NVIDIA Certified Associate - Accelerated Data Science
Validates foundational proficiency in leveraging GPU-accelerated tools and libraries for data science workflows, including RAPIDS cuDF for data manipulation, cuML for machine learning, data science pipeline automation, descriptive analysis and visualization, GPU computing foundations, introductory MLOps practices, advanced data structures with cuGraph, and software environment management. The exam covers eight domains: Data Manipulation and Preparation (23%), Machine Learning With RAPIDS (16%), Data Science Pipelines and Workflow Automation (13%), Descriptive Analysis and Visualization (13%), Foundations of Accelerated Data Science (12%), Introductory MLOps Practices (10%), Advance Data Structures (7%), and Software and Environment Management (6%). Format: 50-60 multiple-choice questions, 60 minutes, proctored online.
Exam domains
- Data Manipulation and Preparation23%
GPU-accelerated tabular ETL with cuDF mirroring the pandas API (groupby, joins, rolling, string ops) and Dask-cuDF for multi-GPU partitioned DataFrames. Covers ingest from CSV/Parquet/ORC, type coercion, null handling, feature engineering, and host-to-device transfers.
- Machine Learning With RAPIDS16%
Training and inference with cuML's scikit-learn-compatible estimators (linear/logistic regression, random forest, k-means, DBSCAN, UMAP, t-SNE, PCA) plus XGBoost GPU histograms and cuGraph algorithms (PageRank, BFS, Louvain). Includes hyperparameter search and cuML/sklearn interop.
- Descriptive Analysis and Visualization13%
Exploratory analysis on GPU DataFrames using cuDF describe/value_counts and crosstabs, paired with cuxfilter, hvPlot, Datashader, and Plotly Dash for interactive multi-million-row dashboards. Emphasises GPU-rendered scatter, choropleth, and time-series views without down-sampling.
- Data Science Pipelines and Workflow Automation13%
Sources
Questions are grounded in 50 references from official and authoritative materials.