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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

Question 1

You are tasked with implementing data caching to reduce shuffle in an accelerated machine learning pipeline using NVIDIA technologies. You need to cache intermediate results after a shuffle operation in a distributed setting.
Which of the following is the best approach to minimize shuffle overhead and maximize performance?

A. Use DALI to perform preprocessing and cache the output before the shuffle operation, thereby eliminating the need for shuffle.
B. Implement Spark's default disk caching to store shuffle results, allowing the GPU to access disk data directly.
C. Cache the data in GPU memory using RAPIDS cuDF for faster access, and leverage GPU-based partitioning to reduce shuffle size.
D. Use RAPIDS cuDF to cache the shuffled data on disk and then re-load it from disk during subsequent stages.

Question 2

You are building a real-time recommendation system that processes high-frequency transactional data from millions of users.
The system must:
- Ingest and preprocess data efficiently
- Perform similarity computations for user-item recommendations
- Scale to handle rapid incoming transactions
Which of the following NVIDIA technologies is the best choice for this use case?

A. NVIDIA Triton Inference Server
B. CUDA Kernels with Custom C++ Code
C. NVIDIA NVTabular
D. RAPIDS cuGraph

Question 3

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

Question 4

You are tasked with optimizing a data science workflow to scale across multiple GPUs using Dask.
Which of the following approaches would be most effective for implementing data parallelism in this scenario? (Select two)

A. Use Dask's default scheduler to distribute work across GPUs without any specific configuration
B. Use dask_cuda's CUDACluster to manage the multi-GPU setup and parallelize computations
C. Use Dask's distributed scheduler along with dask_cuda to handle GPU resources for parallel processing
D. Manually partition the dataset and assign each partition to a specific GPU using dask.delayed
E. Run the computation on a single GPU and let Dask handle CPU parallelism automatically

Question 5

You are a data scientist analyzing a social media network with NVIDIA cuGraph to identify the most influential users using the PageRank algorithm.
Which option best describes how cuGraph PageRank operates on a directed graph?

A. PageRank in cuGraph uses an iterative power method to update node importance values based on incoming edges, incorporating a damping factor to handle random jumps.
B. PageRank in cuGraph is a label propagation algorithm that clusters nodes into communities rather than ranking their importance.
C. PageRank in cuGraph operates only on undirected graphs and cannot be applied to networks where edges have a direction.
D. PageRank assigns equal importance to all nodes in the graph initially and updates values only based on outgoing edges, ignoring incoming edges.

Solutions: