Snowflake ML Platform Engineer interview questions

Calculate how many reads a training loader needs in flight, then see when larger requests waste bandwidth. Worked examples separate useful samples, fetched bytes, prefetch and the limits of Little's Law.

Define model aliases, permission-aware routing, response reuse and bounded retries. Trace a request whose cached answer becomes invalid after its source permissions change.

Follow one request from API admission to streamed output, then zoom into the token-generation loop. See where the cache lives, who schedules work, and how workload measurements turn the drawing into a capacity plan.

Follow the weight bytes through a small decode batch and a long prefill. A scoped roofline calculation explains the difference without pretending to predict actual latency.

Trace GPU credits through reservation, allocation, metering and settlement, with crash-safe ownership and a separate fairness policy.

Follow a token into retained attention state, derive bytes per position for a 70B model, and distinguish present cache usage from the room needed to finish a response.

Track request admission through a concrete page-reservation example, then connect scheduling, growth and stream cleanup to measured service limits.

Derive a 10,240-GPU layout from model work, then account for collective traffic, durable checkpoints, power and replacements. A nested diagram connects tensor, pipeline and data parallel groups to the fleet.

Work from each GPU's cache budget, reserve room for generated tokens, then check latency separately. A limiting-rank example shows why fitting today's prompts is not enough to admit tomorrow's work.

Compare raw weight sizes at 16, 8 and 4 bits, then budget KV cache and runtime memory for Llama 3.1 70B. Follow the worked per-GPU example.

Work from token timestamps to first-token wait, average pace and individual stalls. Then count requests meeting both latency limits, keeping failures and measurement boundaries explicit.

Draw query-head sharing, count actual stored state and compare payloads with explicit model geometry and binary units.

Work from training FLOPs to fleet throughput, days and GPU-hours. Then see how utilization and downtime change the estimate.

Use the Llama 3 interruption record without turning job interruptions into device lifetime, predictable failures or a universal spare percentage.

Use a flexible 45-minute plan to connect requirements, a request path, capacity arithmetic, selected deep dives and a clear closing decision.

Trace one cache position through a block table, calculate the memory recovered from oversized reservations, and explain why sharing still needs careful writes and matching.

Separate weight storage, matrix arithmetic and attention-cache precision. Choose a supported candidate using task quality, memory and service measurements.

Handle an inaccessible GPU with bounded recovery and preserved evidence, without treating a reboot or repeat count as proof of root cause.