Exam NCA-GENM Topic 1 Question 75 Discussion

Actual exam question for NVIDIA's NCA-GENM exam
Question #: 75
Topic #: 1

You are tasked with optimizing a multimodal A1 model that processes both images and text. You observe significant latency during the image encoding phase using a pre-trained ResNet50 model. Which of the following techniques would be MOST effective in reducing latency while preserving accuracy, considering energy efficiency?

A. Increase the batch size for image processing. B. Apply knowledge distillation, training a smaller, faster model to mimic the ResNet50 output. C. Replace ResNet50 with a larger, more complex model like ResNeXt101. D. Use full precision floating point operations throughout the ResNet50 model. E. Disable GPU acceleration for image processing to reduce power consumption.

Suggested Answer: B Vote an answer

Knowledge distillation involves training a smaller, more efficient model to approximate the behavior of a larger, more accurate model. This can significantly reduce latency without a major drop in accuracy. Increasing batch size (A) may increase throughput but doesn't necessarily reduce latency per image. Replacing with a larger model (C) will increase latency and power consumption. Using full precision (D) is less energy-efficient than using mixed precision or quantization. Disabling GPU acceleration (E) would drastically increase latency.

by Gill at Dec 03, 2025, 02:38 PM

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