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Precise predication of the real exam

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

2. A data scientist is working with large-scale tabular datasets and wants to optimize data ingestion and storage for accelerated processing on NVIDIA GPUs. The scientist is considering different file formats and storage optimizations to maximize performance in a RAPIDS-based workflow.
Which of the following approaches is the most suitable for optimizing both storage and processing performance?

A) Load data directly into NumPy arrays before using RAPIDS cuDF for processing.
B) Convert datasets into CSV format and store them in local disk storage.
C) Use JSON format for easy readability and process it directly in cuDF.
D) Store data in Parquet format and load it using cuDF in RAPIDS.

3. You are working with a large-scale social network dataset and need to analyze relationships between users to detect communities using the Louvain algorithm. Given the benefits of GPU acceleration, you decide to use cuGraph for this task.
Which of the following statements best describes why cuGraph is beneficial for this workload?

A) Running graph algorithms on a GPU leads to higher latency due to the overhead of data movement between CPU and GPU.
B) cuGraph primarily accelerates deep learning tasks and is not optimized for graph-based algorithms.
C) cuGraph enables parallel computation on the GPU, significantly speeding up community detection tasks compared to traditional CPU-based implementations.
D) cuGraph does not support weighted graphs, making it less useful for real-world social network analysis.

4. You need to deploy a containerized machine learning model that utilizes NVIDIA GPUs on a cloud- based Kubernetes cluster.
Which of the following steps is essential for ensuring proper GPU utilization inside a Docker container?

A) Use a standard Python-based container image instead of an NVIDIA GPU-optimized image
B) Use the NVIDIA Container Toolkit (nvidia-docker2) to allow GPU access within Docker containers
C) Install GPU drivers inside the container to ensure access to the host's hardware
D) Run the container using the default Docker runtime without any additional configurations

5. You are building an MLOps pipeline for a predictive model that uses tabular data with both categorical and numerical features.
To ensure efficient data processing and optimal model training on an NVIDIA GPU, which of the following data types would be most suitable for a categorical feature representing different product categories?

A) String
B) Int32
C) Float64
D) Int64

Solutions: