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

  • Exam Code: NCP-ADS
  • Exam Name: NVIDIA-Certified-Professional Accelerated Data Science
  • Updated: Oct 14, 2026
  • Q & A: 303 Questions and Answers

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NVIDIA NCP-ADS Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Manipulation and Software Literacy19%- Distributed computing with Dask
  • 1. Dask-cuDF for parallel data processing
  • 2. Scaling data operations across multiple GPUs
- GPU-accelerated data manipulation using cuDF
  • 1. Data integration, joining, merging, and filtering
  • 2. Groupby, apply, and aggregation operations
  • 3. cuDF vs pandas API mapping and usage
- Software literacy and development tools
  • 1. RAPIDS ecosystem (cuDF, cuML, cuGraph, cuPy)
  • 2. Python, NumPy, pandas, Jupyter proficiency
Topic 2: GPU and Cloud Computing16%- Cloud GPU environments
  • 1. Cloud-based GPU instance configuration
  • 2. Containerized workflow deployment on cloud
- GPU architecture and fundamentals
  • 1. GPU architecture fundamentals for data science
  • 2. CPU vs GPU workloads and memory transfer optimization
- GPU resource management
  • 1. Efficient GPU resource allocation and scheduling
- Performance optimization
  • 1. Memory profiling with DLProf
  • 2. Mixed precision and bottleneck analysis
  • 3. Single and multi-GPU performance optimization
Topic 3: Machine Learning15%- Model training with GPU acceleration
  • 1. Multi-GPU training strategies
  • 2. Training models using cuML and GPU-accelerated XGBoost
  • 3. Selection of appropriate algorithms for GPU execution
- Deep learning frameworks integration
  • 1. Overfitting vs underfitting concepts
  • 2. Using RAPIDS with TensorFlow and PyTorch
- Feature engineering and hyperparameter tuning
  • 1. Feature engineering for ML models
  • 2. Batching and memory-efficient training methods
  • 3. Hyperparameter tuning techniques
Topic 4: Data Analysis14%- Visualization
  • 1. Selecting appropriate plots for different analysis goals
  • 2. Visualizing data using Plotly and Matplotlib
- Time-series analysis
  • 1. Time-series data handling and forecasting
  • 2. Anomaly detection in time-series datasets
- Graph analytics
  • 1. Node importance evaluation and network relationship visualization
  • 2. Creating and analyzing graph data using cuGraph
- Exploratory data analysis
  • 1. Descriptive statistics and summary analysis
  • 2. Performing EDA on GPU-accelerated datasets
Topic 5: MLOps19%- Containerization and environment management
  • 1. Conda environment management
  • 2. Docker for reproducible GPU-accelerated workflows
- Model deployment and serving
  • 1. Production deployment strategies
  • 2. Model saving, loading, and prediction generation
- Model monitoring and management
  • 1. Managing model artifacts and configurations for reproducibility
  • 2. Monitoring production models for drift and performance degradation
- Experiment tracking
  • 1. Benchmarking workflows and selecting optimal hardware
  • 2. MLflow, Weights & Biases, and custom tracking tools
Topic 6: Data Preparation17%- GPU-accelerated ETL workflows
  • 1. Efficient processing and storage with Parquet
  • 2. RAPIDS-based ETL pipelines
- Data loading and preprocessing
  • 1. NVIDIA DALI for high-performance data loading
  • 2. Handling class imbalance and generating synthetic data
- Data cleaning and quality handling
  • 1. Handling missing values and data quality issues
  • 2. Data governance and compliance
- Feature engineering
  • 1. Feature engineering for numerical and categorical variables
  • 2. Dimensionality reduction and data sampling

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

Question #1

You are designing a data pipeline for a healthcare analytics company that processes petabytes of structured and semi-structured patient data. Your goal is to optimize performance while maintaining accuracy.
Which of the following strategies is the most appropriate choice for accelerating this workload?

  • A. Load the data into a PostgreSQL database and run SQL queries for all transformations before training a model.
  • B. Use RAPIDS cuDF and Dask-cuDF for distributed preprocessing and RAPIDS cuML for GPU- accelerated machine learning model training.
  • C. Use RAPIDS cuDF for data ingestion, preprocessing, and transformation, then offload model training to scikit-learn on CPUs.
  • D. Use Apache Hadoop and MapReduce for preprocessing before training models on a single GPU.
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Question #2

You are developing an end-to-end data pipeline that processes terabytes of image metadata using NVIDIA technologies. You need a software stack that efficiently integrates GPU-accelerated data processing, machine learning, and visualization.
Which of the following tool combinations is best suited for this task?

  • A. Hadoop for data storage, NumPy for computations, and TensorFlow for visualization.
  • B. Excel for data analysis, scikit-learn for machine learning, and Seaborn for visualization.
  • C. Pandas for data manipulation, XGBoost for machine learning, and Matplotlib for visualization.
  • D. cuDF for data manipulation, cuML for machine learning, and Plotly for visualization.
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

Question #3

A research team is analyzing large-scale social interactions and wants to identify strongly connected communities within a massive graph dataset using NVIDIA's cuGraph library.
Which method would be the most efficient for this task?

  • A. Use cuGraph's Louvain method to detect hierarchical communities based on modularity optimization.
  • B. Apply cuGraph's Dijkstra's algorithm to find the shortest paths between all nodes and group them into communities.
  • C. Run the cuGraph PageRank algorithm and classify nodes with high scores as community leaders.
  • D. Apply cuGraph's Label Propagation Algorithm (LPA) to divide the graph into communities without predefining the number of clusters.
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

Question #4

You are working on a large-scale social network analysis project using cuGraph. Your goal is to identify the most influential individuals in the network based on their connectivity and reach.
Which of the following cuGraph algorithms would be the most appropriate choice for this task?

  • A. PageRank
  • B. Connected Components
  • C. Single Source Shortest Path (SSSP)
  • D. Louvain Clustering
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

Question #5

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 CUDA Profiler (nvprof)
  • B. NVIDIA Nsight Systems
  • C. NVIDIA nvidia-smi
  • D. NVIDIA DLA (Deep Learning Accelerator)
  • E. NVIDIA TensorRT Profiling
Reveal Solution  Discussion  0

Correct Answer: A,B  🗳️

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