Microsoft System Design & Production Engineering interview questions

Averages lie, and interviewers know most candidates quote them anyway. The fastest way to sound production-grade in any latency discussion is to reason about percentiles correctly, including the one trap almost everyone falls into.

One malformed message can silently wedge an entire pipeline, and it happens at every customer eventually. The DLQ answer interviewers want covers what most candidates forget: what happens *after* the message lands there.

This isn't a deployment question, it's a judgment question wearing one. Interviewers are scoring whether you have a decision framework, a safety checklist, and the spine to say no to a customer politely.

A Fermi estimate with a paycheck attached. The interviewers' favorite filter: candidates who jump to a dollar figure miss the two numbers that actually break deployments, peak QPS and tokens-per-minute limits.

A signature FDE triage question with a hidden rubric: interviewers score the *order* of your moves, not just the list. There's a reason 'what changed?' beats 'check the dashboards', and a reason mitigation beats diagnosis.

API keys for systems you don't own, in environments you can't always reach, audited by security teams who don't trust you. The strong answer has one organizing principle, and a concrete story for rotation and the air-gapped case.

The Palantir decomposition round has no right answer, it has a right METHOD, and it's the most-failed interview in the FDE loop. Here is the four-move rubric interviewers actually score: clarify, model, spine, iterate.

The classic Palantir decomposition prompt. The winning answer hinges on one asymmetry candidates rarely surface unprompted, and on starting with something embarrassingly simpler than an ML model.

The most open-ended prompt in the Palantir canon, no scale given, no user given, no disaster given. What separates passing answers is one design assumption about the data that almost nobody states out loud.

The purest data-modeling prompt Palantir asks, the whole interview lives in the entity-resolution edge cases. Twins, maiden names, shared insurance IDs... and one architectural decision about merging that candidates get fatally wrong.

Looks like an algorithms question; it's actually an operations question wearing one. The candidates who fail reach for 'solve VRP'; the ones who pass design for the 2pm reality that invalidates every morning plan.

Enterprise deals die in this design review. The answer has three layers most candidates blur together: who you are, what you can see, and how an embedded product proves both, plus the permission-staleness trap that fails Glean loops.

The defining Palantir-style deployment question. Everything your laptop did silently (package pulls, API calls, telemetry, auth callbacks, clock sync you never thought about) becomes a named workstream. Strong answers produce the checklist by category, not by anecdote.

The CFO sees API invoices; nobody sees the cost of the inference team they're about to hire. The strong answer models utilization, the variable that decides this question, and names the break-even honestly enough to be trusted.

Two questions disguised as one: classic multi-region (where the hard part is data, not compute) and dependency failover (where the hard part is that your fallback model gives different answers). Degraded modes are designed, not improvised.

The question that defines forward-deployed engineering as a discipline: production you're responsible for but can't touch. The answer is a system (version skew, telemetry tiers, diagnostic bundles, release trains), not a collection of favors from customer IT.

Active-active is easy until two regions write the same agent's memory at once. The strong answer picks a write model on purpose, names what conflicts and what cannot, and refuses to pretend a long-running agent run survives a regional failover for free.

No egress is the easy half. The hard half is that an auditor will read your logs, your access trail, and your model-update process for the next three years. The strong answer designs the evidence the audit consumes as a first-class output.

A crashed agent is easy. The hard one returns a confident answer after a tool quietly returned nothing and the model made up the rest. The strong answer instruments the steps that fail without throwing, ties traces to an outcome signal, and refuses to call a 200 a success.

Ten X-ray studies a second, a 2-second budget, and a missed cancer is unacceptable. The hard parts are recall-first thresholding, a monitor that watches the radiologist, and surviving a new scanner.

Every queue, webhook, and retry loop you'll ever deploy at a customer hides this distinction. Interviewers use it to separate people who've shipped from people who've read about shipping.

A staple at OpenAI and Anthropic in both coding and design rounds. Most candidates can name the algorithms; few can say which one lets 2x your limit through, and that's the part that gets scored.

The signature FDE debugging genre: a moving fault, an opaque client, and a customer who's sure it's your fault. The winning method splits the problem at the boundary, and knows the four classic culprits hiding on their side.

Standard observability tells you the request returned 200 in 800ms. It cannot tell you the answer was wrong. The strong answer names the new failure plane LLMs introduce, and takes a real position on the prompts-and-PII question.

The bug only appears under load, the code 'hasn't changed,' and the customer just got charged twice. There's one root-cause pattern behind almost every version of this incident, and a fix with a subtle race most candidates miss.

The decomposition prompt we use to teach the skill that actually decides these interviews: graceful iteration under perturbation. Your score isn't your design; it's the delta when the design gets punched.