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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

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

2. A team is processing large-scale tabular data using cuDF and cuML on NVIDIA GPUs but is facing performance degradation.
Which of the following techniques would be the most effective in identifying and resolving bottlenecks in the pipeline?

A) Increase GPU clock speed manually to force higher processing power.
B) Use nvprof or nsight compute to analyze kernel execution time and memory transfer efficiency.
C) Reduce the dataset size to a smaller sample to speed up processing.
D) Convert all datasets into pandas DataFrames for initial processing before moving to cuDF.

3. A data science team is using NVIDIA GPUs to accelerate their AI workflow for a fraud detection system. They follow the CRISP-DM methodology to ensure a structured approach to the project.
During the Data Preparation phase, what is the most effective way to leverage NVIDIA technologies?

A) Perform all data transformation on a single CPU core to ensure stability before deploying to GPU- accelerated training.
B) Use NVIDIA RAPIDS to preprocess large-scale transaction datasets efficiently on GPUs before training the model.
C) Ignore feature engineering and feed raw data directly into the model, relying on deep learning to extract relevant features.
D) Manually clean and transform data using traditional CPU-based processing tools like pandas, then transfer the data to the GPU only for training.

4. 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) Use a Conda environment with RAPIDS-compatible versions of libraries installed using conda install
-c rapidsai -c nvidia.
D) Install all required packages globally on the system using pip install without a virtual environment.

5. You need to deploy a machine learning model on a GPU-equipped system. The GPU has 16GB of VRAM, and the model requires approximately 12GB of memory during inference. However, additional system processes and other applications consume 5GB of VRAM.
What would happen if you attempt to run inference without making any optimizations, and how should you resolve the issue?

A) The model will run successfully but with reduced performance due to memory fragmentation
B) The model will fail to run due to out-of-memory (OOM) errors, and using a smaller batch size can help reduce memory usage
C) The model will run without issues because 16GB of VRAM is sufficient for a 12GB model
D) Switching from a GPU to CPU inference will resolve memory issues without performance loss

Solutions: