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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. When performing benchmarking and optimization for GPU-accelerated workflows, which of the following tools is best suited for analyzing the memory utilization and computational efficiency of deep learning models running on Nvidia GPUs?

A) Nvidia CUDA Profiler
B) Nvidia Nsight Compute
C) Nvidia Riva
D) Nvidia TensorRT

2. Which of the following techniques are commonly used to identify and acquire datasets for machine learning and data science projects? (Select three)

A) Crowdsourcing
B) Web scraping
C) Manual data entry
D) Data augmentation
E) Public APIs

3. A machine learning engineer is training a large transformer-based model for natural language processing (NLP). They want to maximize training speed and efficiency using NVIDIA GPUs.
Which of the following techniques would most effectively enhance GPU utilization and reduce training time?

A) Running training exclusively on CPU
B) Using mixed-precision training with Tensor Cores
C) Prefetching data with the CPU while training on the GPU
D) Disabling data parallelism

4. You are training a machine learning model using scikit-learn-like API on a dataset with millions of samples and thousands of features. You need to optimize both training time and inference speed using NVIDIA technologies.
Which solution is the most appropriate?

A) Use NVIDIA RAPIDS cuML for GPU-accelerated machine learning model training.
B) Use NVIDIA Magnum IO to optimize machine learning model parameters on the GPU.
C) Use NVIDIA Triton Inference Server to train the model efficiently on a single GPU.
D) Use NVIDIA Modulus to accelerate machine learning training and feature selection.

5. Which of the following Nvidia technologies is commonly used for deploying machine learning models in production environments, enabling scalable deployment and monitoring?

A) Nvidia Triton Inference Server
B) Nvidia Jetson Nano
C) Nvidia CUDA
D) Nvidia DeepStream

Solutions: