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Last Updated: Oct 18, 2026
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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Manipulation and Software Literacy | 19% | - Distributed computing with Dask
|
| Machine Learning | 15% | - Deep learning frameworks integration
|
| GPU and Cloud Computing | 16% | - Cloud GPU environments
|
| Data Analysis | 14% | - Time-series analysis
|
| Data Preparation | 17% | - Data cleaning and quality handling
|
| MLOps | 19% | - Model deployment and serving
|
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
You are processing a large-scale transportation network graph using NVIDIA cuGraph. The graph is extremely large, consuming almost all available GPU memory. Performance is deteriorating, and some computations fail due to memory exhaustion.
What is the best approach to efficiently handle this large graph while keeping computations on the GPU?
- A. Store the graph as a large Python dictionary and use cuGraph only for specific queries.
- B. Convert the graph into a NetworkX graph and process it on the CPU to reduce GPU memory usage.
- C. Manually split the graph into chunks and process each chunk separately without any coordination.
- D. Use cuGraph's multi-GPU support via Dask-cuGraph to distribute the graph across multiple GPUs.
Correct Answer: D 🗳️
You are working with a social network dataset containing millions of user interactions and need to identify influential users based on their connectivity and interactions.
Which approach using NVIDIA's cuGraph library is the most appropriate for this task?
- A. Use cuGraph's PageRank algorithm to rank users based on their importance in the network.
- B. Use cuGraph's DBSCAN clustering to detect communities in the social network.
- C. Apply cuGraph's K-Means clustering to group users with similar connectivity patterns.
- D. Run a breadth-first search (BFS) on the entire graph to find the most influential users.
Correct Answer: A 🗳️
You are building a large-scale AI training pipeline that requires efficient storage and retrieval of structured and unstructured datasets across multiple GPUs.
Which of the following is the best NVIDIA technology to organize and manage datasets at scale?
- A. NVIDIA Magnum IO for high-performance I/O and dataset storage optimization.
- B. NVIDIA Clara Imaging for storing structured and unstructured datasets efficiently.
- C. NVIDIA Morpheus for accelerating dataset indexing and retrieval in AI pipelines.
- D. NVIDIA Nsight Systems for managing dataset storage and retrieval performance.
Correct Answer: A 🗳️
You are working on a large dataset (several terabytes in size) and need to perform data preprocessing, filtering, and transformations before training a machine learning model.
Given the dataset size and the requirement to optimize for GPU acceleration using NVIDIA technologies, which of the following is the most appropriate data processing library to use?
- A. Modin with Ray backend
- B. pandas
- C. NumPy with CuPy acceleration
- D. Dask DataFrame with Dask-CUDA
Correct Answer: D 🗳️
You are tasked with processing a large dataset using multiple GPUs to accelerate computation. You decide to use Dask to implement data parallelism with NVIDIA's RAPIDS framework to maximize GPU utilization.
Which of the following steps is essential for efficiently distributing the workload across multiple GPUs in Dask?
- A. Manually allocate GPU memory using cupy for each worker instead of using Dask's scheduler.
- B. Set up a single Dask dataframe without partitioning and rely on automatic workload balancing.
- C. Use dask_cuda.LocalCUDACluster() to create a multi-GPU cluster and dask.distributed.Client() to manage execution.
- D. Use dask.dataframe.repartition() to distribute data evenly across multiple GPUs.
Correct Answer: C 🗳️
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