Exam NCA-GENM Topic 1 Question 245 Discussion

Actual exam question for NVIDIA's NCA-GENM exam
Question #: 245
Topic #: 1

You're developing a real-time video captioning system. Latency is critical. Which of the following optimization strategies would provide the MOST significant reduction in end-to-end latency, assuming the captioning model is already optimized for inference?

A. Using a smaller batch size for inference. B. Employing model quantization (e.g., converting weights from FP32 to INT8). C. Overlapping image feature extraction and caption generation using asynchronous execution. D. Caching the most frequent words in a local vocabulary. E. All of the above.

Suggested Answer: E Vote an answer

While all listed strategies contribute to reducing latency, Option C - overlapping image feature extraction and caption generation - offers a substantial reduction by parallelizing these typically sequential processes. It leverages asynchronous execution to minimize idle time. Smaller batch sizes (A) and Model Quantization (B) would directly improve model inference. Caching frequent words (D) could reduce lookup time during caption generation. However, the overlapping approach provides a more significant change by improving concurrency. E is a combination of all of the above which leads to best optimized model.

by Kristin at Mar 01, 2026, 11:23 AM

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