NVIDIA NCP-ADS Exam Information and Actual Questions

  • Exam Code/Number: NCP-ADS
  • Exam Name/Title: NVIDIA-Certified-Professional Accelerated Data Science
  • Certification Provider: NVIDIA
  • Corresponding Certification: NVIDIA-Certified Professional
  • Exam Questions: 303
  • Updated On: Jul 11, 2026

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

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NVIDIA NCP-ADS Exam Overview:

Certification Vendor:NVIDIA
Exam Name:NVIDIA-Certified Professional Accelerated Data Science
Exam Number:NCP-ADS
Available Languages:English
Exam Duration:120 minutes
Related Certifications:NVIDIA-Certified Professional: Accelerated Data Science
Exam Format:Multiple-choice
Certificate Validity Period:2 years
Real Exam Qty:60-70
Exam Price:$200 USD
Passing Score:~70%
Sample Questions:NVIDIA NCP-ADS Sample Questions
Exam Way:Online, remotely proctored
Pre Condition:Two to three years of hands-on experience in accelerated data science. Strong foundation in machine learning and GPU-accelerated computing. Experience in GPU-based optimization strategies and accelerated data manipulation techniques. Deep understanding of end-to-end data science workflows, from data preparation and cleansing to model development and deployment.
Official Syllabus URL:https://www.nvidia.com/en-us/learn/certification/accelerated-data-science-professional

NVIDIA NCP-ADS Exam Syllabus Topics:

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


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