NCP-GENL Model Optimization Practice Question
Network Topology
Refer to the exhibit. An engineer notices that the TensorRT engine takes an excessively long time to build. What is the most likely cause, and how can it be mitigated?
⚠ Common exam trap
Candidates often guess that the model is too large or the GPU is underpowered, missing the fact that TensorRT's exhaustive search for optimization tactics is the primary cause of slow build times.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use an explicit tactic sources list
The long build time is likely due to the large workspace allocation combined with a large search space for the optimization tactics. TensorRT tests a variety of kernel implementations to find the fastest one. To reduce build time, the engineer can limit the 'tactic selection' or use a profile-based build where common shapes are pre-recorded, preventing the engine from exhaustively searching every possible configuration for all input shapes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Decrease the workspace size
Why it's wrong here
Decreasing the workspace size might speed up the build but could lead to suboptimal engine performance. If the workspace is too small, TensorRT cannot allocate the memory required to test certain high-performance kernels, resulting in a less efficient execution engine. This is not the recommended way to solve build time.
- ✗
Use a calibration cache
Why it's wrong here
A calibration cache is used for INT8 quantization to avoid re-calculating the scaling factors. While it speeds up the quantization process, it does not address the time spent during the kernel search phase of the TensorRT engine build, which is where the bulk of the build time is spent.
- ✓
Use an explicit tactic sources list
Why this is correct
Specifying the tactic sources allows the builder to skip certain search paths or limit the number of kernels it tests. By narrowing the scope of the tactic search, the build process completes significantly faster while still producing a highly optimized engine that utilizes the target GPU's capabilities effectively.
- ✗
Increase the batch size
Why it's wrong here
Increasing the batch size would force TensorRT to evaluate more scenarios, which would make the build process significantly slower, not faster. The search space for optimized kernels grows with the input complexity, so larger batch sizes should be avoided unless strictly necessary for the final production deployment.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official NVIDIA exam blueprint
This NCP-GENL practice question is part of Courseiva's free NVIDIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the NCP-GENL exam.