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Implement a circuit breaker in front of a model endpoint. What are the states, and what does the half-open state buy?

Three states and one insight: after a sustained failure, retrying is worse than not calling at all, and the only way to learn whether the backend recovered is to send a small number of probes. The state machine, the two thresholds, and why a rate over a window beats a consecutive-failure count.

Updated Sep 2026 · Grounded in real AI infrastructure interview loops and written to a senior-engineer editorial bar, with every number worked and every diagram hand-built.

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The concepts behind this question

Ranked by how closely each one overlaps this question's topic, so the first card is the thing to read if the answer above moved too fast.

Foundational

📐 AI Systems Design

Control Plane and API Design for GPU PlatformsEvery GPU platform has a control plane, and its API is what the rest of the organization experiences as the platform. Three semantics decide whether it survives contact with a network: idempotent creation so a retried request does not launch a second job on sixty-four GPUs, cancellation modelled as intent because only the node agent can stop a running process, and cursor pagination that does not skip rows when work is created during a listing.

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💻 Coding for Infra🔒 Premium

Batching Queues and BackpressureWrite a request batcher is the coding round's version of the serving engine's scheduler: requests arrive one at a time, the GPU wants them in groups, and the batcher decides when a group is full enough to send without holding anyone too long or accepting more than it can hold. The two knobs are the maximum batch size and the maximum wait, the invariant is a bounded queue, and the follow-ups (priorities, cost-aware batching, cancellation, bounded in-flight batches) are the ideas the real engines carry. This page implements the batcher in asyncio, derives what each knob buys, and walks the follow-ups.

Advanced

💻 Coding for Infra🔒 Premium

Interval Merging and Utilization LogsGiven busy intervals per GPU, when was the whole cluster idle? What was the utilization per hour from a log of start and stop events? Which jobs overlapped? These are the interval problems of the infrastructure coding screen, and they share one tool: sort the endpoints and sweep. The sweep line turns every variant into a single pass with a counter, the sort is the only thing that costs more than linear time, and the edge cases (touching intervals, zero-length events, an unterminated start) are where candidates lose the round. This page works the standard problem and its relatives with code, tests and the complexity derivation.

Foundational

💻 Coding for Infra

The GPU Credit Scheduler PatternThe most widely reported coding problem in AI infrastructure loops is a small scheduler: accounts hold credits, jobs arrive with a cost and a priority, and you must decide which jobs run, in what order, without letting any account overspend, then extend it under follow-ups (refunds, reservations, concurrency limits, fairness). It is not a trick question; it is a test of whether you can model state cleanly, pick the right data structures, keep invariants under mutation, and talk about complexity while typing. This page works the problem from the first line to the fourth follow-up, with the code, the invariants, and the derivations.

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FEDITOR'S NOTE

Scored on the three states with the half-open probe, on a failure rate over a window rather than consecutive failures, and on knowing this complements retry rather than replacing it.

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