FDE interview round guide
Decomposition and customer problem framing round
Updated November 11, 2026 · 9 practice questions
TL;DR
The decomposition round hands you a business problem with no spec ("our ops team wants AI to cut late shipments") and watches how you get from there to a plan a team could start on Monday. Strong answers pin the outcome to a decision someone makes, check what data actually exists before designing anything, pick a first slice small enough to ship in weeks, and say how success will be measured. Model choice is the last thing you talk about, not the first.
What this round tests
Forward deployed work starts where a product spec would normally be: a customer with a problem, a deadline and a messy set of systems. This round is a compressed version of the first week on a new account. The interviewer usually plays the customer or a sponsor inside the customer, answers questions when asked, and volunteers little.
- Framing: can you turn "use AI to fix X" into a measurable outcome tied to a decision a person makes every day?
- Data realism: do you ask what data exists, who owns it, how fresh it is and whether you can get access, before you design on top of it?
- Prioritisation: can you cut the problem into a first slice that delivers value in two to six weeks, and say what you are deliberately not doing yet?
- Technical plausibility: is the plan buildable with ordinary components, and do you know where the hard parts are (integration, data quality, permissions), not just the model?
- Communication: can you explain the plan to a non-engineer, state your assumptions out loud, and adjust when the "customer" pushes back?
How to approach it: a six-step decomposition
Our guidance (editorial), not a candidate report
Use the steps as a spine for the conversation, not a script. Spend roughly a third of the time on steps 1 to 3; most weak answers skip them and jump to architecture.
1. Outcome as a decision
Restate the ask as "who decides what differently, and what number moves". "Reduce late shipments" becomes "ops leads re-plan at-risk loads earlier, so on-time delivery rises from 88% to 92%". If you cannot name the person and the decision, keep asking.
2. Today's workflow
Map the current process: actors, steps, systems and hand-offs. Ask where time or money is lost and what people do by hand. This is where the real requirements hide ("the carrier emails a PDF, someone retypes it").
3. Data reality
For each signal you need: which system holds it, who owns it, how fresh it is, how it joins to the rest, how dirty it is, and whether you can legally and practically get it (PII, contracts, security review lead time). Treat access as a schedule risk, not a detail.
4. First slice
Choose the smallest end-to-end path a real user will touch: one team, one workflow, one decision. Prefer a rule or simple model that ships in weeks over a sophisticated one that ships next quarter. Say explicitly what is out of scope for now.
5. Build plan
Name the components (ingest, transform, logic, surface where the user already works), the integration points and what stays manual in v1. Sequence the work so something is visible to the customer every week or two.
6. Measurement and risks
Define the success metric, a leading indicator you can see in week one, the baseline you compare against, and the two or three things most likely to sink the project, with what you would do about each.
Worked example: "Use AI to cut our late shipments"
The prompt
You are the engineer on a new account: a freight brokerage that moves about 3,000 truckloads a week for retailers. The COO says: "Late deliveries are killing our retailer scorecards. We bought your platform; use AI to fix it." You have 40 minutes with the COO (played by the interviewer).
A strong answer, condensed to what the candidate actually says and decides at each step.
Outcome
Asks how lateness is measured on the scorecards (delivered after the appointment window), the current rate (say 11% of loads late) and what a late load costs (chargebacks of $150 to $500 plus risk to the account). Proposes the target: "Cut late deliveries from 11% to 8% on the pilot lanes in one quarter, measured by the retailers' own appointment data." Then asks who could have acted earlier on a late load: the answer is the track-and-trace team, 12 people who re-book carriers or reschedule appointments.
Workflow
Learns that the team calls drivers at fixed times ("check calls"), works from a spreadsheet export of the transport management system (TMS), and finds out about most problems when the driver or the receiver phones in. So the decision to change is when a load gets attention, not how it is fixed: the fixes (new appointment, recovery carrier) already exist.
Data
Lists what is needed to predict a miss: appointment time and location (TMS, reliable), the truck's current position (electronic logging device or app GPS for about 70% of carriers, asked how they get it), driver hours-of-service remaining, historical dwell time at each facility (derivable from TMS timestamps) and weather or traffic (bought feed, later). Flags two risks: the 30% of carriers without tracking, and that TMS timestamps are typed in by people, so "arrived" may be logged hours late.
First slice
Proposes an "at-risk loads" queue for in-transit loads on the five highest-volume lanes: every 15 minutes, compute the remaining drive time from the last ping, add expected dwell at any intermediate stop and the driver's required break if hours are low, and flag the load if the projected arrival is past the window minus a buffer. No machine learning in v1; a transparent ETA rule the team can argue with. Out of scope for now: untracked carriers, pickup-side delays, automatic re-booking.
Build
Ingest TMS loads (nightly export plus an hourly delta via the TMS API if it has one), carrier pings via the tracking provider's webhook, and compute the flag in the platform. Surface the queue inside the tool the team already uses: a view sorted by how many minutes each load has to spare before its window, with the reason ("projected 47 min late: 6 h drive, 2 h of hours-of-service left"). Week 1: data access and a backtest on last month's loads. Week 2 to 3: shadow mode, flags shown to two team leads only. Week 4: the whole team on the pilot lanes.
Measurement and risks
Primary metric: late rate on pilot lanes against the same lanes last quarter and against non-pilot lanes over the same weeks (to separate seasonality). Leading indicators: precision of the flag in the backtest (of loads flagged 4 hours ahead, how many were really late) and the share of flags the team acted on. Risks: noisy pings (mitigate with a staleness rule: no ping for 2 hours is its own flag), alert fatigue (cap the queue at the 20 loads with the least time to spare), and the team not trusting it (show the reason for every flag; review misses weekly with the leads).
Why it scores well: the candidate found the real lever (earlier attention, not a new fix), tested data quality before relying on it, shipped a rule before a model, and set up a fair comparison. When the COO asks "so where is the AI?", the strong answer is: "The backtest tells us where the rule misses. If misses cluster around dwell time at certain facilities, a learned dwell estimate is the first model we add, and we will know it helped because the same backtest gets better."
Common follow-ups and what they probe
- "The data you need does not exist. Now what?"
- Probes whether you can create a proxy or start collecting. Good answers name a proxy you can get now, a manual step that produces the data as a side effect of the workflow, and the date the real signal becomes usable.
- "Our VP wants all five regions live by next month."
- Probes how you say no. Offer a sequence with a date for each step and the risk of going wider earlier, and ask which region has the most to gain so the first win is visible.
- "How would you know in two weeks whether this is working?"
- Probes leading indicators. Name something observable before the outcome metric moves: precision of flags, actions taken per flag, time from flag to action.
- "What would you build, what would you configure, and what would you leave manual?"
- Probes judgement about product versus custom work. Configure the platform where it fits, write custom code only at the integration edges, and keep low-volume steps manual until the volume justifies automation.
- "How does this carry over to our next customer?"
- Probes whether you notice reusable patterns. Point at the parts that were customer-specific (the TMS connector) and the parts that could become product (the at-risk queue that ranks loads by time to spare).
How it is scored
Our guidance (editorial), not a candidate report
Companies do not publish rubrics for this round. The table is our synthesis of what the round is designed to find out, written as the signals an interviewer can observe.
| Dimension | Weak signal | Strong signal |
|---|---|---|
| Framing | Restates the prompt; starts designing at once | Names the decision, the owner and the metric before designing |
| Data realism | Assumes clean, available data | Asks source, owner, freshness and access; flags quality risks |
| Scoping | Proposes the full platform in v1 | Ships a narrow first slice; says what is out of scope and why |
| Technical plan | Buzzwords ("we'll use an LLM") | Concrete components, integration points and sequencing |
| Measurement | No baseline or comparison | Baseline, control, leading indicator and success threshold |
| Communication | Talks to the interviewer as an engineer only | Plain language, checks in, adapts when pushed back on |
Common mistakes
- Opening with model or architecture choices before knowing the decision, the users or the data.
- Asking twenty clarifying questions with no hypothesis behind them. Ask fewer questions, each tied to a decision in your plan.
- Boiling the ocean: every region, every user type and full automation in the first phase.
- Treating data access as a detail. Security reviews and data-sharing agreements are often the longest item on the schedule.
- No success metric, or a metric nobody at the customer already tracks.
- Never stating assumptions, so the interviewer cannot correct you and you drift off course.
Practice questions for this round
Reported interview questions from our bank that fit this round, reviewed by hand. They come from user uploads and 1Point3Acres posts. They were reported from software, ML and AI engineering loops, many of them at companies that hire forward deployed engineers; only 3 on this list were reported from an FDE loop as such. Questions marked Free are open to everyone.
Few reported questions match this round: most interview reports describe coding or design prompts, not open business problems. These are the ones that genuinely ask you to frame an ambiguous business or customer problem. For more practice, work through the worked example above and run a mock round with the AI interviewer.
A system design question about turning a table of past cab rides into a tool that working drivers could use within days. It tests scoping an open problem to one user decision, deriving useful signals from raw ride records, choosing a success metric, and sketching a small service end to end.
Palantirreported November 2026A system design question about a workplace tool where employees discover colleagues with the same hobbies, talk, and plan get-togethers. It looks at defining success for the business, which records to rely on, privacy and consent, how to rank suggestions, and how to make the tool useful when few people have signed up.
Palantirreported July 2025A system design question about a search tool that suggests places to eat where a group can actually hear each other at the hour they plan to go. It covers making a fuzzy goal measurable, launching without direct measurements, handling a venue whose atmosphere changes through the week, and weighing evidence that disagrees.
Palantirreported November 2026A product and domain question about commercial insurance underwriting. It asks for a walkthrough of how a new risk becomes a bound policy, a reasoned choice of which step software should take over first, a customer-facing success metric, and the questions worth asking a working underwriter.
FurtherAIreported October 2026This Amazon Applied Scientist interview module evaluates a candidate's ability to navigate vague, under-defined business problems. The interviewer provides a deliberately sparse scenario centered around machine learning domains like ranking or brand safety, expecting the applicant to proactively clarify objectives, propose structured solutions, and reason through architectural tradeoffs. Unlocking the complete preparation guide, framework for handling ambiguity, and expert evaluation notes requires an active platform subscription.
Amazonreported April 2026This machine learning fundamentals problem, reported from an interview at BetterUp, challenges candidates to translate a virtual coaching scenario into a concrete predictive objective. You will write data wrangling code to clean and prepare a raw dataset without building the full model, utilize large language models to assist with the workflow, and discuss important production deployment considerations. Access to the complete problem description and model solution requires a subscription.
This Capital One machine learning system design case study requires architects to build a secure, compliant virtual assistant for a heavily regulated financial institution. The exercise tests your expertise in weighing retrieval-augmented generation against fine-tuned language models, addressing latency constraints, and measuring automated interaction success. You must carefully scope automated workflows versus human handoffs. To view the comprehensive design framework and expert solution, a subscription is required.
Capital Onereported February 2026Navigate a comprehensive financial evaluation framework in this Capital One data science case study centered on credit portfolio profitability. You will be tasked with deconstructing revenue streams and loss vectors for lending products while demonstrating agile mental math to quantify the impact of risk policy adjustments or line-of-credit modifications. The assessment measures your strategic business acumen, structured problem decomposition, and ability to translate risk-adjusted metrics into actionable executive recommendations. Discover the detailed case analysis and professional resolution strategies by subscribing.
Capital Onereported February 2026Assessing product intuition and user-centric problem-solving is the objective of this open-ended discussion question commonly used at Reddit. Candidates are expected to analyze a specific user journey, segment target audiences, identify friction points, and propose measurable improvements backed by clear hypotheses. This framework-driven exercise evaluates your strategic thinking, ability to prioritize metrics, and communication skills during product design rounds. To read the complete interview guide, evaluation rubric, and sample expert responses, a subscription is required.
Redditreported March 2026
Companies in this list
FAQ
- Is the decomposition round the same as a product sense interview?
- They overlap, but the decomposition round expects you to go further into data, integration and sequencing. A product sense answer can stop at what to build and why; an FDE answer has to say how it gets built inside the customer's systems and how you will know it worked.
- Should I bring up machine learning or LLMs at all?
- Yes, where they are the right tool, but late. First show the decision, the data and a simple baseline. Then explain what a model would add and how you would prove it beats the baseline.
- How many clarifying questions should I ask?
- Enough to fill in the outcome, the workflow and the data, usually five to eight good ones. Each question should change something in your plan; say why you are asking it.
- What if the interviewer refuses to answer a question?
- State an assumption, say how the plan would change if it were wrong, and move on. That is exactly what you would do with a busy customer.
Other FDE rounds
Practical coding
Working code against messy input: data wrangling, calling a paginated, rate-limited API, and debugging code you did not write.
Deployment system design
Design inside someone else's environment: their identity, network and systems of record. RAG and agents, data pipelines and integrations that survive real failures.
AI / LLM applications
Build something useful on a model API: structured outputs, retrieval, tools and agents, with an evaluation that proves it works and guardrails for when it does not.
Behavioral & customer
Stories about ownership and judgement, plus live role-plays: a failed demo, an unhappy sponsor, a request you have to turn down.