NVIDIA Accelerated Data Science Associate Preparation Details
The Accelerated Data Science Associate certification (NCA-ADS) validates your skills in using GPU-accelerated tools like RAPIDS, cuDF, and cuML for real-world data workflows. This guide maps every exam domain to NVIDIA’s official documentation, covering data preparation, machine learning, pipeline automation, visualization, and MLOps. You can also explore more NVIDIA certification study guides on the NVIDIA Certifications category to keep building your skills.
NVIDIA Accelerated Data Science Associate Materials
Data Manipulation and Preparation – 23%
Data integration, joining, and manipulation using cuDF and pandas
10 Minutes to cuDF and Dask cuDF
Welcome to the cuDF documentation!
Data cleaning, quality handling, and governance compliance
Delivering AI-Ready Enterprise Data With GPU-Accelerated AI Storage
GPU-accelerated ETL workflows with RAPIDS, Dask, or Spark
RAPIDS Accelerator for Apache Spark – User Guide
Best Practices for Multi-GPU Data Analysis Using RAPIDS with Dask
Feature engineering for numerical and categorical variables
Target Encoding with RAPIDS cuML: Do More with Your Categorical Data
cuML – RAPIDS Machine Learning Library
Handling class imbalance and generating synthetic data
Faster Resampling with Imbalanced-learn and cuML
Training and Evaluating Machine Learning Models
Dimensionality reduction and data sampling
Even Faster and More Scalable UMAP on the GPU with RAPIDS cuML
Efficient processing and storage with Parquet and modern frameworks
10 Minutes to cuDF and Dask cuDF
Machine Learning With RAPIDS – 16%
GPU-accelerated model training with cuML and XGBoost
Welcome to cuML’s documentation!
Training and Evaluating Machine Learning Models
Regression, classification, and clustering techniques
Training and Evaluating Machine Learning Models
Model evaluation, comparison, and generalization assessment
Training and Evaluating Machine Learning Models
Hyperparameter tuning and optimization
Cross-validation methods
Training and Evaluating Machine Learning Models
Performance metrics and confusion matrix interpretation
GPU-Accelerated Land Use Land Cover Classification
Data Science Pipelines and Workflow Automation – 13%
End-to-end data science pipeline design
Example Projects for Data Science Workflows
RAPIDS Accelerates Data Science End-to-End
10 Minutes to cuDF and Dask cuDF
Feature engineering, selection, and transformation for model improvement
cuML – RAPIDS Machine Learning Library
Mitigating underfitting and overfitting through model and feature adjustments
Training and Evaluating Machine Learning Models
Dataset augmentation and integration for enhanced training data
Faster Resampling with Imbalanced-learn and cuML
Automation and scalability of data science workflows
Building reproducible pipelines with RAPIDS and Dask
NVIDIA AI Workbench Example Projects
Enable a data flywheel where production data continuously improves deployed models
Data Flywheel: What It Is and How It Works
Descriptive Analysis and Visualization – 13%
Exploratory data analysis (EDA) and descriptive statistics
Accelerated Data Analytics: Speed Up Data Exploration with RAPIDS cuDF
Visualization
Accelerated Data Analytics: A Guide to Data Visualization with RAPIDS
Build a Fully Interactive Dashboard in a Few Lines of Python
Selecting appropriate plots for different analysis goals
Accelerated Data Analytics: A Guide to Data Visualization with RAPIDS
Build a Fully Interactive Dashboard in a Few Lines of Python
Hypothesis testing and statistical significance evaluation
Accelerated Data Analytics: Speed Up Data Exploration with RAPIDS cuDF
Training and Evaluating Machine Learning Models
Interpreting patterns, trends, and relationships in data
Accelerated Data Analytics: Speed Up Data Exploration with RAPIDS cuDF
Foundations of Accelerated Data Science – 12%
Python fundamentals for data analysis (NumPy, pandas, Jupyter)
NumPy: The Absolute Basics for Beginners
Core GPU acceleration concepts and advantages for data science
Welcome to the cuDF documentation!
RAPIDS cuDF Accelerates pandas Nearly 150x with Zero Code Changes
CPU versus GPU workloads and memory transfer optimization
Best Practices for Multi-GPU Data Analysis Using RAPIDS with Dask
End-to-end data science workflow (ingest, ETL, clean, transform)
10 Minutes to cuDF and Dask cuDF
Example Projects for Data Science Workflows
Distributed versus GPU-accelerated computing frameworks
Model parameters, tuning, and overfitting versus underfitting concepts
Training and Evaluating Machine Learning Models
Introductory MLOps Practices – 10%
Monitoring and optimizing machine learning (ML) pipelines for performance and reliability
GPU-Accelerated Land Use Land Cover Classification
Managing and tracking experiments with MLflow, Weights & Biases, and custom tools
Model saving, loading, and prediction generation
Model Serialization and Persistence
Monitoring production models for drift and performance degradation
Data Flywheel: What It Is and How It Works
GPU-Accelerated Land Use Land Cover Classification
Managing model artifacts and configurations for reproducibility
Model Serialization and Persistence
Benchmarking workflows and selecting optimal hardware
Advanced Data Structures – 7%
Time series data handling, splitting, and forecasting evaluation
Managing missing or irregular timestamps with cuDF interpolation
Graph-based data representation and analysis
Node importance evaluation and network relationship visualization
cuGraph – RAPIDS Graph Analytics Library
Software and Environment Management – 6%
Enable reproducibility in data science projects by maintaining environment files
Configure reproducible Python environments using Conda, pip, or Docker
Installing the NVIDIA Container Toolkit
Efficiently manage software dependencies and collaborate in multi-user data science environments
Perform GPU environment check (driver/CUDA®/RAPIDS compatibility, nvidia-smi, device visibility) and resolve a dependency conflict
Specialized Configurations with Docker
Understand the basics of version control using Git
1.1 Getting Started – About Version Control
2.1 Git Basics – Getting a Git Repository
Wrapping Up Accelerated Data Science Associate
This guide walked through every domain of the Accelerated Data Science Associate (NCA-ADS) exam, from cuDF data manipulation to cuML model training, pipeline automation, visualization, and MLOps. Working through the official RAPIDS and NVIDIA documentation above will build real hands-on familiarity with the tools the exam tests. You can also explore more NVIDIA certification study guides on the NVIDIA Certifications category to keep building your skills. Have a question or tip? Leave a comment below.
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