NVIDIA NCP-ADS

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are working on a large-scale machine learning workload that involves training a deep learning model using multiple GPUs. You want to leverage Dask to implement data parallelism efficiently using NVIDIA GPUs.
Which of the following approaches best achieves data parallelism in this context?

A) Leverage Dask-CUDA to automatically assign computations to available GPUs using the worker pool
B) Use Dask DataFrame to parallelize deep learning model training across multiple GPUs
C) Use Dask with CuPy to distribute NumPy-based computations across multiple GPUs
D) Run a single large Dask task on the CPU and use Dask-MPI for multi-GPU execution

2. Which of the following best describes a key advantage of using cloud-based GPU instances for machine learning model training?

A) Cloud GPU instances cannot support containerized workloads, limiting their applicability for MLOps and CI/CD pipelines.
B) Cloud-based GPU instances offer lower latency and better network performance compared to on- premise deployments, regardless of geographical location.
C) Cloud GPUs are always more cost-effective than on-premise GPUs, as they do not incur long-term usage costs.
D) Cloud GPUs provide dynamically scalable resources, allowing users to increase or decrease compute power based on demand without upfront hardware investment.

3. You are working with a large dataset containing 500 million records stored as a parquet file. Your task is to perform data filtering, aggregation, and transformation as efficiently as possible.
Which of the following approaches would be the best choice for accelerated data manipulation using NVIDIA technologies?

A) Convert the dataset to a NumPy array and use standard Python loops
B) Use RAPIDS cuDF to load, filter, and transform the dataset on the GPU
C) Use multiprocessing in Python to parallelize pandas operations across CPU cores
D) Load the dataset into pandas and use apply() for transformations

4. A data science team wants to deploy a GPU-accelerated pipeline using cuGraph to analyze graph data on cloud infrastructure. They are evaluating different cloud-based GPU solutions.
Which of the following factors should they consider when selecting a cloud-based GPU instance for running cuGraph efficiently?

A) Cloud-based GPUs are only useful for rendering graphics, not for running cuGraph algorithms.
B) cuGraph runs equally well on CPU-based virtual machines, making GPU instances unnecessary.
C) The availability of NVIDIA CUDA-enabled GPUs, as cuGraph requires CUDA for acceleration.
D) The choice of GPU instance does not affect cuGraph performance since all GPUs execute graph algorithms at the same speed.

5. What is the primary advantage of using NVIDIA Triton Inference Server for deploying and monitoring machine learning models in production?

A) It only supports TensorFlow models for inference.
B) It provides GPU optimization to handle high-throughput inference workloads.
C) It automatically tunes hyperparameters for all models.
D) It is designed solely for edge devices and not for data centers.

Solutions: