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Exam NCA-AIIO Topic 1 Question 32 Discussion

Actual exam question for NVIDIA's NCA-AIIO exam
Question #: 32
Topic #: 1
Your AI team is running a distributed deep learning training job on an NVIDIA DGX A100 clusterusing multiple nodes. The training process is slowing down significantly as the model size increases. Which of the following strategies would be most effective in optimizing the training performance?

Suggested Answer: A Vote an answer

Enabling Mixed Precision Training is the most effective strategy to optimize training performance on an NVIDIA DGX A100 cluster as model size increases. Mixed precision uses lower-precision data types (e.g., FP16) alongside FP32, reducing memory usage and leveraging Tensor Cores on A100 GPUs for faster computation without significant accuracy loss. This approach, detailed in NVIDIA's "Mixed Precision Training Guide," accelerates training by allowing larger models to fit in GPU memory and speeding up matrix operations, addressing slowdowns in distributed setups.
Data parallelism (B) distributes data but may not help if memory constraints slow computation. Decreasing nodes (C) reduces parallelism, worsening performance. Increasing batch size (D) can strain memory further, exacerbating slowdowns. NVIDIA's DGX A100 documentation highlights mixed precision as a key optimization for large models.

by Claude at Jan 02, 2026, 07:15 AM

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