1. Study Guide (PDF – 250 Pages): Download the comprehensive PDF book, designed as a companion resource to support your certification exam preparation. The link is available in the Resources section under Practice Paper 1, Question 1.

2. Interview Questions & Answers (PDF): Access the complete set of interview questions and answers in PDF format, available in the Resources section of Question 1.

3. Interview Questions & Answers (Audio Book – 2 Hr 30 Mins): Listen and learn on the go with the audiobook version of interview questions and answers. The download link is provided in the Resources section of Question 1.

Disclaimer: “This course is not affiliated with or endorsed by NVIDIA. NVIDIA and CUDA are trademarks of NVIDIA Corporation.”

Prepare for success in the Certified Professional Accelerated Data Science (NCP-ADS) certification with this comprehensive practice test and preparation course, designed to help you master GPU-accelerated data science, machine learning, and MLOps workflows. This intermediate-level certification validates your expertise in leveraging GPU-powered tools, libraries, and workflows to dramatically accelerate data processing, analysis, model training, and deployment.

The NCP-ADS exam consists of 60–70 multiple-choice questions to be completed within 90 minutes. This course is carefully structured to mirror the real exam format while equipping you with the job-ready skills essential for today’s data science professionals.

Key exam domains and skills covered in this course:

• Accelerated Data Science — Optimize performance by integrating GPU acceleration into every stage of the data science pipeline.

• GPU Acceleration — Reduce processing times for data manipulation, analysis, and model training using CUDA-enabled libraries.

• Data Analysis & Visualization — Extract actionable insights and present them effectively using accelerated data visualization techniques.

• Data Preparation & Cleansing — Clean, normalize, and transform massive datasets using GPU-powered tools like cuDF.

• Feature Engineering — Design and optimize features at scale using accelerated data transformation methods.

• Machine Learning & Deep Learning — Build, train, and evaluate models faster using GPU-accelerated frameworks such as RAPIDS, cuML, and TensorFlow.

• ETL (Extract, Transform, Load) — Manage large-scale datasets efficiently using GPU acceleration for high-throughput data pipelines.

• Graph Analytics — Implement and optimize graph-based analytics using GPU acceleration for complex relationships and networks.

• MLOps — Deploy, monitor, and scale machine learning models using accelerated pipelines for production environments.

• Time-Series Analysis — Apply advanced forecasting techniques using GPU-optimized time-series libraries.

Why choose this course?

• 300 practice questions designed to reflect the difficulty, scope, and style of the official NCP-ADS certification exam.

• Comprehensive explanations for each answer to deepen understanding and reinforce key concepts.

• Covers all NCP-ADS exam topics to ensure thorough preparation.

• Real-world skill development — not just exam prep, but practical, hands-on knowledge you can apply immediately in accelerated data science projects.

Who should take this course?
This course is ideal for:

• Data Scientists seeking to accelerate workflows and validate expertise with technologies.

• Data Engineers & Analysts handling large-scale datasets who want to optimize processing speeds.

• Machine Learning Engineers looking to shorten model training times with GPU acceleration.

• AI DevOps Engineers managing deployment pipelines for accelerated ML workflows.

• Solution Architects designing enterprise-level accelerated data science environments.

• Deep Learning Performance Engineers & Researchers pushing the limits of AI and analytics.

If you work with GPU-accelerated computing, RAPIDS, cuDF, cuML, TensorFlow, PyTorch, MLOps, ETL, time-series analysis, or large-scale data pipelines, this course will prepare you for the NCP-ADS exam and enhance your real-world accelerated data science expertise.

By the end of this course, you will be able to:

• Confidently take and pass the NCP-ADS certification exam.

• Implement GPU-accelerated workflows for data analysis, modeling, and deployment.

• Optimize machine learning and data processing pipelines for performance and scalability.

• Apply accelerated data science techniques to solve real-world challenges efficiently.

Enroll now and take the next step toward becoming a certified Accelerated Data Science professional, ready to deliver faster, more efficient, and scalable AI-driven insights.

Who this course is for:

• This course is designed for anyone preparing for the NVIDIA-Certified Professional Accelerated Data Science (NCP-ADS) certification—whether you’re an experienced professional or just starting your journey in GPU-accelerated data science, machine learning, and MLOps.
• Data Scientists seeking to master GPU acceleration for faster model training and data analysis.
• Data Engineers managing large datasets and building high-performance ETL pipelines.
• Machine Learning Engineers optimizing workflows using NVIDIA RAPIDS, cuDF, cuML, TensorFlow, or PyTorch.
• AI DevOps Engineers deploying and scaling accelerated ML models in production environments.
• Applied Data Scientists and Researchers working on large-scale analytics and deep learning projects.
• Solution Architects designing enterprise-level accelerated data science environments.
• Data Analysts aiming to speed up data visualization, feature engineering, and time-series forecasting.
• Anyone with an interest in learning GPU-powered data science techniques and passing the NCP-ADS certification.