Decomposition Questions Asked in Palantir Interview

Decomposition interviews test one specific skill: your ability to break a broad, ambiguous problem into a clear structure before proposing a solution. This guide covers what the format tests, a seven-step answering framework, and six real questions from verified 2025 and 2026 interview loops at Palantir and other companies, each with specific guidance on how to approach it. For more guidance, see the how to prepare for Palantir interviews.

What is a Decomposition Interview?

A decomposition interview presents you with a broad, ambiguous problem and asks you to break it into smaller, manageable parts before proposing a solution.

Instead of testing whether you know a fixed answer, it tests how you organize your thinking when the problem itself is not fully defined. Closely related to a case interview, this format appears under different names depending on the company and role. At Palantir, Google, and Meta, decomposition questions show up inside system design or product sense rounds. At consulting firms such as McKinsey and Bain, a structurally identical format appears in the case round, though the subject matter leans toward financial and operational scenarios.

In 2026, this format tests three distinct capabilities.

First, structured thinking and prioritization. Can you take a vague prompt and organize it into a logical set of categories instead of jumping straight to a solution? Once you have a list of possible angles, can you identify which two or three actually matter most, instead of treating every branch as equally important?

Second, communication under ambiguity. Can you narrate your thought process clearly enough that a stranger sitting across from you can follow your logic in real time? Interviewers are not just grading the framework you produce. They are evaluating whether you can explain your reasoning as you build it.

Third, judgment about what matters. Given limited time and incomplete information, can you make a defensible call about where to focus rather than covering every possible angle with equal depth? This judgment is what separates strong answers from technically correct but structurally weak ones.

How to Answer a Decomposition Interview Question?

Decomposition questions follow a repeatable pattern once you know what interviewers are actually testing.

Example prompt: "A ride-sharing app has seen a drop in completed rides over the last month. How would you figure out why?"

Step 1: Clarify the Problem

Clarifying the problem means asking targeted questions before you attempt any structure, so you do not waste time solving the wrong issue. Interviewers deliberately leave out details, and asking good questions is part of what they are scoring.

For the ride-sharing example, do not start listing causes right away. Instead, ask questions such as: Is the drop happening in every city or specific regions? Is it a drop in requested rides, or a drop in the percentage of requested rides that get completed? Did anything change recently, such as pricing, driver incentives, or a competitor entering the market? Over what exact time window are we measuring this drop compared to what baseline?

A candidate who asks 2 to 3 sharp questions first shows the interviewer they understand that ambiguous problems need scoping before they need solutions.

Step 2: Identify the Main Components

Identifying the main components means splitting the problem into a small number of broad categories that represent the different areas where the issue could live. 3 to 5 categories is the target.

For the ride-sharing drop, a reasonable first cut might include rider-side factors (fewer people opening the app, more people abandoning before booking), driver-side factors (fewer drivers online, longer wait times), pricing and incentive factors (surge pricing changes, reduced driver bonuses), and external factors (weather, a local event, a competitor promotion, a regulatory change).

Notice that each category answers a different question about where the drop could be coming from. That variety is what makes the list useful instead of just a pile of related ideas.

Step 3: Make the Categories MECE

Making your categories MECE means checking that they do not overlap with each other and that, together, they cover the entire problem with no gaps.

MECE stands for Mutually Exclusive and Collectively Exhaustive, a framework developed by Barbara Minto during her time at McKinsey, and it remains the standard test consultants and interviewers use to judge whether a candidate's structure is genuinely useful.

Going back to the ride-sharing categories, check for overlap first. Is "reduced driver bonuses" a pricing issue or a driver-side issue? It touches both, so state clearly that you are grouping incentive changes under pricing and incentives, and driver behavior under driver-side, so there is no double counting.

Then check for gaps. Have you missed anything? A technical outage or app bug that blocks bookings would not fit cleanly into any of the four categories above, so add a fifth: platform and technical factors.

A framework does not need to be perfectly MECE to be useful, but naming the overlaps and gaps out loud shows the interviewer you understand the concept and are applying it deliberately rather than guessing at a structure. For a complete set of MECE-based structures you can adapt to different interview scenarios, the case interview frameworks guide covers seven approaches that map to most prompts you will encounter, including issue trees, value chain breakdowns, and hypothesis trees. Once you have a go-to framework structure, the next challenge is knowing when to adapt it to the specific constraints of the prompt. Some prompts call for a standard component breakdown. Others benefit from a timeline-based structure or a funnel approach that moves from broad categories to narrow metrics. Recognizing which pattern fits the prompt is itself a skill that comes from exposure to multiple frameworks rather than memorizing a single one. Interviewers notice when a candidate applies a canned framework inappropriately, and they notice even more when a candidate adapts their structure thoughtfully based on what the prompt actually specifies.

Step 4: Prioritize the Most Important Areas

Prioritizing means picking the 1 or 2 categories most likely to explain the problem and stating why, instead of analyzing every branch equally. Interviewers are looking for judgment about where the real signal probably lives. This prioritization step is the single most differentiated skill across all technical interview formats that involve ambiguous, open-ended problems, because it is the step where candidates most reliably reveal whether they are reasoning from evidence or hedging to cover every possibility.

For the ride-sharing example, say something like: "Given that this drop happened across every city at the same time, I would deprioritize external factors like local weather or events, since those are usually regional. I would also deprioritize platform bugs unless engineering flagged an incident, since that tends to show up as a sharper, more sudden drop rather than a gradual one over a month. That leaves rider-side and pricing and incentives as the two areas I want to dig into first, since a month-long, cross-market decline points toward something structural rather than local."

This step is where many candidates go wrong. They build a strong MECE framework and then try to analyze all branches with equal depth, running out of time before concluding. Naming your priority branches and defending that choice with a reason tied to the details in the prompt is what separates a strong answer from an average one.

Step 5: Analyze Each Component

Analyzing each component means digging into your prioritized branches with specific questions, data points, or hypotheses rather than staying at the level of vague category names. This is where you show real subject-matter thinking.

For the rider-side branch: "I want to know if fewer riders are opening the app at all, or if the same number are opening it but fewer are completing a booking. If it is the second case, I would look at whether estimated wait times shown in the app went up, since riders tend to abandon a booking if the quoted wait time crosses a certain threshold."

For the pricing and incentives branch: "I would check whether average fare per ride increased in the same window, and whether driver incentive spend dropped, since a reduction in driver bonuses often leads to fewer drivers online during peak hours, which then raises wait times and causes riders to cancel."

Each of these statements ties directly back to something measurable, which turns a category label into an actual hypothesis an interviewer can engage with. The same analytical instinct applies whether you are diagnosing a product metric or structuring an estimation from scratch, which is why practicing market sizing interview questions alongside decomposition prompts sharpens quantitative reasoning across both formats.

Step 6: Connect the Findings

Connecting the findings means showing how the pieces you analyzed relate to each other, since real problems are rarely explained by a single isolated cause. This is where you demonstrate systems thinking rather than checklist thinking.

In the ride-sharing example: "If driver incentive spend dropped at the same time fewer drivers came online during peak hours, and that led to longer quoted wait times, and riders abandon bookings once wait times cross a certain point, then the pricing and incentives issue and the rider-side abandonment issue are actually the same root cause showing up in two places. The fix would need to address the incentive change directly, rather than treating rider abandonment as a separate problem."

This is the step that most clearly separates a strong candidate from an average one, because it shows you are thinking about the system as a whole rather than treating your MECE categories as isolated boxes.

Step 7: Give a Clear Recommendation

Giving a clear recommendation means closing your answer with a specific next step or decision. Interviewers consistently mark down candidates who analyze a problem well but never land on an actual answer.

A strong close for the ride-sharing example: "My recommendation is to first pull driver incentive spend data against wait time and cancellation data for the affected window. If the correlation holds, I would recommend restoring the driver bonus level in the two or three markets showing the sharpest decline, and monitoring completed ride rate over the following two weeks before rolling any change out further." That is specific and gives the interviewer something concrete to push back on or agree with.

6 Real Decomposition Interview Questions (2025-2026)

The questions below were gathered from real 2025 and 2026 interview loops, discussion threads on Reddit and TeamBlind, and direct accounts from candidates who sat through rounds at specific companies. For the Palantir-specific format in more detail, including the internal scoring rubric and what each step of the seven-point method is worth, the Palantir decomposition interview guide covers the company format, the common follow-up questions interviewers use to push deeper, and the preparation approach that candidates who passed the round used. Use the seven-step method above as your default approach, then adjust based on the guidance for each question below.

1) Design a class scheduling app for your university; assume you would have two weeks to build a fully functioning prototype.

(Source: Reddit, 2025)

Start by clarifying who the primary user is, since a scheduling app for students planning their semester is a different product than one for administrators assigning classrooms.

Once scoped, break the problem into core components: course and section data, student input and constraints (required classes, preferred times, professor ratings), a conflict detection engine, and an output or recommendation layer that suggests valid schedules.

Given the two-week constraint, prioritize the conflict detection engine and a basic input flow first, since a working schedule generator with no polish is more valuable in a prototype than a polished interface with broken logic.

Concretely, what you would cut: no mobile app, no account system beyond a simple login, and a hardcoded course catalog instead of a live integration with the registrar's system.

Close by stating what the two-week milestone actually delivers: a web tool where a student enters required courses and gets back two or three conflict-free schedule options.

2) Decompose an internal LinkedIn-like system.

(Source: Glassdoor, 2025)

Clarify the primary use case: is this for employees to find internal experts and mentors, or for HR to map organizational skills? That single question changes the entire structure. Break the system into components: user profiles (skills, role, team, past projects), a search and discovery layer, a connection or messaging layer, and a content or feed layer for internal updates.

Prioritize the profile and search components first, since discovery is the core value of a LinkedIn-like tool and everything else, including feeds and messaging, only matters once people can actually find each other.

Give a concrete example of a MECE profile schema: role and team, skills tagged by proficiency, projects worked on, and availability for mentorship. Close by naming the first success metric: percentage of employees who complete a profile within the first month, since adoption is the real risk with any internal tool.

When an interviewer pushes this question toward implementation, the component architecture shares principles with engineering rounds. The system design interview cheat sheet covers the same component-first thinking extended to APIs, data models, and scale constraints for when a decomposition round turns into an architecture discussion.

3) Decomposition of a social network for matching employees with similar interests.

(Source: Reddit, 2026)

Clarify what "similar interests" means in this context: hobbies outside work, professional interests, or both, since the matching logic depends heavily on that answer.

Break the system into components: interest data collection (how employees input or reveal interests), a matching algorithm, a way for matched employees to actually connect (events, group chats, or one-on-one introductions), and a feedback loop to measure whether matches led to real connections.

Prioritize interest data quality and the connection mechanism over building a sophisticated matching algorithm, since a good match that never turns into an actual conversation delivers no value. Give a concrete example: start with a simple tag-based system, employees select up to five interest tags from a curated list, and matches surface as a monthly digest suggesting three colleagues who share at least two tags. Close by naming the metric that matters: percentage of suggested matches that lead to a scheduled coffee chat or event, not just the number of matches generated.

Firsthand accounts from 2026 interview loops add detail that no guide can fully capture. One Palantir SWE candidate described what the decomposition round felt like under real-time pressure in this community discussion on the Palantir SWE 2026 onsite, including what caught them off guard and what preparation they would prioritize differently the next time.

4) You are given access to {a set of data points}. Design an application or system that extracts business or social value from it. What would it do? What components would you build?

(Source: Reddit, 2026)

Clarify what the data points actually represent before proposing anything, since the same dataset can support very different products depending on what fields, volume, and update frequency are available.

Break your response into three components regardless of the specific dataset: a value hypothesis (what problem this data could solve for someone), a processing and analysis layer (how raw data becomes a usable signal), and a delivery layer (how the insight reaches the end user, whether through a dashboard, alert, or recommendation).

Prioritize the value hypothesis first and defend it clearly, since interviewers use this question to see whether you can identify a real use case rather than just describing technical steps.

Give a concrete example using a hypothetical dataset of retail foot traffic by store location and hour: the value hypothesis could be helping small retailers optimize staffing schedules, the analysis layer would identify peak hour patterns by day of the week, and the delivery layer would be a simple weekly email showing recommended staffing changes.

Close by stating one assumption you are making about data quality or availability, since naming assumptions shows the interviewer you understand real data is messier than the prompt implies.

5) You are given eight thousand records of taxi data in London with eight different columns: ID, fare, start location, end location, start time, end time, and distance. You need to suggest an idea: draw up system components, come up with the required API, and code if required, for a solution that could be developed and deployed in one week. What would you do?

(Source: TeamBlind, 2025)

Clarify the target user for the idea before jumping to a build plan, since eight thousand rows of trip data could serve riders, drivers, or city planners in very different ways.

Given the one-week constraint, propose something narrow and immediately useful, such as a fare estimation tool that predicts trip cost based on start location, end location, and time of day, since that is buildable from the existing columns without needing external data.

Break the system into three components: a data processing layer that cleans the eight columns and engineers features like trip duration and average speed by route, a simple prediction model trained on fare versus distance, time, and location, and a lightweight API endpoint that accepts a start point, end point, and time, then returns an estimated fare.

For the API, sketch a minimal contract: a POST endpoint accepting start_location, end_location, and requested_time, returning estimated_fare and estimated_duration. Close by naming the one-week milestone precisely: a working endpoint tested against a held-out slice of the eight thousand records, with accuracy reported as average error in dollars, not a polished front end.

6) Can you sketch out how you would implement the game Pac-Man?

(Source: Glassdoor, 2025)

Clarify what "implement" means in the context of the interview, since a systems-level sketch is different from writing full game logic in code, and confirming this upfront prevents you from over-building or under-building your answer.

Break the game into its natural components: the game board or maze representation, the player character and its movement and input handling, the ghost characters and their movement or AI logic, collision detection between the player, ghosts, and pellets, and the scoring and game state system that tracks lives, level, and win or loss conditions.

Prioritize the board representation and collision detection first, since every other system, including ghost AI and scoring, depends on knowing exactly where every entity sits on the grid at any given moment. Give a concrete example: represent the maze as a two-dimensional grid where each cell is either a wall, an open path, a pellet, or a power pellet, then check collisions each game tick by comparing the player's grid coordinates against the ghosts' coordinates.

Close by naming the piece worth simplifying first if time runs short, which is ghost AI, since even a simple random-movement ghost lets you demo a fully playable loop, whereas skipping collision detection breaks the entire game. Patterns like this repeat across decomposition rounds at every company, which is why practicing the underlying problem-solving approach matters more than memorizing individual questions. Since decomposition rounds are open-ended and unscripted, Interview CoPilot™ provides real-time support during a live interview, helping you think through unfamiliar problems and structure responses on the fly rather than freezing when a prompt goes in an unexpected direction.

How to Prepare for Decomposition Interview Rounds

The most effective preparation for a decomposition round is practicing the seven-step structure out loud under time pressure, not reading more written examples. Candidates who complete 10 to 15 timed mock sessions before their onsite score consistently higher on structure and recommendation clarity than those who only study written breakdowns. The reason is that decomposition answers require a specific kind of real-time thinking: the ability to build a structure while you are still narrating it, which reading alone does not train. You need to practice the motion of clarifying, structuring, and prioritizing until the sequence becomes automatic. Time yourself on each mock session, aiming to reach a full recommendation within eight minutes before extending to more complex prompts.

Building a practice habit around the seven-step method changes how you experience the real round in measurable ways. Candidates who have completed 10 or more mock sessions report that the clarification step, which often feels awkward in early practice, becomes a natural opening that buys thinking time rather than spending it. The prioritization step, where most untrained candidates hedge by covering every branch equally, starts to feel like the strongest moment in the answer rather than a risk. The recommendation step, which many candidates omit in practice, becomes the confident close that actually makes the interviewer want to engage rather than move on. These shifts happen through repetition and feedback, not through reading about the concepts.

For realistic practice sessions with immediate feedback, AI Mock Interview generates fresh decomposition prompts, scores your structure against the rubric interviewers use, and provides specific feedback on your prioritization and recommendation clarity after each attempt. That feedback loop is what allows you to identify exactly where your structure breaks down and fix it before a real interview.

Conclusion

Decomposition interview questions test how you think when a problem has no clear starting point, not whether you already know the right answer.

The seven-step method in this guide covers every phase of a strong answer, from asking clarifying questions through connecting your findings to a concrete recommendation. Practice it until the sequence feels automatic, and the specific question you get on interview day matters far less than you might expect.

Remember that interviewers expect you to think in real time, not to already know the answer. A calm, structured process that reaches a reasonable recommendation will consistently outperform a rushed attempt to guess the "correct" answer the interviewer supposedly has in mind.

Frequently Asked Questions about Decomposition Interviews

What is a decomposition interview question?

A decomposition interview question presents a broad, open-ended problem and asks you to break it into smaller, structured parts before proposing a solution. It tests how you organize ambiguous information, not whether you know one fixed correct answer. These questions appear across product, engineering, data, and consulting interviews at companies of every size.

How long should a decomposition interview answer take?

Decomposition answers should run between five and eight minutes when practiced out loud, including clarifying questions, building your framework, and reaching a recommendation. Interviewers generally allow more time in a live round, but the actual analysis portion should stay tight so you have room to answer follow-up questions without running over.

Is a decomposition interview the same as a case interview?

A decomposition interview and a case interview overlap heavily but are not identical. Case interviews, common at consulting firms, almost always involve a business scenario with numbers to analyze. Decomposition interviews at tech companies can involve technical systems, datasets, or product scenarios, so the underlying method is the same even though the subject matter varies significantly.

What is the biggest mistake candidates make in decomposition interviews?

The most common mistake is jumping straight to a solution before asking clarifying questions or building a structure. This makes it look like you are guessing rather than reasoning, and it usually leads to answering a narrower question than the one the interviewer actually asked. A close second mistake is analyzing every branch of a framework with equal depth instead of prioritizing.

Which companies use decomposition interview rounds in 2026?

Palantir is the most consistent user of a dedicated decomposition round, where it appears as a standalone step in the onsite loop for SWE and data roles. Google and Meta use similar formats embedded within product sense or system design rounds rather than as a labeled decomposition interview. Consulting firms including McKinsey, Bain, and BCG use a structurally identical format in their case rounds, though the subject matter leans toward financial and operational scenarios rather than technical systems.

Candidates who use real-time interview AI to practice before the real interview typically feel more prepared and confident going in.

Related Interview Guides

• Profitability Framework for Case Interviews: Covers the core financial decomposition used in consulting rounds, applying the same component-first MECE logic to revenue, cost, and margin structures that decomposition rounds test in a different context.
• How to Handle Unexpected Interview Questions: Decomposition prompts often feel unexpected even when you know the format is coming. This guide covers the mindset and pacing techniques for staying composed when a question does not match what you rehearsed.
• How to Answer Interview Questions: Frameworks That Score: A broader guide to answering frameworks across behavioral, product, and analytical rounds, showing how the same organizing logic applies across interview types at the same companies.
• Behavioral Interview Question Bank: 200 behavioral questions organized by competency, including the communication and structured prioritization skills that decomposition rounds also test directly.