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Last Updated: Oct 21, 2026

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NVIDIA NCP-ADS Exam Syllabus Topics:

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

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

Question #1

A data engineer is preparing a dataset for training a deep learning model. The dataset contains numerical features with missing values, outliers, and inconsistent units.
Which of the following strategies is the most appropriate for ensuring a standardized and clean dataset?

  • A. Remove all rows with missing values and outliers to ensure only clean data is used.
  • B. Replace missing values with the mean, apply z-score normalization, and clip extreme outliers based on a threshold (e.g., 3 standard deviations).
  • C. Standardize the dataset using the mean and standard deviation, but keep missing values and outliers unchanged to avoid data manipulation.
  • D. Use the median to fill missing values, convert all numerical values into categorical bins, and apply Min-Max scaling.
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Question #2

You are working on a deep learning project that requires a large dataset of high-resolution satellite images for training a convolutional neural network (CNN). You want to leverage NVIDIA technologies to efficiently acquire and manage the dataset.
Which of the following approaches is the most suitable?

  • A. Use NVIDIA RAPIDS cuDF to directly download and preprocess satellite images from an API in real time.
  • B. Use NVIDIA DeepStream to acquire satellite images and store them in a structured dataset for machine learning.
  • C. Use NVIDIA Modulus to generate synthetic satellite images instead of acquiring real-world data.
  • D. Use NVIDIA DALI (Data Loading Library) to stream and preprocess satellite image data efficiently for deep learning training.
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

Question #3

You are processing large-scale datasets in Dask-cuDF and observe that your computation involves excessive data shuffling, which slows down performance. You decide to implement data caching to reduce shuffle overhead.
Which of the following best describes a technique to reduce shuffle costs in a Dask-cuDF workflow?

  • A. Splitting the DataFrame into multiple smaller DataFrames and recomputing each separately reduces shuffle operations in Dask-cuDF.
  • B. Persisting intermediate results in distributed GPU memory using df.persist() ensures that Dask does not recompute partitions, reducing shuffle overhead.
  • C. Disabling lazy execution using dask.config.set(lazy=False) forces all computations to execute eagerly, thereby avoiding shuffle.
  • D. Using .compute() on each partition immediately forces Dask to materialize results and store them in memory, ensuring shuffle operations are avoided.
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Question #4

A data scientist is working with large-scale ETL (Extract, Transform, Load) pipelines on GPU- accelerated infrastructure using RAPIDS. The workload involves frequent shuffle operations, which significantly impact performance.
What is the best approach using NVIDIA technologies to reduce shuffle overhead and improve performance?

  • A. Store all intermediate shuffle data in CPU memory using pandas to ensure persistence and reduce GPU load.
  • B. Use RAPIDS cuDF's GPU memory caching to store intermediate DataFrames and avoid redundant shuffle operations.
  • C. Use RAPIDS cuML to replace shuffle-intensive operations with an ML model that predicts data distribution.
  • D. Enable CUDA Unified Memory to automatically optimize shuffle performance without manual intervention.
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Question #5

Which of the following tools in the NVIDIA AI stack is specifically designed to accelerate the deployment of machine learning models for production by optimizing inference performance?

  • A. Triton Inference Server
  • B. cuBLAS
  • C. DLA (Deep Learning Accelerator)
  • D. cuML
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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