Maswali yanayounganishwa na mwongozo · Rahisi Kiwango

Maswali ya AI ya NVIDIA

Inashughulikia majukumu ya mfumo ikolojia wa NVIDIA, uboreshaji wa makisio, uoanifu, uthibitishaji wa matokeo, na kipimo cha utendaji cha mwisho hadi mwisho.

Njia za mwongozo zinazohusianaNvidia AI
Swali 1 ya 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?

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  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