Demo NVIDIA NCP-ADS Exam Questions

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Question 1

You are training a large-scale random forest model on a dataset with millions of rows and hundreds of features. The training time is significantly high when using traditional CPU-based machine learning frameworks. Which NVIDIA technology should you use to accelerate training while maintaining compatibility with common ML frameworks like scikit-learn?

A

NVIDIA DeepStream to preprocess tabular data and optimize random forest model execution.

B

NVIDIA RAPIDS cuML to accelerate random forest training using GPU-optimized implementations.

C

NVIDIA Triton Inference Server to distribute random forest model training across multiple GPUs.

D

NVIDIA TensorRT to accelerate random forest model training by optimizing tree-based algorithms.

Correct Answer: B

Question 2

A data scientist is training a deep learning model on an NVIDIA GPU but is encountering out-ofmemory (OOM) errors. To optimize GPU memory usage while maintaining efficient training performance, which of the following strategies should they prioritize?

A

Increasing batch size without adjusting the optimizer settings.

B

Using mixed precision training with automatic loss scaling.

C

Storing all training data in GPU memory at once.

D

Using single-precision (FP32) calculations for better accuracy.

Correct Answer: B

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