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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. A data science team is developing a machine learning pipeline requiring specific CUDA, cuDNN, and RAPIDS versions for compatibility across environments. They need a framework to manage dependencies and version conflicts.
Which approach is best for managing software dependencies using NVIDIA technologies?

A) Using only virtual environments (venv) without managing GPU dependencies separately
B) Manually installing each package and its dependencies using pip
C) Using Conda with NVIDIA Conda channels to manage CUDA and cuDNN dependencies
D) Using a single system-wide installation of CUDA and forcing all projects to use the same version

2. You are processing a dataset of high-resolution images for deep learning training and want to optimize image loading, augmentation, and transformation on an NVIDIA GPU.
Which of the following statements correctly describes the role of NVIDIA DALI (Data Loading Library) in accelerating data preparation?

A) NVIDIA DALI only supports preprocessing images and does not include any functionality for video or text data.
B) NVIDIA DALI can offload image preprocessing tasks to the GPU, reducing CPU bottlenecks and improving training throughput.
C) NVIDIA DALI does not support data augmentation and is only used for raw data loading.
D) NVIDIA DALI is an alternative to cuDF for GPU-accelerated tabular data processing.

3. You are training a deep learning model on a large dataset and are deciding whether to use a single GPU or multiple GPUs.
Which of the following are true considerations when comparing single-GPU and multi-GPU training setups? (Select two)

A) Multi-GPU training can significantly reduce training time when the dataset is large and the model is computationally intensive.
B) Single-GPU training is generally more cost-effective and should be preferred unless scaling is absolutely necessary.
C) Single-GPU training is limited by the VRAM (video memory) on the GPU, so larger models or datasets may require multi-GPU setups.
D) Multi-GPU training requires modifications to the model architecture to make it compatible with parallel processing.
E) Multi-GPU setups perform better only when the batch size is reduced.

4. A data scientist wants to process a large dataset using multiple GPUs on an NVIDIA-supported system. They decide to use Dask to enable parallelism.
Which of the following steps is most essential for leveraging Dask for multi-GPU scaling?

A) Use dask.array or dask.dataframe with dask_cuda.CUDACluster to distribute computations across multiple GPUs
B) Train a deep learning model on a single GPU first, then switch to Dask for scaling
C) Convert all data into Pandas DataFrames before distributing computations
D) Use ThreadPoolExecutor to manage parallel computations on GPUs

5. You are working on a large-scale machine learning workload that involves training a deep learning model using multiple GPUs. You want to leverage Dask to implement data parallelism efficiently using NVIDIA GPUs.
Which of the following approaches best achieves data parallelism in this context?

A) Leverage Dask-CUDA to automatically assign computations to available GPUs using the worker pool
B) Run a single large Dask task on the CPU and use Dask-MPI for multi-GPU execution
C) Use Dask with CuPy to distribute NumPy-based computations across multiple GPUs
D) Use Dask DataFrame to parallelize deep learning model training across multiple GPUs

Solutions: