Question 1

How many questions are on the NVIDIA Certified Associate - Accelerated Data Science exam?

Accepted Answer

Our record lists 50 questions for the NVIDIA Certified Associate - Accelerated Data Science. The vendor does not publish a question count, so treat that as a working figure rather than an official specification.

Question 2

How long do you have to complete the NVIDIA Certified Associate - Accelerated Data Science exam?

Accepted Answer

Our record lists 60 minutes, roughly 72 seconds per question. The vendor does not publish a duration for this exam, so use it as a planning figure.

Question 3

What score do you need to pass the NVIDIA Certified Associate - Accelerated Data Science exam?

Accepted Answer

Our record lists 70% as the pass mark for the NVIDIA Certified Associate - Accelerated Data Science. Most certification bodies do not publish a numeric cut score, so treat this as our benchmark rather than an official threshold.

Question 4

What are the domains on the NVIDIA Certified Associate - Accelerated Data Science exam?

Accepted Answer

There are 8 domains: Advance Data Structures, Introductory MLOps Practices, Data Manipulation and Preparation, Machine Learning With NVIDIA RAPIDS, Software and Environment Management, Descriptive Analysis and Visualization, Foundations of Accelerated Data Science, Data Science Pipelines and Workflow Automation. The official weights are Advance Data Structures 11%, Introductory MLOps Practices 10%, Data Manipulation and Preparation 23%, Machine Learning With NVIDIA RAPIDS 16%, Software and Environment Management 9%, Descriptive Analysis and Visualization 10%, Foundations of Accelerated Data Science 10%, Data Science Pipelines and Workflow Automation 11%.

Question 5

How should I use the domain weights when revising?

Accepted Answer

Weight by percentage, not by domain. 11% is the one where lost points cost you the most, and Certilum scores you per domain so you can see exactly where that is.

Question 6

Does Certilum have practice questions for the NVIDIA Certified Associate - Accelerated Data Science exam?

Accepted Answer

Yes: 50 questions in our bank for this certification, each tagged to the exam domain it belongs to, with the official domain weights applied.

Specification	Value
Questions	50
Time limit	60 minutes (72s per question)
Passing score	70%
Domains	8
Level	Associate
Question bank here	50 questions
Source	Certilum's own record — the vendor does not publish this figure

NVIDIA Certified Associate - Accelerated Data Science Exam Format

Exam facts at a glance

Official domain weights

More about the NVIDIA Certified Associate - Accelerated Data Science exam

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Frequently asked questions

Domain	Weight	Topics	Practice Qs
Advance Data Structures	11%	Creating graph structures with cuGraph Graph algorithms for shortest path, centrality, and community detection Analyzing connected components and graph statistics +1 more	6
Introductory MLOps Practices	10%	Model lifecycle management and experiment tracking Using MLflow for logging, packaging, and serving Building CI/CD pipelines for ML +1 more	5
Data Manipulation and Preparation	23%	Loading and inspecting data with cuDF Filtering, sorting, grouping, and joining DataFrames Cleaning missing values and converting data types +2 more	12
Machine Learning With NVIDIA RAPIDS	16%	cuML API and scikit-learn integration Supervised learning models: linear regression, classification, and tree ensembles Unsupervised learning: clustering and dimensionality reduction +2 more	8
Software and Environment Management	9%	Installing RAPIDS with conda and pip Using NVIDIA GPU Cloud (NGC) containers Managing dependencies and environment reproducibility +1 more	4
Descriptive Analysis and Visualization	10%	Generating descriptive statistics with GPU-accelerated methods Building histograms, scatter plots, and correlation heatmaps Creating interactive visualizations with cuxfilter +1 more	5
Foundations of Accelerated Data Science	10%	GPU architecture and CUDA fundamentals RAPIDS ecosystem and its core libraries Data transfer, memory bandwidth, and I/O bottlenecks +1 more	5
Data Science Pipelines and Workflow Automation	11%	Designing reproducible data pipelines Using Dask for parallel task scheduling Orchestrating workflows with Airflow, Prefect, or Kubeflow +1 more	5