TensorRT and Inference Engines Quiz

Answers and explanations appear as you take the quiz.

1. What is the goal of building an inference engine with TensorRT?
   • Guarantee the fastest possible execution on every system
   • Optimize execution of a supported trained model for the configured target
   • Choose training labels without a dataset
   • Replace every GPU with a CPU
2. How does layer fusion speed up inference?
   • By merging multiple operations into one GPU kernel to reduce memory traffic
   • By deleting layers that are not needed
   • By running each layer on a separate GPU
   • By converting the model to run on the CPU
3. In a representative-data INT8 calibration workflow, why examine the model’s value distributions?
   • To guarantee no accuracy change under all future inputs
   • To retrain the model from zero automatically
   • To select quantization scales suited to the observed numerical ranges
   • To remove every compatibility requirement
4. Why is a compiled TensorRT engine generally specific to one GPU architecture?
   • Because it is encrypted to that GPU's serial number
   • Because NVIDIA licenses it per device
   • Because it stores the training data inside the engine
   • Because kernel auto-tuning bakes in the fastest kernels for that GPU's exact core and memory layout
5. Which feature of TensorRT-LLM specifically helps serve large language models efficiently?
   • In-flight (continuous) batching and paged KV-cache
   • Automatic image augmentation
   • Built-in web hosting
   • Audio noise cancellation
6. Why evaluate task accuracy after choosing lower-precision TensorRT execution?
   • Lower precision always preserves every output bit
   • Only the model filename affects predictions
   • Reduced numerical precision can change model outputs or quality
   • Accuracy evaluation is needed only during tokenization
7. A TensorRT dynamic-shape engine receives input outside its configured profile range. What should deployment validation check?
   • Whether the engine can accept every shape without a profile
   • Only whether the model name is short
   • Whether training can restart from a screenshot
   • Whether the profile supports that requested shape
8. A TensorRT engine is moved to a different runtime and GPU configuration. What must the team establish?
   • That any serialized engine runs unchanged everywhere
   • That the selected compatibility settings support the new environment
   • That the engine contains all its original training data
   • That every GPU shares one serial number