Most Forward Deployed Engineer (FDE) interview loops include a case-study round: the interviewer describes a customer situation and asks how you would handle it. It tests what makes FDEs different from other engineers, which is turning an ambiguous business problem into a working, safe system. This guide gives you a structure for any case and three worked scenarios.
For coding and concept questions, see FDE interview questions; for behavioural rounds, behavioural questions for AI engineers; for pure architecture, AI system design questions.
What interviewers look for
- Do you ask clarifying questions before designing?
- Do you tie the solution to a measurable business outcome?
- Is the architecture realistic about data, security and integration?
- Do you plan for the model being wrong?
- Can you explain trade-offs simply, as you would to a customer?
A 6-step structure for any case
- Clarify (3–5 minutes): users, current process, data sources, volume, constraints (compliance, residency, budget), timeline.
- Define success: one business metric, one quality metric, one operational metric.
- Propose the simplest solution that works: often RAG or a narrow agent with human approval, not a fully autonomous system.
- Walk the architecture: request flow, data flow, where permissions are enforced.
- Risks and mitigations: wrong answers, data leakage, prompt injection, latency, cost, adoption.
- Rollout: pilot, evaluation gate, wider release, what you'd do in the first two weeks.
Scenario 1: insurance claims triage
Prompt: "An insurer receives 3,000 claim emails a day with attachments. Adjusters spend hours sorting them. What would you build?"
Clarifying questions: Which systems hold policies and claims? What decisions do adjusters make in triage? Which claim types are highest risk? What must stay in India? What error rate is acceptable?
Solution: extract key fields from the email and attachments (document extraction service plus an LLM with structured output), classify claim type and urgency, look up the policy through an API, and route to the right queue with a summary. Adjusters approve or correct the routing; nothing is auto-paid.
Metrics: time to first touch, routing accuracy against adjuster decisions, extraction field accuracy.
Risks: fraud signals hidden by summarisation (keep originals one click away), PII in logs (redact), new claim types (route to a human queue when confidence is low).
A full build is in the claims triage agent project.
Scenario 2: internal IT helpdesk agent
Prompt: "A company wants an agent that resets passwords, grants software access and answers IT questions."
Key points to raise:
- Split into read-only Q&A (RAG over IT knowledge articles) and actions (tools).
- Actions need strong identity checks, least-privilege tool permissions and approval for anything risky (admin rights, finance systems).
- Tools wrap existing ServiceNow or Entra ID APIs; every call is logged with the requesting user.
- Prompt injection from ticket text or documents must not trigger actions.
- Start with the top 5 request types by volume, measure deflection, then expand.
Related: ServiceNow agent with MCP.
Scenario 3: "our chatbot gives wrong answers"
Prompt: "A customer's RAG assistant launched last month. Users say it's often wrong. You have two weeks."
Approach:
- Collect 50–100 real failing questions with the expected answers from subject experts.
- Trace each failure: was the right document retrieved? Was it in the index at all? Did the model ignore it?
- Typical fixes by cause: missing or stale documents (ingestion), poor chunking or no metadata filters (retrieval), exact-code queries failing (add hybrid search), answers beyond the context (stricter prompt, groundedness check, "I don't know" path).
- Re-run the evaluation set after each change and report the numbers to the customer.
This is the most common real FDE task. See RAG evaluation metrics and the 16-week RAG engagement playbook.
Mistakes that fail candidates
- Jumping to "fine-tune a model" or "multi-agent system" before understanding the problem.
- No mention of permissions, PII or audit logging.
- No plan for evaluation or for when the model is unsure.
- Talking only to the interviewer's technical side and ignoring the business owner's goal.
- Running out of time before covering rollout and risks.
How to practise
- Take 10 business processes you know (loan approval, onboarding, invoice matching) and run the 6 steps aloud in 20 minutes each.
- Write a one-page solution design doc for two of them.
- Do mock rounds with a friend playing a sceptical customer.
Cloud Soft Solutions runs FDE mock interviews, including case-study rounds, in the FDE PRO program.
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Frequently Asked Questions
What is the FDE case-study interview?
A round where the interviewer describes a customer situation and asks how you would handle it end to end, from clarifying questions to rollout.
How should I structure my answer?
Clarify, define success, propose the simplest working solution, walk the architecture, cover risks and mitigations, then the rollout plan.
What do interviewers look for?
Clarifying questions, a measurable business outcome, realistic data and security design, a plan for wrong answers, and clear trade-off explanations.
What mistakes fail candidates?
Jumping to fine-tuning or multi-agent designs too early, ignoring permissions and PII, no evaluation plan, and running out of time before rollout.
How can I practise?
Run the six steps aloud on 10 familiar business processes in 20 minutes each, and do mock rounds with someone playing a sceptical customer.
