Quiz lié à un guide · Difficile Niveau

Quiz sur l'optimisation de l'inférence IA

Teste le réglage axé sur les mesures, le réalisme de la charge de travail, les compromis en matière de traitement par lots, l'étendue du cache, l'impact sur la précision et la qualité des tâches terminées.

Chemins de guidage associésOptimisation de l'inférence IA
Question 1 de 8

Why measure end-to-end performance before optimizing a model?

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Les réponses et les explications apparaissent au fur et à mesure que vous répondez au quiz.

  1. Why measure end-to-end performance before optimizing a model?
    • Every workload is model-compute bound
    • Optimization always improves task quality
    • A short warm benchmark represents production use
    • The bottleneck may be retrieval, tokenization, transfer, or output handling rather than inference
  2. What can batching improve, and what can it worsen?
    • Throughput may improve while individual latency rises as requests wait for a batch
    • Both always improve together
    • Memory always decreases
    • Task accuracy is guaranteed to increase
  3. When is a cached model response safe to reuse?
    • Whenever the request text looks similar
    • When the key accounts for relevant input, model version, permissions, and context
    • When the cache is stored privately
    • Only when the answer is short
  4. What should be checked after reducing numerical precision?
    • Only whether inference became faster
    • Only the model’s file size
    • Task quality on the same representative evaluation set as the original configuration
    • Whether the GPU supports the format
  5. A benchmark uses one warmed-up short request. Why may it mislead?
    • It contains too many user cases
    • A warm request is never measurable
    • It evaluates the wrong model weights by definition
    • It omits cold starts, concurrency, long requests, and cancellation behavior
  6. What does time to first token measure compared with total completion time?
    • The initial streaming delay versus the time to finish the response
    • Two names for exactly the same metric
    • Model accuracy versus calibration
    • Queue depth versus cache hit rate
  7. Why tune one meaningful factor at a time?
    • It guarantees no regression
    • It helps attribute improvements and regressions to the change made
    • It removes the need for a baseline
    • It makes different workloads comparable automatically
  8. What is the optimization objective in the guide?
    • The highest isolated throughput number
    • The lowest latency on a single test
    • A better completed task within the required quality and resource constraints
    • The fewest model parameters regardless of quality