NVIDIA-Certified Professional: Accelerated Data Science (NCP-ADS) Preparation Details
The NVIDIA-Certified Professional: Accelerated Data Science (NCP-ADS) exam tests your ability to build GPU-accelerated data science workflows with the RAPIDS suite. This guide maps every domain and objective to verified NVIDIA and RAPIDS documentation for certification readiness. You can also explore more NVIDIA certification study guides on the NVIDIA category to keep building your skills.
NVIDIA-Certified Professional: Accelerated Data Science Materials
| Coursera | GPU Programming Specialization |
| Udemy | NVIDIA NCP-ADS Exam Prep: Accelerated Data Science. |
| Whizlabs | NVIDIA-Professional Accelerated Data Science Course |
Data Analysis: Exam Weight 14%
Topics Covered
1.1 Detect anomalies in a time-series dataset.
Accelerated Data Analytics: Faster Time Series Analysis with RAPIDS cuDF
Accelerating Time Series Forecasting with RAPIDS cuML
Welcome to cuML’s documentation!
1.2 Conduct time-series analysis.
Accelerated Data Analytics: Faster Time Series Analysis with RAPIDS cuDF
10 Minutes to cuDF and Dask cuDF
Welcome to cuML’s documentation!
1.3 Create and analyze graph data using something like cuGraph.
1.4 Identify how much data is big data (or when to use which acceleration method).
Get Started with GPU Acceleration for Data Science
RAPIDS on Databricks: A Guide to GPU-Accelerated Data Processing
Accelerated Data Analytics: Speed Up Data Exploration with RAPIDS cuDF
1.5 Perform exploratory data analysis (EDA).
Accelerated Data Analytics: Speed Up Data Exploration with RAPIDS cuDF
10 Minutes to cuDF and Dask cuDF
1.6 Visualize time-series data.
Accelerated Data Analytics: A Guide to Data Visualization with RAPIDS
Welcome to cuxfilter’s documentation
Data Manipulation and Software Literacy Exam Weight 19%
Topics Covered
2.1 Design and implement ETL workflows using accelerated ETL processes.
Accelerating ETL on KubeFlow with RAPIDS
RAPIDS Accelerates Data Science End-to-End
GPUs for ETL? Run Faster, Less Costly Workloads with NVIDIA RAPIDS Accelerator
2.2 Implement data caching to reduce shuffle.
Best Practices for Multi-GPU Data Analysis Using RAPIDS with Dask
2.3 Use distributed data processing frameworks to process big data.
RAPIDS on Databricks: A Guide to GPU-Accelerated Data Processing
GPUs for ETL? Run Faster, Less Costly Workloads with NVIDIA RAPIDS Accelerator
Accelerating ETL on KubeFlow with RAPIDS
2.4 Implement data parallelism using Dask for multi-GPU scaling.
Best Practices for Multi-GPU Data Analysis Using RAPIDS with Dask
2.5 Profile deep learning models using tools such as DLProf.
2.6 Determine the optimal data processing libraries to use for varying dataset sizes.
Get Started with GPU Acceleration for Data Science
RAPIDS on Databricks: A Guide to GPU-Accelerated Data Processing
2.7 Transform and standardize data using cuDF.
10 Minutes to cuDF and Dask cuDF
Accelerated Data Analytics: Speed Up Data Exploration with RAPIDS cuDF
2.8 Manage and allocate GPU memory effectively to improve performance, utilizing strategies to optimize memory.
GPU Memory Essentials for AI Performance
Best Practices for Multi-GPU Data Analysis Using RAPIDS with Dask
2.9 Scale inference for large datasets in both single-node and multi-node systems.
Fast and Scalable AI Model Deployment with NVIDIA Triton Inference Server
Deploying NVIDIA Triton at Scale with MIG and Kubernetes
Data Preparation: Exam Weight 17%
Topics Covered
3.1 Perform data cleansing and preprocessing using cuDF and pandas.
10 Minutes to cuDF and Dask cuDF
Accelerated Data Analytics: Speed Up Data Exploration with RAPIDS cuDF
NVIDIA NeMo Curator for Developers
3.2 Transform and standardize data.
10 Minutes to cuDF and Dask cuDF
3.3 Standardize data as needed to ensure uniformity across features.
3.4 Identify and acquire datasets.
Boosting Data Ingest Throughput with GPUDirect Storage and RAPIDS cuDF
3.5 Monitor data processing pipelines to recognize bottlenecks.
Best Practices for Multi-GPU Data Analysis Using RAPIDS with Dask
3.6 Process, organize, and store datasets.
Boosting Data Ingest Throughput with GPUDirect Storage and RAPIDS cuDF
Enhancing Generative AI Model Accuracy with NVIDIA NeMo Curator
GPU and Cloud Computing: Exam Weight 16%
Topics Covered
4.1 Analyze graph data using GPU-accelerated tools like cuGraph.
4.2 Optimize performance of the data science process through GPU acceleration.
Get Started with GPU Acceleration for Data Science
GPU Accelerated Data Science with RAPIDS
Get Started With Deep Learning Performance
4.3 Describe, follow, and execute the CRISP-DM process.
IBM SPSS Modeler CRISP-DM Guide
4.4 Utilize dependency management frameworks, such as Docker and Conda to manage software-versioning conflicts.
4.5 Determine the optimal data type choice for each feature.
4.6 Compare frameworks’ performance by designing and implementing a benchmark.
Furthering NVIDIA Performance Leadership with MLPerf Inference 1.1 Results
Scaling-out RAPIDS cuML and XGBoost with Dask on Google Kubernetes Engine (GKE)
RAPIDS on Databricks: A Guide to GPU-Accelerated Data Processing
Machine Learning: Exam Weight 15%
Topics Covered
5.1 Split data, choosing the best split type and method.
Training and Evaluating Machine Learning Models
Welcome to cuML’s documentation!
5.2 Standardize data as needed to ensure uniformity across features.
5.3 Identify how much data is big data (or when to use which acceleration method).
Get Started with GPU Acceleration for Data Science
RAPIDS on Databricks: A Guide to GPU-Accelerated Data Processing
5.4 Perform rapid experimentation to find the balance between model accuracy and inference performance.
Accelerating Time Series Forecasting with RAPIDS cuML
Get Started With Deep Learning Performance
Training and Evaluating Machine Learning Models
5.5 Optimize hyperparameters of machine learning models.
5.6 Train machine learning models for both single-GPU and multi-GPU scenarios.
Best Practices for Multi-GPU Data Analysis Using RAPIDS with Dask
Training and Evaluating Machine Learning Models
5.7 Use GPU memory optimization techniques, such as batching and mixed precision, to train machine learning models.
Get Started With Deep Learning Performance
GPU Memory Essentials for AI Performance
Mixed-Precision Training of Deep Neural Networks
5.8 Profile deep learning models using tools such as DLProf.
MLOps: Exam Weight 19%
Topics Covered
6.1 Determine the optimal data type choice for each feature.
6.2 Assess and verify the size in memory of a dataset.
GPU Memory Essentials for AI Performance
6.3 Compare the required memory with the available memory on a device.
GPU Memory Essentials for AI Performance
Best Practices for Multi-GPU Data Analysis Using RAPIDS with Dask
6.4 Perform benchmarking and optimize different GPU-accelerated workflows.
Furthering NVIDIA Performance Leadership with MLPerf Inference 1.1 Results
Get Started With Deep Learning Performance
RAPIDS on Databricks: A Guide to GPU-Accelerated Data Processing
6.5 Deploy and monitor models in production.
Deploying AI Deep Learning Models with NVIDIA Triton Inference Server
Protecting Sensitive Data and AI Models With Confidential Computing
Fast and Scalable AI Model Deployment with NVIDIA Triton Inference Server
Wrapping Up NVIDIA-Certified Professional: Accelerated Data Science
This guide covered every domain of the NVIDIA-Certified Professional: Accelerated Data Science (NCP-ADS) exam guide, from time-series analysis and cuGraph to GPU memory optimization and Triton deployment. With verified NVIDIA and RAPIDS documentation behind each objective, you are well equipped to build a focused study plan and pass the NCP-ADS exam with confidence. You can also explore more NVIDIA certification study guides on the NVIDIA category to keep building your skills. Have a question or tip? Leave a comment below.
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