FDE system design interview

Classic “design Twitter” prompts are the wrong preparation. FDE loops ask how you would land a product capability — often with LLMs — into a customer environment that already has SSO, legacy systems, and a security team that says no by default.

What interviewers optimize for

• Clarify constraints first — data residency, latency, air-gap, who owns Day-2
• Name integration reality — APIs, webhooks, batch ETL, rate limits, idempotency
• Design for failure — retries, poison queues, partial outages, rollback
• Prove quality — evals, logging, groundedness for AI paths

Core pattern checklist

Identity & access

Sketch SSO / SAML / OIDC, service identities, least privilege, and how user permissions flow into retrieval or agent tools. “We’ll use an API key in a config file” is usually a fail.

Integration patterns

• Sync vs async boundaries (and why you chose them)
• Webhook verification, replay protection, backoff
• Warehouse / lakehouse landing zones vs operational stores

LLM / RAG specifics

• Permission-aware retrieval and citation requirements
• Eval harness before “ship the prompt”
• VPC / private networking / air-gapped model hosting options

A strong answer structure (8–12 minutes)

1. Restate goal + non-goals; ask 3–5 constraint questions
2. Draw the happy path end-to-end (user → systems → outcome)
3. Call out the riskiest dependency (usually data or identity)
4. Propose an MVP slice that can land in weeks
5. Add observability, evals, and rollback
6. Describe Day-2 ownership with the customer

Practice prompts

• Deploy an internal RAG assistant over SharePoint + a CRM with SSO and no public internet egress for documents
• Add an agent that can create tickets in the customer ITSM with human approval for high-risk actions
• Move 5TB of on-prem data into a cloud warehouse under a 2-week security review window

Pair with case / decomp practice — many loops blend both.

Common failure modes

• Jumping to Kubernetes diagrams before clarifying data ownership
• Ignoring security / compliance until the last minute
• Designing for perfect data that the customer does not have
• No measurement plan — “we’ll know it works when users like it”