NCA-GENL Experimentation Practice Question
A researcher is using NVIDIA's 'TensorRT-LLM' to optimize an LLM. During the experimentation phase, they observe the model's accuracy drops significantly after quantization. What is the most appropriate next step?
⚠ Common exam trap
Candidates often suggest re-training the whole model or changing the architecture. They overlook the standard, less compute-intensive solution of using calibration data for quantization adjustment.
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 a representative calibration dataset for quantization.
Post-training quantization often introduces errors that degrade model accuracy. To mitigate this, techniques like 'Quantization-Aware Training' (QAT) or using a calibration dataset are essential. These methods help the model adapt to the lower precision format during or after the process. Mastering these techniques is critical for delivering high-performance, resource-efficient models that maintain their accuracy in production environments.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Revert to full FP32 training without any optimization.
Why it's wrong here
Reverting to FP32 is a fallback, not a solution to the optimization problem. While it restores accuracy, it ignores the goal of making the model efficient for deployment. The experiment should focus on finding a quantization approach that balances performance and resource savings rather than abandoning optimization.
- ✗
Increase the model's hidden dimension size.
Why it's wrong here
Increasing the model's hidden dimension size significantly increases the memory footprint and compute requirements. It does not address the loss of precision caused by quantization; instead, it complicates the model further, making quantization even more difficult to manage without losing accuracy on the target task.
- ✓
Use a representative calibration dataset for quantization.
Why this is correct
Quantization parameters are often determined by the distribution of activation values. Using a representative calibration dataset allows the algorithm to estimate the optimal scale and zero-point values more accurately, which significantly reduces the performance degradation that typically occurs when models are converted to lower precision.
- ✗
Switch to a smaller base model architecture.
Why it's wrong here
Switching to a smaller model changes the entire baseline of the experiment. If the original model was chosen for its performance, moving to a smaller one will likely lead to even lower accuracy. The researcher should first attempt to solve the quantization issue with the existing architecture.
About these practice questions
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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 NCA-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 NCA-GENL exam.