Exam Name: NVIDIA-Certified-Professional Accelerated Data Science
Certification Provider: NVIDIA
Corresponding Certification: NVIDIA-Certified Professional
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Updated: Oct 16, 2026
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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Preparation | 17% | - Feature engineering and data type optimization - Data validation and quality assurance - Workflow monitoring and bottleneck identification - Data cleaning, preprocessing and transformation |
| Data Manipulation and Software Literacy | 19% | - Performance profiling and optimization tools - Data processing libraries selection and usage - GPU-accelerated ETL workflows - Dependency management and containerization |
| MLOps | 19% | - Model deployment and serving - Pipeline automation and orchestration - Monitoring, logging and maintenance - End-to-end workflow management |
| GPU and Cloud Computing | 16% | - Cloud GPU environments and deployment - GPU architecture and acceleration principles - CRISP-DM and data science methodology - Resource management and scaling strategies |
| Machine Learning | 15% | - GPU-accelerated ML frameworks and algorithms - Distributed training strategies - Model training and hyperparameter tuning - Model evaluation and validation |
| Data Analysis | 14% | - Exploratory Data Analysis (EDA) - Time-series analysis and anomaly detection - Data visualization and graph analytics - Distributed and parallel data processing |
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
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.
Correct Answer: D 🗳️
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
Correct Answer: C 🗳️
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
Correct Answer: D 🗳️
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
Correct Answer: B,C 🗳️
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.
Correct Answer: A 🗳️
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