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NVIDIA NCP-ADS Actual Tests : NVIDIA-Certified-Professional Accelerated Data Science

NCP-ADS actual test
  • Exam Code: NCP-ADS
  • Exam Name: NVIDIA-Certified-Professional Accelerated Data Science
  • Updated: May 31, 2026
  • Q & A: 303 Questions and Answers
  • PDF Demo
  • PC Test Engine
  • Online Test Engine
  • Total Price: $59.99  

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. A data scientist is using an NVIDIA RAPIDS-based data processing pipeline on a GPU cluster. They notice that the pipeline is not performing as expected and suspect a bottleneck.
Which of the following approaches would best help identify the source of the bottleneck?

A) Disable GPU parallelism and run computations on the CPU to compare performance.
B) Increase the batch size of data being processed without analyzing memory utilization.
C) Rely on nvidia-smi alone to check GPU utilization without considering other profiling tools.
D) Use nsys (NVIDIA Nsight Systems) to profile GPU kernel execution and memory transfer times.


2. A data scientist is working on a social network analysis project where they need to find the most influential users in a large-scale graph dataset. The dataset consists of millions of users connected through directed edges.
Which of the following approaches would be the best choice for this task using NVIDIA GPU-accelerated tools?

A) Use cuDF's groupby().sum() function to count the number of connections per user.
B) Convert the graph into a pandas DataFrame and apply NetworkX's PageRank implementation.
C) Use cuGraph's bfs() (Breadth-First Search) to find the most influential nodes.
D) Use cuGraph's pagerank() function to identify influential nodes based on link structure.


3. Which of the following is the primary benefit of using NVIDIA RAPIDS in the context of data science workflows?

A) It allows for the implementation of reinforcement learning algorithms
B) It enables real-time data streaming and analytics
C) It simplifies the creation of visualizations for large datasets
D) It speeds up the preprocessing and analysis of large datasets by utilizing GPUs


4. You are using cuGraph to run the PageRank algorithm on a directed web graph. The dataset is large, and you want to ensure an accurate and efficient computation while optimizing GPU performance.
Which of the following configurations is the best approach for running PageRank in cuGraph?

A) Load the graph into NetworkX first, compute PageRank, and then convert the results back into cuGraph format
B) Run cugraph.pagerank() with a damping factor of 0.85 and set the max iterations to 100 with a convergence threshold
C) Convert the graph into an adjacency matrix and perform matrix multiplication iteratively for convergence
D) Use the cugraph.pagerank() function with a damping factor of 0 and 10 iterations


5. Which of the following Nvidia technologies is primarily used for performing benchmarking and optimizing GPU-accelerated deep learning workflows, especially focusing on model training performance?

A) Nvidia Nsight Systems
B) Nvidia Triton Inference Server
C) Nvidia DeepStream SDK
D) Nvidia CUDA


Solutions:

Question # 1
Answer: D
Question # 2
Answer: D
Question # 3
Answer: D
Question # 4
Answer: B
Question # 5
Answer: A

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