Certified Professional Accelerated Data Science (NCP-ADS)

The Accelerated Data Science Certification Exam Prep - Practice Tests course is designed to help you prepare, assess your readiness, and build confidence for the NCP-ADS certification exam. Earning this certification demonstrates your ability to develop, optimize, and scale data science pipelines using accelerated computing tools. In this course, you won't find video lectures or theoretical lessons.

Instead, you'll engage directly with timed, high-quality practice tests that mirror the actual exam format. These tests are created to closely reflect the question style, difficulty level, and topic distribution found on the official exam. What You Can Expect from This Course:4 full-length practice examsAbout 70 questions per test, randomized to simulate real conditionsMultiple-choice format, just like the real examAuto-scoring and performance breakdownsDetailed answer explanations for every questionUnlimited retakes to reinforce your learningBy completing these tests, you'll gain insight into the key domains of the exam including:Data ingestion and preprocessing using cuDFAccelerated data pipelines and RAPIDS ecosystemMachine learning with RAPIDS and XGBoostOptimization strategies and deployment considerationsExample Question:You are optimizing a machine learning training pipeline that uses a large tabular dataset.

Which of the following techniques is most likely to improve GPU memory efficiency during training. A) Using a larger batch size to fully utilize the GPUB) Converting all categorical variables into one-hot encoded vectorsC) Enabling mixed precision trainingD) Applying L2 regularization to the modelCorrect Answer: C) Enabling mixed precision trainingExplanation:Mixed precision training combines 16-bit and 32-bit floating point types to reduce memory usage while maintaining model accuracy. This technique allows more data to fit into GPU memory, often resulting in faster training times without significant accuracy trade-offs.

Increasing batch size or one-hot encoding could increase memory demands, and regularization impacts model generalization, not memory efficiency. How to Use This Course Effectively:Simulate the real exam environment: Set a timer, remove distractions, and approach each test as if it's the actual certification exam. Review explanations: Don't just focus on correct answers-read every explanation to understand the reasoning and learn from mistakes.