NVIDIA : NCP-ADS Questions & Answers
Updated: Sep 02, 2026
No. of Questions: 303 Questions & Answers with Testing Engine
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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| GPU and Cloud Computing | 16% | - Cloud GPU environments and deployment - CRISP-DM and data science methodology - Resource management and scaling strategies - GPU architecture and acceleration principles |
| Data Manipulation and Software Literacy | 19% | - Performance profiling and optimization tools - GPU-accelerated ETL workflows - Dependency management and containerization - Data processing libraries selection and usage |
| Data Preparation | 17% | - Workflow monitoring and bottleneck identification - Data validation and quality assurance - Feature engineering and data type optimization - Data cleaning, preprocessing and transformation |
| MLOps | 19% | - Model deployment and serving - End-to-end workflow management - Monitoring, logging and maintenance - Pipeline automation and orchestration |
| Data Analysis | 14% | - Exploratory Data Analysis (EDA) - Data visualization and graph analytics - Time-series analysis and anomaly detection - Distributed and parallel data processing |
| Machine Learning | 15% | - Model training and hyperparameter tuning - Model evaluation and validation - Distributed training strategies - GPU-accelerated ML frameworks and algorithms |
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
Question 1
A data scientist is setting up a RAPIDS AI environment for a machine learning project that requires CUDA-enabled libraries and specific package versions to avoid conflicts.
Which of the following approaches best ensures a stable and reproducible environment while leveraging NVIDIA technologies?
A. Use a system-wide Python installation and install packages globally to ensure consistency across different users.
B. Install CUDA and RAPIDS AI libraries using OS-level package managers such as apt or yum instead of Conda or Docker.
C. Manually install each required package with pip to ensure the latest versions are used without interference from Conda.
D. Use Conda with the rapidsai channel to create an isolated environment that includes cuDF, cuML, and other GPU-accelerated libraries.
Question 2
You are working on a data processing pipeline using NVIDIA GPUs for accelerating computations.
You need to monitor the pipeline's performance to identify bottlenecks.
Which of the following tools or techniques can be used to efficiently recognize bottlenecks in such a GPU-accelerated pipeline? (Select two)
A. NVIDIA Nsight Systems
B. NVIDIA CUDA Profiler (nvprof)
C. NVIDIA DLA (Deep Learning Accelerator)
D. NVIDIA nvidia-smi
E. NVIDIA TensorRT Profiling
Question 3
A machine learning engineer is training a large transformer-based model for natural language processing (NLP). They want to maximize training speed and efficiency using NVIDIA GPUs.
Which of the following techniques would most effectively enhance GPU utilization and reduce training time?
A. Using mixed-precision training with Tensor Cores
B. Disabling data parallelism
C. Prefetching data with the CPU while training on the GPU
D. Running training exclusively on CPU
Question 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. Convert all data into Pandas DataFrames before distributing computations
B. Use ThreadPoolExecutor to manage parallel computations on GPUs
C. Use dask.array or dask.dataframe with dask_cuda.CUDACluster to distribute computations across multiple GPUs
D. Train a deep learning model on a single GPU first, then switch to Dask for scaling
Question 5
A research team is analyzing a large transportation network and wants to identify groups of locations that are closely connected based on travel routes.
Which NVIDIA cuGraph function would be the most effective for this task?
A. Use cuGraph's louvain() function to detect communities within the graph.
B. Use cuGraph's pagerank() to rank the most connected locations.
C. Apply cuGraph's bfs() (Breadth-First Search) to find clusters of locations.
D. Use cuGraph's shortest_path() to compute all shortest paths and derive clusters manually.
Solutions:
| Question 1 Answer: D | Question 2 Answer: A,B | Question 3 Answer: A | Question 4 Answer: C | Question 5 Answer: A |
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