The team wants more caching, but personalization and freshness make cache correctness hard. How would you decide what to cache?

Instruction: Explain how you would balance cache value against correctness risk.

Context: Tests how the candidate diagnoses the problem, chooses the safest next step, and reasons through recovery. Explain how you would balance cache value against correctness risk.

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I would cache only the layers whose validity I can explain clearly. That might be retrieval candidates, prompt fragments, deterministic transformations, or stable non-personalized answers, while leaving the truly personalized or freshness-sensitive synthesis path uncached.

I like decomposing...

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Your preparation path

Work through these questions in order. Read the answer aloud, then explain it in your own words.

1. Start with the foundations

Build the vocabulary and explain the core decisions.

  1. What drives latency in an LLM application besides the model itself? Free sample
  2. How would you explain batching to a product engineer? Free sample
  3. How would you explain throughput versus latency in model serving? Free sample

2. Apply it to a real workflow

Practice diagnosis, validation, and everyday tradeoffs.

  1. What signals matter most when optimizing cost without hiding quality loss? Free sample
  2. Traffic spikes create queueing even though the model service itself is healthy. How would you investigate? Member answer
  3. The product is fast on short prompts and unstable on long tool-using requests. What would you inspect? Member answer

3. Prepare for senior discussions

Explain failure boundaries, recovery, and production choices.

  1. How would you reason about tail latency in a multi-step LLM workflow? Member answer
  2. Long prompts stall active LLM streams. Would you separate prefill and decoding? Member answer
  3. How is KV-cache quantization different from weight quantization, and how would you validate it? Member answer

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