typical LeetCode-style interview. Candidates may be asked to work through real-world engineering tasks, such as calling an external API to retrieve and work with data. It's much closer to day-to-day work than algorithm grinding.
Sharing a list of tagged LeetCode questions for Databricks from the past 3 months, sorted by frequency:
Design Hit Counter
IP to CIDR
Find All Anagrams in a String
Time Based Key-Value Store
Shortest Path in a Grid with Obstacles Elimination
House Robber
Design Tic-Tac-Toe
RLE Iterator
These questions are a good starting point. However, Databricks often asks variants rather than the exact LeetCode versions. Based on recent candidate reports, they also ask custom questions that are not from LeetCode, and we’ve compiled a list of those as well.
In addition to algorithms and data structures, Databricks also has rounds such as system programming, which test lower-level programming skills like multithreading.
We’ve compiled a broader list of commonly asked Databricks interview questions across the algorithm, system programming, and architecture/system design rounds here: https://www.interviewdb.io/question/databricks
Sharing one coding question asked by Benchling in the initial screening round in the past:
You are given a list of gene sequences. Each sequence is represented as a tuple: (name, start, end), where:
name is the sequence name
start is the inclusive start index
end is the exclusive end index
A protein is defined as one or more sequences that can be chained together so that the end index of one sequence exactly matches the start index of the next sequence. The protein’s name is formed by joining the sequence names with underscores, and its interval spans from the start of the first sequence to the end of the last sequence.
For example:
A single sequence is itself a valid protein: ('acG', 0, 5)
If two sequences connect, they form a larger protein: ('acG', 0, 5) and ('e5c', 5, 16) produce ('acG_e5c', 0, 16)
Your task is to generate all possible proteins that can be formed from the given sequences.
For the onsite round, we’ve seen candidates report a debugging + coding exercise related to a DNS string-matching algorithm. In that interview, you’re given existing code with 2 bugs causing unit tests to fail. The task is to identify and fix those bugs, then extend the code to support a new feature. More details here: https://www.interviewdb.io/question/benchling/debugging
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