NVIDIA-Certified Associate

Accelerated Data Science

(NCA-ADS)

About This Certification
Exam Preparation
Exam Blueprint
Contact Us

About This Certification
Exam Preparation
Exam Blueprint
Contact Us

About This Certification

The NCA-Accelerated Data Science certification is an intermediate-level credential that validates a candidate’s proficiency in leveraging GPU-accelerated tools and libraries for data science workflows. The exam is online and proctored remotely, includes 50–60 questions, and has a 60-minute time limit.

Please carefully review our certification FAQs and exam policies before scheduling your exam.

If you have any questions, please contact us here.

Important note: To access the exam, you’ll need to create a Certiverse account.

Certification Exam Details

Duration: 60 minutes

Price: $125

Certification level: Associate

Subject: Accelerated data science

Number of questions: 50–60

Prerequisites: 1–2 years of experience in accelerated data science, using GPU-based tools to efficiently process and analyze large datasets and improve the performance of machine learning, ETL, and analytics workloads.

Language: English

Validity: This certification is valid for two years from issuance. Recertification may be achieved by retaking the exam.

Credentials: Upon passing the exam, participants will receive a digital badge and optional certificate indicating the certification level and topic.

Exam Preparation

Topics Covered in the Exam

Advance Data Structures
Data Manipulation and Preparation
Data Science Pipelines and Workflow Automation
Descriptive Analysis and Visualization
Foundations of Accelerated Data Science
Introductory MLOps Practices
Machine Learning With NVIDIA RAPIDS™
Software and Environment Management

Candidate Audiences

Data scientists
Data analysts
Data engineers
Machine learning engineers
AI DevOps engineers
Software engineers
Solution architects
Deep learning performance engineers
Researchers

Certification Learning Path

Accelerating End-to-End Data Science Workflows

In this self-paced course, you’ll learn how to build and execute end-to-end, GPU-accelerated data science workflows that will enable you to quickly explore, iterate, and get your work into production.

Note: This course is also offered as an Instructor-led workshop.

Learn More About This Course

Introduction to Time Series Forecasting

Coming soon!

Best Practices in Feature Engineering for Tabular Data with GPU Acceleration

Learn how to improve model performance for large datasets through feature engineering using GPU-accelerated Python libraries.

Learn More About This Course

Exam Study Guide

Coming soon.

Exam Blueprint

The table below provides an overview of the topic areas covered in the certification exam and how much of the exam is focused on that subject.

Topics Areas	% of Exam	Topics Covered
Data Manipulation and Preparation	23%	Data integration, joining, and manipulation using NVIDIA cuDF and pandas Data cleaning, quality handling, and governance compliance GPU-accelerated ETL workflows with RAPIDS, Dask, or Spark Feature engineering for numerical and categorical variables Handling class imbalance and generating synthetic data Dimensionality reduction and data sampling Efficient processing and storage with Parquet and modern frameworks
Machine Learning With RAPIDS	16%	GPU-accelerated model training with NVIDIA cuML and XGBoost Regression, classification, and clustering techniques Model evaluation, comparison, and generalization assessment Hyperparameter tuning and optimization Cross-validation methods Performance metrics and confusion matrix interpretation
Data Science Pipelines and Workflow Automation	13%	End-to-end data science pipeline design Feature engineering, selection, and transformation for model improvement Mitigating underfitting and overfitting through model and feature adjustments Dataset augmentation and integration for enhanced training data Automation and scalability of data science workflows Building reproducible pipelines with RAPIDS and Dask
Descriptive Analysis and Visualization	13%	Exploratory data analysis (EDA) and descriptive statistics Visualization Selecting appropriate plots for different analysis goals Hypothesis testing and statistical significance evaluation Interpreting patterns, trends, and relationships in data
Foundations of Accelerated Data Science	12%	Python fundamentals for data analysis (NumPy, pandas, Jupyter) Core GPU acceleration concepts and advantages for data science CPU vs. GPU workloads and memory transfer optimization End-to-end data science workflow (ingest, ETL, clean, transform) Distributed vs. GPU-accelerated computing frameworks Model parameters, tuning, and overfitting vs. underfitting concepts
Introductory MLOps Practices	10%	Monitoring and optimizing machine learning (ML) pipelines for performance and reliability Managing and tracking experiments with MLflow, Weights & Biases, and custom tools Model saving, loading, and prediction generation Monitoring production models for drift and performance degradation Managing model artifacts and configurations for reproducibility Benchmarking workflows and selecting optimal hardware
Advance Data Structures	7%	Time-series data handling, splitting, and forecasting evaluation Managing missing or irregular timestamps with cuDF interpolation CPU vs. GPU performance for temporal analytics Graph-based data representation and analysis Node importance evaluation and network relationship visualization
Software and Environment Management	6%	Contributing to reproducibility in data science projects by maintaining environment files Configuring reproducible Python environments using Conda, PIP, or Docker Managing software dependencies efficiently and collaborating in multi-user data science environments Performing GPU environment check (driver/CUDA/RAPIDS compatibility, nvidia-smi, device visibility) and resolving a dependency conflict Understanding the basics of version control using git

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NVIDIA offers training and certification for professionals looking to enhance their skills and knowledge in the field of AI, accelerated computing, data science, advanced networking, graphics, simulation, and more.

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