Exam NCA-GENM Topic 1 Question 282 Discussion

Actual exam question for NVIDIA's NCA-GENM exam
Question #: 282
Topic #: 1

You are tasked with optimizing a U-Net model for real-time image segmentation on an embedded device with limited GPU memory. The original model is trained in FP32 precision. Which of the following techniques, when applied together, would likely yield the best trade-off between accuracy and performance?

A. Quantization Aware Training (QAT) to INT8, Knowledge Distillation from the FP32 model to a smaller student model, and channel pruning to reduce the number of filters. B. FP16 mixed-precision training, layer fusion to combine multiple operations into one, and increasing the batch size to improve GPU utilization. C. Weight clustering to reduce model size, pruning low-importance connections, and using a larger learning rate during fine-tuning. D. Converting all layers to FP16, removing skip connections from the IJ-Net architecture, and using a smaller input image resolution. E. Applying standard post-training quantization to INT8, replacing convolutional layers with fully connected layers, and using a smaller batch size.

Suggested Answer: A Vote an answer

For embedded devices, aggressive optimization is needed. QAT to INT8 provides significant memory and performance gains, but requires retraining. Knowledge distillation allows training a smaller, faster student model to mimic the original model's behavior, and channel pruning reduces computational cost by removing less important filters. The combination provides the best trade-off. FP16 (B) helps but isn't as aggressive as INT8. Increasing batch size (B) might not be feasible given limited memory. Removing skip connections (D) drastically hurts accuracy. Fully connected layers (E) increase the number of parameters.

by Hogan at Oct 08, 2025, 05:23 PM

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