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

  • Exam Code: NCP-ADS
  • Exam Name: NVIDIA-Certified-Professional Accelerated Data Science
  • Updated: Oct 09, 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%- GPU-accelerated data manipulation using cuDF
  • 1. Groupby, apply, and aggregation operations
  • 2. Data integration, joining, merging, and filtering
  • 3. cuDF vs pandas API mapping and usage
- Distributed computing with Dask
  • 1. Dask-cuDF for parallel data processing
  • 2. Scaling data operations across multiple GPUs
- Software literacy and development tools
  • 1. RAPIDS ecosystem (cuDF, cuML, cuGraph, cuPy)
  • 2. Python, NumPy, pandas, Jupyter proficiency
Topic 2: MLOps19%- Containerization and environment management
  • 1. Docker for reproducible GPU-accelerated workflows
  • 2. Conda environment management
- Model deployment and serving
  • 1. Model saving, loading, and prediction generation
  • 2. Production deployment strategies
- 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 3: GPU and Cloud Computing16%- GPU resource management
  • 1. Efficient GPU resource allocation and scheduling
- Performance optimization
  • 1. Mixed precision and bottleneck analysis
  • 2. Single and multi-GPU performance optimization
  • 3. Memory profiling with DLProf
- Cloud GPU environments
  • 1. Containerized workflow deployment on cloud
  • 2. Cloud-based GPU instance configuration
- GPU architecture and fundamentals
  • 1. GPU architecture fundamentals for data science
  • 2. CPU vs GPU workloads and memory transfer optimization
Topic 4: Machine Learning15%- Model training with GPU acceleration
  • 1. Training models using cuML and GPU-accelerated XGBoost
  • 2. Multi-GPU training strategies
  • 3. Selection of appropriate algorithms for GPU execution
- Feature engineering and hyperparameter tuning
  • 1. Feature engineering for ML models
  • 2. Hyperparameter tuning techniques
  • 3. Batching and memory-efficient training methods
- Deep learning frameworks integration
  • 1. Using RAPIDS with TensorFlow and PyTorch
  • 2. Overfitting vs underfitting concepts
Topic 5: Data Preparation17%- Data loading and preprocessing
  • 1. Handling class imbalance and generating synthetic data
  • 2. NVIDIA DALI for high-performance data loading
- 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
- GPU-accelerated ETL workflows
  • 1. RAPIDS-based ETL pipelines
  • 2. Efficient processing and storage with Parquet
Topic 6: Data Analysis14%- Time-series analysis
  • 1. Anomaly detection in time-series datasets
  • 2. Time-series data handling and forecasting
- Exploratory data analysis
  • 1. Descriptive statistics and summary analysis
  • 2. Performing EDA on GPU-accelerated datasets
- Visualization
  • 1. Selecting appropriate plots for different analysis goals
  • 2. Visualizing data using Plotly and Matplotlib
- Graph analytics
  • 1. Creating and analyzing graph data using cuGraph
  • 2. Node importance evaluation and network relationship visualization

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

Question #1

A machine learning engineer wants to deploy a GPU-accelerated inference model in a containerized environment while ensuring compatibility with NVIDIA libraries.
Which of the following is the best approach for managing dependencies?

  • A. Install GPU drivers directly inside the container instead of on the host system to avoid dependency conflicts.
  • B. Run Docker containers without any special configurations, as Docker automatically detects and utilizes GPUs.
  • C. Disable the --gpus flag when running Docker containers, as RAPIDS AI libraries do not require explicit GPU selection.
  • D. Use the official NVIDIA Docker base images (nvidia/cuda) and install RAPIDS AI libraries within the container to ensure GPU compatibility.
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

Question #2

Which NVIDIA technology is specifically designed for accelerating deep learning workloads in the cloud?

  • A. NVIDIA Jetson
  • B. TensorRT
  • C. NVIDIA Tesla
  • D. NVIDIA A100
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

Question #3

You are working on a medium-sized dataset (~500,000 rows, 20 columns) and need to perform fast exploratory data analysis (EDA) with filtering, aggregations, and transformations.
Which of the following Python libraries would be the most efficient choice for this task?

  • A. Vaex
  • B. Pandas
  • C. PySpark
  • D. Dask
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Question #4

You need to train a deep learning model using PyTorch on a dataset too large for a single GPU. You decide to use Dask with NVIDIA GPUs for multi-GPU scaling.
Which approach is the most effective for distributing the workload?

  • A. Use Dask's built-in deep learning API to automatically distribute PyTorch models across GPUs
  • B. Use Dask Bag to shard the dataset and train separate PyTorch models on each shard
  • C. Use Dask-CUDA workers with PyTorch's DistributedDataParallel (DDP) for training across multiple GPUs
  • D. Use Dask.delayed to wrap PyTorch training functions and schedule them across multiple GPUs
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

Question #5

You are working on a large-scale graph analysis project using NVIDIA cuGraph for accelerated computations. Your dataset consists of millions of nodes and edges representing social network interactions. You need to efficiently compute PageRank while minimizing memory usage.
Which of the following techniques would be the most effective?

  • A. Manually unroll loops and precompute node ranks in a NumPy array before feeding them into cuGraph.
  • B. Use cuGraph's sparse matrix representation to store graph data and perform computations efficiently.
  • C. Use Pandas DataFrames to manage the graph structure and leverage Pandas indexing for efficient queries.
  • D. Convert the graph into an adjacency list format and store it in Python dictionaries for faster lookups.
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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