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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. A machine learning engineer wants to deploy a GPU-accelerated inference model in a containerized environment while ensuring compatibility with NVIDIA libraries.
Which of the following is the best approach for managing dependencies?

A) Install GPU drivers directly inside the container instead of on the host system to avoid dependency conflicts.
B) Run Docker containers without any special configurations, as Docker automatically detects and utilizes GPUs.
C) Disable the --gpus flag when running Docker containers, as RAPIDS AI libraries do not require explicit GPU selection.
D) Use the official NVIDIA Docker base images (nvidia/cuda) and install RAPIDS AI libraries within the container to ensure GPU compatibility.

2. Which NVIDIA technology is specifically designed for accelerating deep learning workloads in the cloud?

A) NVIDIA Jetson
B) TensorRT
C) NVIDIA Tesla
D) NVIDIA A100

3. You are working on a medium-sized dataset (~500,000 rows, 20 columns) and need to perform fast exploratory data analysis (EDA) with filtering, aggregations, and transformations.
Which of the following Python libraries would be the most efficient choice for this task?

A) Vaex
B) Pandas
C) PySpark
D) Dask

4. You need to train a deep learning model using PyTorch on a dataset too large for a single GPU. You decide to use Dask with NVIDIA GPUs for multi-GPU scaling.
Which approach is the most effective for distributing the workload?

A) Use Dask's built-in deep learning API to automatically distribute PyTorch models across GPUs
B) Use Dask Bag to shard the dataset and train separate PyTorch models on each shard
C) Use Dask-CUDA workers with PyTorch's DistributedDataParallel (DDP) for training across multiple GPUs
D) Use Dask.delayed to wrap PyTorch training functions and schedule them across multiple GPUs

5. You are working on a large-scale graph analysis project using NVIDIA cuGraph for accelerated computations. Your dataset consists of millions of nodes and edges representing social network interactions. You need to efficiently compute PageRank while minimizing memory usage.
Which of the following techniques would be the most effective?

A) Manually unroll loops and precompute node ranks in a NumPy array before feeding them into cuGraph.
B) Use cuGraph's sparse matrix representation to store graph data and perform computations efficiently.
C) Use Pandas DataFrames to manage the graph structure and leverage Pandas indexing for efficient queries.
D) Convert the graph into an adjacency list format and store it in Python dictionaries for faster lookups.

Solutions: