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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

Question #1
You are designing a reproducible benchmark to compare the performance of deep learning models across frameworks like PyTorch and TensorFlow using NVIDIA's A100 GPU.
Which step is most critical in ensuring fair benchmarking conditions?

A. Measuring only forward pass latency to compare inference speed while ignoring backward pass computation.
B. Ensuring the same CUDA/cuDNN and driver versions are installed when running benchmarks across frameworks.
C. Enabling XLA compiler optimizations only for TensorFlow to enhance its performance.
D. Using a different precision setting for each framework to maximize performance per framework's capabilities.

Question #2
You are working on a data science project using NVIDIA RAPIDS on a multi-GPU system.
To ensure reproducibility and avoid software versioning conflicts, which of the following is the best approach for managing dependencies?

A. Use a manually compiled CUDA installation alongside system-installed Python libraries to manage GPU dependencies.
B. Avoid dependency management frameworks and rely on manual tracking of package versions using a text file.
C. Install all required packages globally on the system using pip install without a virtual environment.
D. Use a Conda environment with RAPIDS-compatible versions of libraries installed using conda install
-c rapidsai -c nvidia.

Question #3
You are preprocessing a dataset using NVIDIA RAPIDS cuDF and need to handle missing values in the column temperature by replacing them with the column's median value.
Which of the following approaches correctly achieves this in an optimized manner?

A. df['temperature'].dropna(inplace=True)
B. 1. df['temperature'] = df['temperature'].map(2. lambda x: df['temperature'].median() if x is None else x
3.)
C. df['temperature'].fillna(df['temperature'].mean(), inplace=True)
D. df['temperature'].fillna(df['temperature'].median(), inplace=True)

Question #4
You need to set up an isolated, GPU-accelerated environment for a deep learning project that requires specific CUDA, cuDNN, and RAPIDS versions.
Which of the following best ensures a reproducible environment using Docker?

A. Install NVIDIA drivers manually inside a Docker container every time it runs.
B. Build a container from an Ubuntu base image and manually install all dependencies without specifying versions.
C. Use the nvidia/cuda base image and specify the required RAPIDS and deep learning libraries in a Dockerfile.
D. Use a system-wide CUDA installation and mount the /usr/local/cuda directory into the container to provide GPU support.

Question #5
A team of data scientists needs to deploy a machine learning model that depends on specific versions of CUDA and TensorFlow, ensuring it runs consistently across different machines without manually configuring each system.
Which of the following approaches best ensures consistency while leveraging NVIDIA GPUs?

A. Compiling all dependencies into the host machine and using system-wide installations
B. Using NVIDIA Docker (nvidia-docker) to containerize the model and manage GPU dependencies
C. Using Docker without GPU support and relying on CPU fallback when running TensorFlow
D. Running the model in a local Python virtual environment and copying dependencies manually

Solutions: