Aegis-GPU

NCP-ADS · Professional · 120 min · 60–70 questions · $200

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
Professional
Duration
120 minutes
Questions
60–70
Price
$200
Delivery
Online, remotely proctored
Language
English
Validity
Two years from issuance
Scoring
Pass/fail (no numeric score is reported)
Prerequisites
Two to three years of hands-on experience in accelerated data science. Strong foundation in machine learning and GPU-accelerated computing. Experience in GPU-based optimization strategies and accelerated data manipulation techniques. Deep understanding of end-to-end data science workflows, from data preparation and cleansing to model development and deployment, with a focus on leveraging GPU acceleration for enhanced performance and efficiency.

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

Official blueprint

Data analysis — 14%
  1. 1.1 Detecting anomalies in a time-series dataset
  2. 1.2 Conducting time-series analysis
  3. 1.3 Creating and analyzing graph data using something like cuGraph
  4. 1.4 Identifying how much data is big data (or when to use which acceleration method)
  5. 1.5 Performing exploratory data analysis (EDA)
  6. 1.6 Visualizing time-series data
Data manipulation and software literacy — 19%
  1. 2.1 Designing and implementing extract, transform, and load (ETL) workflows using accelerated ETL processes
  2. 2.2 Implementing data caching to reduce shuffle
  3. 2.3 Using distributed data processing frameworks for processing big data
  4. 2.4 Implementing data parallelism using Dask for multi-GPU scaling
  5. 2.5 Profiling deep learning models using tools such as DLProf
  6. 2.6 Determining the optimal data processing library to use for varying dataset sizes
Data preparation — 17%
  1. 3.1 Performing data cleansing and preprocessing using CuDF and pandas
  2. 3.2 Transforming and standardizing data
  3. 3.3 Standardizing data as needed to ensure uniformity across features
  4. 3.4 Generating synthetic data to augment datasets using cuDF and NVIDIA RAPIDS
  5. 3.5 Identifying and acquiring datasets
  6. 3.6 Monitoring data processing pipelines to recognize bottlenecks
  7. 3.7 Processing, organizing, and storing datasets
GPU and cloud computing — 16%
  1. 4.1 Analyzing graph data using GPU-accelerated tools like cuGraph
  2. 4.2 Optimizing performance of the data science process through GPU acceleration
  3. 4.3 Describing, following, and executing the CRISP-DM process
  4. 4.4 Utilizing dependency management frameworks, such as Docker and Conda to manage software-versioning conflicts
  5. 4.5 Determining the optimal data type choice for each feature
  6. 4.6 Comparing frameworks' performance by designing and implementing a benchmark
Machine learning — 15%
  1. 5.1 Feature engineering
  2. 5.2 Identifying how much data is big data (or when to use which acceleration method)
  3. 5.3 Performing rapid experimentation to find the balance between model accuracy and inference performance
  4. 5.4 Optimizing hyperparameters of machine learning models
  5. 5.5 Training machine learning models, comparing single-GPU and multi-GPU scenarios
  6. 5.6 Using GPU memory-optimization techniques, such as batching and mixed precision, to train machine learning models
MLOps — 19%
  1. 6.1 Determining the optimal data type choice for each feature
  2. 6.2 Assessing and verifying the memory size of a dataset
  3. 6.3 Comparing the required memory with the available memory on a device
  4. 6.4 Performing benchmarking and optimizing different GPU-accelerated workflows
  5. 6.5 Deploying and monitoring models in production

Study roadmap

  1. Baseline. Read the official exam page and study guide, then skim every domain below to find gaps.
  2. Data manipulation and software literacy (19%). Study the NVIDIA training and docs linked below for this domain; keep notes per objective.
  3. MLOps (19%). Study the NVIDIA training and docs linked below for this domain; keep notes per objective.
  4. Data preparation (17%). Study the NVIDIA training and docs linked below for this domain; keep notes per objective.
  5. GPU and cloud computing (16%). Study the NVIDIA training and docs linked below for this domain; keep notes per objective.
  6. Machine learning (15%). Study the NVIDIA training and docs linked below for this domain; keep notes per objective.
  7. Data analysis (14%). Study the NVIDIA training and docs linked below for this domain; keep notes per objective.
  8. Exam rehearsal. Re-read your weakest domain notes and the study guide.

Official resources

From NVIDIA