NCA-GENL Core Machine Learning and AI Knowledge Practice Question
A team is deploying a large language model for real-time text generation and notices that inference latency is too high. They want to reduce latency without retraining the model. Which technique is most appropriate?
⚠ Common exam trap
Many candidates confuse throughput with latency; increasing batch size improves throughput but can worsen latency.
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
✓
Apply quantization to reduce the model's precision from FP32 to FP16 or INT8.
Quantization reduces the precision of model parameters, which lowers memory bandwidth and speeds up computation. It can be applied without retraining and is a common technique for reducing inference latency. Other options either increase latency or do not affect it. Thus, quantization is the most appropriate choice.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the batch size for inference requests.
Why it's wrong here
Increasing batch size can improve throughput but typically increases latency for individual requests because the model processes more examples simultaneously. For real-time generation, lower latency per request is desired, so larger batches are counterproductive. Batching is better for offline or high-throughput scenarios, not for minimizing latency.
- ✗
Use a larger model with more parameters.
Why it's wrong here
A larger model would increase computational cost and memory usage, making inference slower, not faster. The goal is to reduce latency, so increasing model size is the opposite of what is needed. Larger models may improve quality but at the expense of speed, which is not acceptable for real-time deployment.
- ✓
Apply quantization to reduce the model's precision from FP32 to FP16 or INT8.
Why this is correct
Quantization reduces the numerical precision of weights and activations, which decreases memory bandwidth and computational requirements, leading to faster inference. It can be applied post-training without retraining. This directly addresses latency by making the model smaller and faster to execute, while often maintaining acceptable accuracy.
- ✗
Train the model for additional epochs to improve its efficiency.
Why it's wrong here
Training longer does not inherently make inference faster; it may even lead to overfitting. The model's architecture and precision determine inference speed, not the number of training epochs. Retraining is also costly and not necessary for latency reduction. This option does not address the core issue of inference efficiency.
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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.