Aegis-GPU

NCA-ADS · Associate · 60 min · 50–60 questions · $125

Accelerated Data Science

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More study tools

  • Ask Aegis: the tutor button at the bottom right answers from the course content with citations.
  • Xid error reference: every in-use NVIDIA Xid code with NVIDIA's recommended actions.
  • HGX hardware guide: a beginner's tour of an 8-GPU server.

Exam facts

Level
Associate
Duration
60 minutes
Questions
50–60
Price
$125
Delivery
Online, remotely proctored
Language
English
Validity
Two years from issuance
Scoring
Pass/fail (no numeric score is reported)
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.

Exam facts verified against NVIDIA's NCA-ADS page on 2026-10-08. Always confirm on nvidia.com before booking.

Official blueprint

Data Manipulation and Preparation — 23%
  1. 1.1 Data integration, joining, and manipulation using NVIDIA cuDF and pandas
  2. 1.2 Data cleaning, quality handling, and governance compliance
  3. 1.3 GPU-accelerated ETL workflows with RAPIDS, Dask, or Spark
  4. 1.4 Feature engineering for numerical and categorical variables
  5. 1.5 Handling class imbalance and generating synthetic data
  6. 1.6 Dimensionality reduction and data sampling
  7. 1.7 Efficient processing and storage with Parquet and modern frameworks
Machine Learning With RAPIDS — 16%
  1. 2.1 GPU-accelerated model training with NVIDIA cuML and XGBoost
  2. 2.2 Regression, classification, and clustering techniques
  3. 2.3 Model evaluation, comparison, and generalization assessment
  4. 2.4 Hyperparameter tuning and optimization
  5. 2.5 Cross-validation methods
  6. 2.6 Performance metrics and confusion matrix interpretation
Data Science Pipelines and Workflow Automation — 13%
  1. 3.1 End-to-end data science pipeline design
  2. 3.2 Feature engineering, selection, and transformation for model improvement
  3. 3.3 Mitigating underfitting and overfitting through model and feature adjustments
  4. 3.4 Dataset augmentation and integration for enhanced training data
  5. 3.5 Automation and scalability of data science workflows
  6. 3.6 Building reproducible pipelines with RAPIDS and Dask
Descriptive Analysis and Visualization — 13%
  1. 4.1 Exploratory data analysis (EDA) and descriptive statistics
  2. 4.2 Visualization
  3. 4.3 Selecting appropriate plots for different analysis goals
  4. 4.4 Hypothesis testing and statistical significance evaluation
  5. 4.5 Interpreting patterns, trends, and relationships in data
Foundations of Accelerated Data Science — 12%
  1. 5.1 Python fundamentals for data analysis (NumPy, pandas, Jupyter)
  2. 5.2 Core GPU acceleration concepts and advantages for data science
  3. 5.3 CPU vs. GPU workloads and memory transfer optimization
  4. 5.4 End-to-end data science workflow (ingest, ETL, clean, transform)
  5. 5.5 Distributed vs. GPU-accelerated computing frameworks
  6. 5.6 Model parameters, tuning, and overfitting vs. underfitting concepts
Introductory MLOps Practices — 10%
  1. 6.1 Monitoring and optimizing machine learning (ML) pipelines for performance and reliability
  2. 6.2 Managing and tracking experiments with MLflow, Weights & Biases, and custom tools
  3. 6.3 Model saving, loading, and prediction generation
  4. 6.4 Monitoring production models for drift and performance degradation
  5. 6.5 Managing model artifacts and configurations for reproducibility
  6. 6.6 Benchmarking workflows and selecting optimal hardware
Advance Data Structures — 7%
  1. 7.1 Time-series data handling, splitting, and forecasting evaluation
  2. 7.2 Managing missing or irregular timestamps with cuDF interpolation
  3. 7.3 CPU vs. GPU performance for temporal analytics
  4. 7.4 Graph-based data representation and analysis
  5. 7.5 Node importance evaluation and network relationship visualization
Software and Environment Management — 6%
  1. 8.1 Contributing to reproducibility in data science projects by maintaining environment files
  2. 8.2 Configuring reproducible Python environments using Conda, PIP, or Docker
  3. 8.3 Managing software dependencies efficiently and collaborating in multi-user data science environments
  4. 8.4 Performing GPU environment check (driver/CUDA/RAPIDS compatibility, nvidia-smi, device visibility) and resolving a dependency conflict
  5. 8.5 Understanding the basics of version control using git

Study roadmap

  1. Baseline. Read the official exam page and study guide, then skim every domain below to find gaps.
  2. Data Manipulation and Preparation (23%). Study the NVIDIA training and docs linked below for this domain; keep notes per objective.
  3. Machine Learning With RAPIDS (16%). Study the NVIDIA training and docs linked below for this domain; keep notes per objective.
  4. Data Science Pipelines and Workflow Automation (13%). Study the NVIDIA training and docs linked below for this domain; keep notes per objective.
  5. Descriptive Analysis and Visualization (13%). Study the NVIDIA training and docs linked below for this domain; keep notes per objective.
  6. Foundations of Accelerated Data Science (12%). Study the NVIDIA training and docs linked below for this domain; keep notes per objective.
  7. Introductory MLOps Practices (10%). Study the NVIDIA training and docs linked below for this domain; keep notes per objective.
  8. Advance Data Structures (7%). Study the NVIDIA training and docs linked below for this domain; keep notes per objective.
  9. Software and Environment Management (6%). Study the NVIDIA training and docs linked below for this domain; keep notes per objective.
  10. Exam rehearsal. Re-read your weakest domain notes and the study guide.

Official resources

From NVIDIA