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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You need to determine the optimal data processing library for a small dataset of 500,000 records that will be processed on a multi-core CPU machine with no GPU access.
Which of the following libraries would be the most efficient for this task?

A) Dask
B) PyTorch
C) pandas
D) cuDF

2. A data science team is developing a machine learning pipeline requiring specific CUDA, cuDNN, and RAPIDS versions for compatibility across environments. They need a framework to manage dependencies and version conflicts.
Which approach is best for managing software dependencies using NVIDIA technologies?

A) Using a single system-wide installation of CUDA and forcing all projects to use the same version
B) Manually installing each package and its dependencies using pip
C) Using Conda with NVIDIA Conda channels to manage CUDA and cuDNN dependencies
D) Using only virtual environments (venv) without managing GPU dependencies separately

3. A machine learning engineer is tasked with deploying a real-time image classification model as part of an MLOps pipeline. The model requires low-latency inference and must handle high-throughput requests efficiently.
Which of the following deployment strategies is the most suitable for leveraging GPU acceleration?

A) Running the model inference on a multi-core CPU server with batch processing enabled.
B) Deploying the model using NVIDIA Triton Inference Server with TensorRT optimizations.
C) Using a REST API wrapper to load the model dynamically into memory for each request.
D) Using an Apache Spark cluster with distributed CPU-based inference.

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

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

5. Which of the following tools can be used for profiling deep learning models to identify performance bottlenecks and optimize execution on NVIDIA GPUs? (Select two)

A) TensorBoard
B) Python's cProfile
C) DLProf
D) NVIDIA Nsight Systems

Solutions: