NVIDIA AI Quiz

Answers and explanations appear as you take the quiz.

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