Тест, свързан с ръководство · лесно Ниво

Тест за NVIDIA AI

Обхваща NVIDIA роли в екосистемата, оптимизиране на изводите, съвместимост, валидиране на изхода и измерване на производителността от край до край.

Свързани ръководни пътекиNvidia AI
Въпрос 1 на 8

A request spends 100 ms on inference and 900 ms preparing data. If inference becomes twice as fast, about how long does the request take?

Визуализирайте всеки въпрос8 въпроси

Отговорите и обясненията се появяват, докато отговаряте на теста.

  1. A request spends 100 ms on inference and 900 ms preparing data. If inference becomes twice as fast, about how long does the request take?
    • 950 ms
    • 500 ms
    • 900 ms
    • 450 ms
  2. In the NVIDIA deployment workflow described by the guide, what role can an inference optimizer such as TensorRT play?
    • Replace the need to evaluate output quality
    • Optimize a trained model for execution before it is exposed through a serving system
    • Train every model from raw data automatically
    • Store all application data without a runtime
  3. A model is fast on GPU but the application remains slow because input preparation dominates. What should the team investigate?
    • Increasing model parameter count
    • Changing the model vendor without profiling
    • The full data and preprocessing path, not only accelerator throughput
    • Removing output validation
  4. After changing precision or the inference execution path, what should be compared with the original model?
    • Only the file extension
    • Only the GPU temperature
    • Whether the model has the same marketing name
    • Task outputs and quality on representative evaluation inputs
  5. Which details help make an inference benchmark reproducible?
    • Hardware, software versions, precision, supported operations, workload and input conditions
    • Only the vendor name
    • Only the model’s parameter count
    • The color of the server chassis
  6. Which statement about a larger accelerator is supported by the guide?
    • It guarantees every application runs faster
    • It may not improve end-to-end speed when another stage is the bottleneck
    • It removes all data transfer costs
    • It ensures output quality is unchanged
  7. Which cost dimensions does the guide recommend measuring for a deployed AI workload?
    • Only peak theoretical arithmetic throughput
    • Only model accuracy on training data
    • Latency, throughput, power and cost under the intended application conditions
    • Only the accelerator’s name
  8. Why does the guide separate hardware capability from software-stack performance?
    • Software never affects accelerator performance
    • Hardware specifications directly guarantee service speed
    • All NVIDIA devices execute every model identically
    • Supported operations, kernels, and compatibility determine how hardware is actually used