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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. Which of the following tools in the NVIDIA AI stack is specifically designed to accelerate the deployment of machine learning models for production by optimizing inference performance?

A) cuML
B) DLA (Deep Learning Accelerator)
C) cuBLAS
D) Triton Inference Server

2. You are analyzing a large-scale transportation network using cuGraph and notice that query times are longer than expected when running graph algorithms.
What is the best way to optimize graph processing performance using GPU-accelerated tools?

A) Convert the graph to CSR (Compressed Sparse Row) format before running computations to improve memory efficiency.
B) Use cugraph.filter_unconnected_nodes() to remove unconnected nodes before processing.
C) Store the graph in COO (Coordinate List) format instead of CSR (Compressed Sparse Row) format for faster traversal.
D) Use cugraph.to_directed() to convert the graph into a directed format, which improves GPU parallelism.

3. You are setting up a GPU-accelerated data science environment that includes NVIDIA RAPIDS, PyTorch, TensorFlow, and other libraries for machine learning and data processing.
Given that these frameworks have different dependencies and version requirements, what is the best approach to avoid software conflicts while ensuring reproducibility across multiple environments?

A) Install all packages globally using pip on the system-wide Python installation to ensure consistency.
B) Manually download and compile each library from source to guarantee compatibility across all versions.
C) Use a single Docker container with the latest versions of all dependencies installed system-wide.
D) Use Conda to create isolated virtual environments for each project and install dependencies via conda-forge or NVIDIA channels.

4. You are a data scientist working on a large-scale deep learning project that requires significant computational resources. You have the option to run your workloads on a cloud-based GPU instance.
Which of the following statements best describes a key benefit of using cloud-based GPUs for your workload?

A) Cloud-based GPUs eliminate all data transfer bottlenecks and latencies when training models on large datasets.
B) Cloud-based GPUs enable scalable resource allocation, allowing you to dynamically increase or decrease GPU instances as needed.
C) Cloud-based GPUs provide consistent and predictable performance, identical to on-premise dedicated GPUs.
D) Cloud-based GPUs are always more cost-effective than on-premise GPUs, regardless of workload size and duration.

5. You are working with a large dataset containing millions of rows, and you need to store it efficiently for fast read and write operations while maintaining compatibility with CuDF and pandas.
Which of the following file formats is the best choice for efficient columnar storage and GPU-accelerated processing?

A) TXT
B) JSON
C) Parquet
D) CSV

Solutions: