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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

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

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

3. You are developing an end-to-end data pipeline that processes terabytes of image metadata using NVIDIA technologies. You need a software stack that efficiently integrates GPU-accelerated data processing, machine learning, and visualization.
Which of the following tool combinations is best suited for this task?

A) Hadoop for data storage, NumPy for computations, and TensorFlow for visualization.
B) cuDF for data manipulation, cuML for machine learning, and Plotly for visualization.
C) Pandas for data manipulation, XGBoost for machine learning, and Matplotlib for visualization.
D) Excel for data analysis, scikit-learn for machine learning, and Seaborn for visualization.

4. A data science team wants to deploy a GPU-accelerated pipeline using cuGraph to analyze graph data on cloud infrastructure. They are evaluating different cloud-based GPU solutions.
Which of the following factors should they consider when selecting a cloud-based GPU instance for running cuGraph efficiently?

A) cuGraph runs equally well on CPU-based virtual machines, making GPU instances unnecessary.
B) The choice of GPU instance does not affect cuGraph performance since all GPUs execute graph algorithms at the same speed.
C) Cloud-based GPUs are only useful for rendering graphics, not for running cuGraph algorithms.
D) The availability of NVIDIA CUDA-enabled GPUs, as cuGraph requires CUDA for acceleration.

5. A data scientist is training a deep learning model on an NVIDIA GPU but notices that the training speed is not significantly faster than when using a CPU.
Which of the following strategies is the best approach to fully utilize GPU acceleration and optimize training performance?

A) Disabling cuDNN optimizations to allow more flexibility in kernel execution
B) Increasing the batch size beyond the GPU's memory limit
C) Using only CPU-based data augmentation to keep the GPU dedicated to training
D) Using mixed precision training with NVIDIA Tensor Cores

Solutions: