Forward Deployed Engineer Interview Questions (2026)

Forward deployed engineer interviews test whether you can scope a vague customer problem, design and build a system inside someone else's environment, and explain your trade-offs to non-engineers. Below are 45 forward deployed engineer interview questions grouped by the rounds companies actually run, with a short note on what a strong answer covers for each. The round structure is drawn from published company guides and candidate reports, linked in the next section.

Last checked: 22 October 2026.

How the FDE interview process is structured

Most FDE loops combine a recruiter call, a technical screen and a final set of four to six interviews. OpenAI's interview guide describes a skills-based assessment (pair coding, a take-home or a technical test) followed by final interviews of four to six hours with four to six people, and says engineers are judged on well-designed solutions, code quality, performance, test coverage and communication.

Palantir, which created the FDE title, runs the most documented loop. According to Aced (formerly Exponent), the onsite draws three rounds from five: a decomposition round on scoping a vague real-world problem, a learning round on extending an unfamiliar codebase, a coding round, a re-engineering round on debugging several hundred lines of unfamiliar code, and a system design round, followed by a hiring manager interview. AI tools are prohibited throughout.

Newer AI companies add a customer simulation and LLM depth. Educative's FDE course lists decomposition, customer simulation, technical (coding plus AI system design) and behavioural rounds, and a Cognition AI candidate report describes a take-home with a five-minute recorded walkthrough. Recent Glassdoor FDE interview reviews include RAG questions at ValueLabs, a case-study round at Postman and a hallucination question for an associate FDE role at Deloitte.

Customer discovery and scoping questions

1. A hospital network says it wants "an AI assistant for clinicians". How would you scope the first engagement? Start by asking who the users are, which task costs them the most time and how success will be measured. Narrow to one workflow, name the data sources and constraints, and propose a pilot with a clear exit metric.
2. How would you reduce traffic congestion in a city? This is a classic decomposition prompt reported for Palantir. Break the problem into stakeholders, available data (sensors, transit, events) and levers, then pick one measurable sub-problem and sketch the data and APIs needed.
3. Two departments keep separate employee records that disagree. How would you sync them? Clarify which system is the source of truth for each field, how often records change and who resolves conflicts. A strong answer covers matching keys, conflict rules, an audit trail and a rollout that runs both systems in parallel first.
4. What questions do you ask in your first meeting with a new enterprise customer? Cover the business outcome, the current process, data availability and access, security and approval requirements, and who signs off. Show that you listen for the problem behind the requested feature.
5. The customer's stated requirement conflicts with what their users need. What do you do? Show the evidence from user conversations or data, explain the risk of building the stated version, and offer a scoped alternative. Keep the decision with the customer while making the trade-off explicit.
6. How do you write a scope of work for a six-week prototype? Include the problem statement, in-scope and out-of-scope items, data and access dependencies, milestones, acceptance criteria and risks. Mention how scope changes will be handled.
7. How do you decide whether a use case is a good fit for an LLM at all? Weigh tolerance for error, availability of grounding data, cost per task and whether a rules-based or classical ML approach would be simpler. A strong candidate is willing to recommend against an LLM.
8. How do you define success metrics for a deployment? Tie metrics to the business outcome (time saved, resolution rate, error reduction) and pair them with model-level quality measures. Agree a baseline before launch so improvement can be shown.

System design questions for a customer deployment

9. Design a document question-answering system for a bank's internal policies. Cover ingestion, chunking, embeddings, a vector store, retrieval with access control, generation with citations, and an evaluation set. Address data residency and audit logging for a regulated client.
10. Let several teams query shared data without exposing raw records. This decomposition prompt appears in Palantir guides. Discuss aggregation layers, role-based and row-level access, differential exposure of fields and query logging.
11. Design a pipeline that ingests data from a customer's legacy on-premises systems. Cover connectors or change data capture, a staging layer, schema validation, idempotent loads and monitoring. Explain how you handle network restrictions and limited access to the source systems.
12. How would you deploy the same product to a customer who cannot use the public cloud? Discuss containerised deployment on their infrastructure, model hosting options, update and patch processes, and what telemetry you can still collect. Be clear about which features degrade.
13. Design an agent that files and triages IT support tickets. Cover the tools it can call, permissions per tool, human approval for risky actions, state and memory, fallbacks and evaluation. Explain how you prevent it from acting outside defined boundaries.
14. How do you design for a customer whose data quality is poor? Profile the data first, build validation and cleaning into the pipeline, and surface quality metrics to the customer. Scope the first release around the data that is reliable.
15. Your prototype works for 50 users. What changes for 5,000? Discuss load, latency budgets, caching, rate limits on model APIs, cost per request, observability and support processes. Prioritise the changes that remove the biggest risks first.
16. How would you hand over a deployed system to the customer's team? Cover documentation, runbooks, monitoring dashboards, alert ownership, training sessions and a support period. Show that you design for maintainability from the start.

Coding and debugging questions

17. Model shapes that compute their area, then extend the design with a new shape. This screen question is reported for Palantir. Show clean abstractions, clarify requirements, then refactor without breaking existing behaviour.
18. Fetch all records from a paginated REST API and handle failures. Cover pagination tokens, retries with backoff, rate limits, partial failures and tests. Explain how you would make it resumable.
19. Write a SQL query that aggregates across several joined tables. Get the joins and grouping right, watch for duplicate counting from one-to-many joins, and explain how you would verify the result.
20. A tally function double-counts some events. Find and fix the bug. Re-engineering rounds use prompts like this. Reproduce the bug, form a hypothesis, add a failing test and fix the root cause without patching symptoms.
21. Given the number of examples in each class, write a function that returns a class-balanced sample. This was reported in a Cresta FDE interview. Handle classes with too few examples, randomness and reproducibility, and state the complexity.
22. You are given an unfamiliar codebase and its documentation. Add a feature in 45 minutes. Explain how you orient yourself: entry points, tests, data flow and the smallest change that works. Ask purposeful questions and narrate your reasoning.
23. Return the N most common characters in a string. A long-standing Palantir FDE question on Glassdoor. Use a hash map and a heap or sort, discuss ties and complexity, and write tests for edge cases.
24. Write a script to parse and clean a messy customer CSV export. Handle encodings, inconsistent dates, missing values and duplicates, log rejected rows and keep the transformation reproducible.

AI, LLM integration and RAG questions

25. What is RAG and how would you use it in a customer project? Asked at ValueLabs according to Glassdoor. Explain retrieval plus generation, why it grounds answers in customer data, and the main design choices in chunking, embedding and ranking.
26. Tell me about a time you dealt with hallucinations in a system you built end to end. Reported for a Deloitte associate FDE role. Describe how you detected the problem, the fixes (better retrieval, constrained prompts, citations, refusals) and how you measured improvement.
27. How do you evaluate a RAG system before launch? Build a labelled question set with the customer, measure retrieval and answer quality separately, and track faithfulness to sources. Mention regression testing when prompts or models change.
28. Retrieval returns the right documents but answers are still wrong. What do you check? Look at chunk boundaries, context ordering and length, prompt instructions and whether the model is ignoring context. Test with a stronger model to isolate the cause.
29. When would you fine-tune instead of using RAG or prompting? Fine-tuning suits consistent style, format or narrow task behaviour, while RAG suits changing knowledge. Weigh data volume, cost, maintenance and evaluation effort.
30. How do you control the cost and latency of an LLM feature? Discuss model selection by task, caching, prompt length, batching, streaming and routing simple requests to smaller models. Tie the answer to a per-request budget.
31. How would you connect an LLM to a customer's internal tools? Cover tool or function calling, an integration layer such as an MCP server, authentication, scoped permissions and logging every call. Explain how you test tool use.
32. Is a model like the one behind ChatGPT decoder-only or encoder-decoder, and why does it matter? A Roland Berger candidate reported this question. Explain that GPT-style models are decoder-only and how architecture affects tasks such as translation, embedding and generation.
33. How do you monitor an LLM application in production? Track latency, cost, error rates, retrieval hit rates, user feedback and sampled quality reviews. Set alerts on drift and feed failures back into the evaluation set.

Security and compliance questions

34. How do you make sure users only retrieve documents they are allowed to see? Enforce permissions at retrieval time using the source system's access controls, filter before generation and log access. Never rely on the prompt to hide data.
35. How would you defend an LLM application against prompt injection? Separate instructions from untrusted content, limit tool permissions, require approval for high-impact actions and validate outputs. Accept that no single defence is complete.
36. A customer requires that no data leaves India. How does that change your design? Discuss in-region hosting, model deployment options, logging and backup locations, and contractual checks on third-party APIs. Document data flows for the customer's compliance team.
37. How do you handle PII in training data, prompts and logs? Cover detection and redaction, minimisation, retention limits, encryption and restricted log access. Mention relevant regulation such as India's DPDP Act or GDPR where it applies.
38. What would you put in a security review document for a client's CISO? Include architecture and data flow diagrams, threat model, access controls, encryption, logging, incident response and third-party dependencies. Write it for a reviewer who will challenge every assumption.
39. How do you manage secrets and credentials in a customer environment? Use the customer's secrets manager or a vault, rotate credentials, apply least privilege and keep secrets out of code and logs.

Behavioural questions

40. Tell me about a time you pushed back on a customer request. Reported for Palantir hiring manager rounds. Show how you understood the underlying need, presented evidence and kept the relationship intact.
41. How do you talk to clients as an engineer? Asked at Federato according to Glassdoor. Explain how you translate technical trade-offs into business terms and adjust depth to the audience.
42. Describe the most complex project you have led. A recruiter-screen question in the Cognition AI report. Cover the problem, your specific role, key decisions, results and what you would change.
43. Tell me about a deployment that failed or went badly. Take ownership, explain the root cause and describe what you changed afterwards. Interviewers look for honesty more than a perfect outcome.
44. How do you handle working across several customers with competing deadlines? Explain how you prioritise by impact and risk, communicate early when dates slip and protect time for deep work.
45. Why do you want to be a forward deployed engineer here? Connect your experience to the company's customers and products, and show you understand the travel, ambiguity and accountability the role involves.

How to prepare for an FDE interview

Practise decomposition aloud on open-ended problems, since scoping is the round most candidates find unfamiliar. Build at least one end-to-end project that a customer could use, such as a monitored RAG service with access control and an evaluation set, and be ready to walk through its trade-offs. Rehearse reading unfamiliar code quickly, because learning and re-engineering rounds reward that skill directly.

Our guide to becoming a forward deployed engineer sets out the skills in more depth, and the FDE course comparison covers structured preparation options. For a feel of ADaSci's proctored, scenario-based exam format, the ADaSci sample exam is free on the first attempt; it currently previews the AIDP credential.

Prepare with the CFDE credential

The Certified Forward Deployed Engineer (CFDE) covers most of the ground in this list, including agentic frameworks, cloud-native deployment, security and observability, and customer-centric solution design, and ends in a 60-question proctored exam. If you want shipped projects to discuss in interviews, CFDE Pro adds four code-reviewed builds over a four-week live incubator.

Frequently asked questions

How many rounds are there in a forward deployed engineer interview?

Most loops have a recruiter call, a technical screen and three to six final interviews. Palantir's onsite uses three rounds drawn from a pool of five plus a hiring manager interview, and OpenAI describes final interviews of four to six hours with four to six people.

What is the decomposition round in an FDE interview?

It is a roughly 60-minute round, with little or no code, in which you scope a vague real-world problem and sketch a high-level design covering data and APIs. Interviewers assess structured thinking, user empathy and realistic scoping.

Are FDE interviews harder than software engineer interviews?

They are broader. FDE loops still include coding and system design, and add customer scoping, unfamiliar-codebase rounds and questions on communication and judgement, so candidates need to prepare for more formats.

Do FDE interviews include LeetCode-style questions?

Many do. Glassdoor reviews mention LeetCode-medium live coding at BCG and medium-to-hard algorithm questions at Invisible Technologies, while Palantir's coding round focuses on core data structures tied to a product scenario.

Can I use AI tools during an FDE interview?

It depends on the company. Palantir prohibits AI tools throughout its interviews, while OpenAI says AI tool use varies by interview and advises candidates to ask their recruiter.