Platform engineer role-specific interview questions

As asked

Your LLM spend triples overnight after a product release, but traffic only grew 10 percent. How do you investigate and contain it?

Sample answer outline

Segment cost by route, tenant, model, prompt version, and input versus output tokens to find the regression. Common causes include prompt expansion, retrieval returning too many chunks, a loop in tool calling, lost cache hits, or a fallback to a more expensive model. Immediate containment can be budget caps, prompt rollback, max-token limits, cache restoration, or disabling the offending feature flag. The permanent fix is cost tests in CI, per-route budgets, and dashboards that show token deltas on deploy. Strong answers avoid simply lowering max tokens if it breaks task quality.

Expect these follow-ups

• What cost guardrail would you enforce before launch?
• How do you distinguish abusive usage from a product bug?
• Who should be allowed to override a budget cap?

As asked

You inherit a cloud estate where every team uses admin-like roles in a shared account. How would you redesign access without blocking delivery?

Sample answer outline

Start by separating environments and blast radii: production from non-production, shared platform from application accounts, and human access from workload identity. Define roles around actions people actually need, then enforce least privilege with permission boundaries, short-lived credentials, and break-glass access that is audited. Use infrastructure-as-code and policy-as-code so access changes are reviewed, repeatable, and testable. Strong answers mention migration sequencing because cutting everyone over in one move usually breaks deployments. Candidates often trip up by describing perfect IAM theory without a path from the current unsafe state.

Expect these follow-ups

• How do you handle a senior engineer who insists they need admin for debugging?
• Where would you use service control policies or organisation policies?
• What audit signals tell you the new model is working?

As asked

Your tracing bill is too high, but engineers rely on traces to debug rare checkout failures. How would you change the sampling strategy?

Sample answer outline

Separate head sampling, which decides early and is cheap, from tail sampling, which can keep traces based on outcome after the request completes. Keep all error traces, slow traces, and traces for important business flows, while sampling routine successful traffic at a lower rate. Propagate sampling decisions consistently across services so one request does not produce broken partial traces. Use attributes with bounded cardinality to drive retention rules, and validate with engineers that rare failure modes remain findable. A weak answer simply lowers the global sampling percentage and loses the exact traces people need during incidents.

Expect these follow-ups

• What metadata must be propagated for tracing to work across services?
• When is tail sampling too expensive or too late?
• How would you prove the new sampling policy is not hiding production bugs?