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Implement a log-structured event store for fleet metrics with range queries and compaction.

Metrics arrive in time order and are queried by time window, which is the pair of facts the whole design turns on. Sealed segments with a sparse index, pruning that skipped 82 of 84 segments in the executed test, and TTL compaction that drops whole files without rewriting a byte.

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

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Foundational

💻 Coding for Infra

The Practical Coding Screen PlaybookThe AI infrastructure coding screen is 45 to 60 minutes of building a small, realistic piece of systems code (a scheduler, a rate limiter, a batcher, a log parser, a cache) in the language you choose, with an interviewer who extends the problem twice and watches how you handle it. It is not a puzzle round: the score comes from working code early, tests that name the invariants, complexity said out loud, and calm follow-ups. Some companies allow an AI assistant and some ban it, and each policy changes what is measured. This page gives the minute-by-minute plan, the habits that score, and the mistakes that end the screen.

Foundational

🖧 Hardware & Cluster Build-Out

The Bill of Materials for a Training ClusterA GPU cluster is not a pile of GPUs. A 512-GPU scalable unit built to NVIDIA's DGX SuperPOD B300 reference architecture needs 64 nodes, four separate networks, thousands of transceivers, storage that can absorb a checkpoint burst, a management plane, racks, power distribution and cooling equipment. Writing the list out in order is how a design becomes a purchase order, and the items people forget are the ones that hold up a deployment for weeks.

Advanced

🔌 Networking & Storage🔒 Premium

Checkpoint I/OA checkpoint of a 70B model's training state is 1.13 TB, and a 405B model's is 6.5 TB. Written synchronously every half hour, it stalls thousands of GPUs for as long as the slowest rank takes to reach the disk. The design that scales writes each rank's shard in parallel, copies it off the GPU to host memory first so the run resumes in seconds, and drains it to durable storage in the background. The arithmetic is bytes per rank against the bandwidth of each hop, and the goal is a checkpoint that costs the run under 1% of its time.

Foundational

🔌 Networking & Storage

Dataset Lifecycle: Ingest, Shard and RetainA training dataset is not a file, it is a pipeline with four stages and a retention policy, and each stage has a different bottleneck. Ingest is metadata-bound rather than bandwidth-bound. Tokenization is CPU work that should happen once offline rather than every epoch. Sharding decides whether the training read is a stream or a storm of small files. And retention decides how much of the bill is paid for bytes nobody reads.

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Next in this trackA multi-GPU training job hangs at step 400 with every GPU at 100 percent utilization. Debug it.Closest to this topicDesign the API for a GPU job scheduler in 45 minutes. What are the resources, states and semantics?Closest to this topicEstimate how long it takes to write a checkpoint for a 405B training run

FEDITOR'S NOTE

Scored on the append-only design following from the write pattern, on segment pruning before any bytes are read, and on compaction dropping whole segments rather than deleting records.

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