NVIDIA-Certified Associate

# Accelerated Data Science

(NCA-ADS)

[Exam Registration](https://www.certiverse.com/#/checkout/nvidia/store-exam/NCA-ADS)

## About This Certification

The NCA-Accelerated Data Science certification is an entry-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](https://www.nvidia.com/en-us/learn/certification/#faq) before scheduling your exam.

If you have any questions, please contact us [here](https://www.nvidia.com/en-us/learn/organizations/contact-us.md). 

*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](https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-DS-01+V2)

### Accelerated Machine Learning for Time Series Forecasting

In this self-paced course, learn how to accelerate portfolio optimization and real-time risk analysis using NVIDIA GPU-powered CUDA-X and cuOpt libraries.

[Learn More About This Course](https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-DS-10+V1)

### 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](https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-DS-06+V1)

## Exam Study Guide

Review [Study Guide](https://nvdam.widen.net/s/wnsxjfkjdz/nvt-studyguide-accelerated-data-science-associate-exam)

## 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 |

### Get Certified

Register now to take the next step in your career with an industry-recognized certification.

[Register for Exam](https://www.certiverse.com/#/checkout/nvidia/store-exam/NCA-ADS)

### Contact Us

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.

Contact us to learn how we can help you achieve your goals.

[Contact Us](https://enterprise-support.nvidia.com/s/create-case)

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