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NVIDIA · Data & Analytics · Associate

NVIDIA-Certified Associate: Accelerated Data Science exam guide

Exam NCA-ADS

NCA-ADS validates the ability to use GPU-accelerated tools such as RAPIDS (cuDF, cuML) for data preparation, machine learning, analytics and basic MLOps. GPU-accelerated pandas and scikit-learn workflows now cut pipeline times sharply, so the skill is practical for data teams in 2026.

Questions50-60
Time limit60 minutes
Passing scoreSee official page
Exam fee$125 USD
Valid for2 years
DeliveryOnline, remotely proctored (Certiverse account required)

Format: Multiple choice.

Who it's for

Data scientists and analysts with 1-2 years of experience who want to prove they can move Python data workflows onto GPUs.

What the exam covers

Data Manipulation and Preparation23%
Machine Learning With RAPIDS16%
Data Science Pipelines and Workflow Automation13%
Descriptive Analysis and Visualization13%
Foundations of Accelerated Data Science12%
Introductory MLOps Practices10%
Advanced Data Structures7%
Software and Environment Management6%

Eligibility & prerequisites

Required: None formally; NVIDIA lists 1-2 years of accelerated data science experience as the expected background.

Recommended: 1-2 years using GPU-based tools on large datasets for ML, ETL and analytics; recommended courses include Accelerating End-to-End Data Science Workflows.

Keeping it active

Valid 2 years from issuance. Recertify by retaking the current version of the exam before it expires.

Skills & careers

Skills it validates

  • cuDF for GPU dataframes and pandas acceleration
  • cuML for GPU machine learning
  • Feature engineering on tabular data
  • Building end-to-end data pipelines
  • Descriptive statistics and visualization at scale
  • Environment management with conda and containers

Job roles

Data ScientistMachine Learning EngineerData AnalystAnalytics EngineerData Engineer

Where it fits in your path

Planning a degree too? See how this cert earns college credit in the College Credit Navigator.

Build my study plan

  1. Week 1: Foundations: GPU vs CPU for data work, the RAPIDS ecosystem, and the cudf.pandas accelerator mode.
  2. Week 2: Data manipulation with cuDF: joins, groupby, string ops, datetime handling; compare timings against pandas.
  3. Week 3: cuML models, feature engineering for tabular data, time series course; build a pipeline end to end.
  4. Week 4: MLOps basics, environment management, visualization; official study guide and practice questions.

Make it yours: pick your exam date and weekly hours. We'll split your time by domain weight and give you a printable plan.

Exam-day tips

  • Data manipulation is the biggest domain at 23%; practise cuDF APIs until they are second nature.
  • Know which pandas or scikit-learn operations have direct RAPIDS equivalents and which fall back to CPU.
  • Expect questions about memory limits on GPUs and techniques like chunking or Dask-cuDF.
  • Do not ignore environment questions (CUDA versions, containers, conda); they are easy points.

Key terms for this exam

All 17 terms Study with thinQcards™

Frequently asked questions

How much does the NCA-ADS exam cost?

$125 per attempt, booked through Certiverse.

How long is NCA-ADS valid?

Two years from issuance. You recertify by retaking the exam.

Is the exam online?

Yes. NVIDIA delivers its certification exams online with remote proctoring through Certiverse.

Do I need to know CUDA programming?

No. The exam targets Python data tools such as RAPIDS rather than writing CUDA kernels.

NCA-ADS vs NCP-ADS?

NCA-ADS is the 60-minute associate exam; NCP-ADS is the 120-minute professional exam that expects 2-3 years of experience and goes deeper into MLOps and GPU and cloud computing.

Official page: www.nvidia.com. Reviewed November 2026. Vendors change fees and objectives, so confirm with the vendor before you book. Spot something out of date? Tell us.