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NVIDIA · Data & Analytics · Professional

NVIDIA-Certified Professional: Accelerated Data Science exam guide

Exam NCP-ADS

NCP-ADS is NVIDIA's professional data science credential, covering GPU-accelerated data preparation, analysis, machine learning, MLOps and cloud GPU computing. It shows you can design end-to-end accelerated workflows, not just run RAPIDS commands.

Questions60-70
Time limit120 minutes
Passing scoreSee official page
Exam fee$200 USD
Valid for2 years
DeliveryOnline, remotely proctored (Certiverse account required)

Format: Multiple choice.

Who it's for

Data scientists and ML engineers with 2-3 years of hands-on accelerated data science and GPU optimization experience.

What the exam covers

Data Manipulation and Software Literacy19%
MLOps19%
Data Preparation17%
GPU and Cloud Computing16%
Machine Learning15%
Data Analysis14%

Eligibility & prerequisites

Required: None formally; NVIDIA expects 2-3 years of hands-on accelerated data science experience.

Recommended: Strong ML foundation, GPU-based optimization and data manipulation experience, and understanding of workflows from data prep to deployment.

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

  • RAPIDS cuDF, cuML and Dask at scale
  • GPU memory and performance optimization
  • Feature engineering pipelines
  • MLOps: tracking, deployment, monitoring
  • GPU cloud instance selection and cost trade-offs
  • Model training with XGBoost and cuML on GPUs

Job roles

Senior Data ScientistMachine Learning EngineerMLOps EngineerData EngineerAI Platform 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. Weeks 1-2: Accelerating End-to-End Data Science Workflows course; cuDF and Dask-cuDF on datasets larger than GPU memory.
  2. Weeks 3-4: ML on GPUs: cuML, GPU XGBoost, hyperparameter search; compare against CPU baselines.
  3. Week 5: MLOps: experiment tracking, model registry, Triton serving, monitoring and retraining.
  4. Week 6: GPU and cloud computing: instance types, multi-GPU scaling, cost and performance trade-offs.
  5. Week 7: 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

  • MLOps and Data Manipulation are each 19%; do not treat MLOps as an afterthought.
  • Expect scenario questions on scaling beyond one GPU (Dask, multi-node) and memory management.
  • Know how to diagnose why a pipeline is not getting GPU speed-up (CPU fallbacks, data transfer overheads).
  • Questions often compare options by cost and time, so think about the whole workflow.

Key terms for this exam

All 2 terms Study with thinQcards™

Frequently asked questions

How much does the NCP-ADS exam cost?

$200 per attempt, booked through Certiverse.

How long is NCP-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.

Should I take NCA-ADS first?

It is optional. If you have less than two years of GPU data science experience, NCA-ADS is a good stepping stone.

Is NCP-ADS worth it for data scientists?

Yes if your team uses GPUs for data processing or ML; it is one of the few exams that tests GPU-accelerated data workflows specifically.

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.