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NVIDIA · mixed

NVIDIA Certified Professional – AI Data Science and Applications

Discovered via AI catalog search. Level: professional. Blueprint, domains, and attempt settings are applied when you queue this exam in Automation. Multiple-choice exam covering end-to-end AI application development, data science pipelines, and model deployment.

Questions per attempt

65

Free attempts

1 per account

Time limit

120 minutes

Practice

Sign in to start a practice attempt and track results.

About this certification practice

Who it's for

NVIDIA Certified Professional – AI Data Science and Applications suits learners preparing for the official NVIDIA certification path—whether you are upskilling for a new role, validating existing experience, or building confidence before booking the vendor exam. Discovered via AI catalog search. Level: professional. Blueprint, domains, and attempt settings are applied when you queue this exam in Automation. Multiple-choice exam covering end-to-end AI application development, data science pipelines, and model deployment.

What you'll practice

Each attempt draws 65 multiple-choice questions covering ai application development and deployment, data processing and engineering, deep learning model development, machine learning model development, and performance optimization. After you submit, review mode shows your score, per-domain breakdown where available, and explanations for each item—so you can see which themes need another study sprint.

How Exambasics helps

Exambasics is independent, unofficial study material. We do not reproduce vendor exam items. Instead, you get scenario-based practice, clear review after submit, and a calm interface designed for repeat attempts. Sessions can run with a 120-minute limit to mirror exam pacing. New accounts can start with 1 free attempt for this exam.

Difficulty & expectations

Associate-style NVIDIA exams typically blend scenario questions across several domains. Timed practice helps you move from recognition to consistent decision-making.

Preparation tips

  • Take a baseline attempt early—even if you have not finished a course—so you know which domains (ai application development and deployment, data processing and engineering, deep learning model development, machine learning model development, and performance optimization) need the most work.
  • Review explanations for every miss; tag whether the error was a concept gap, a misread stem, or a trap distractor.
  • Schedule full-length timed attempts in the final third of your study plan, not only the night before the real exam.
  • Pair practice with hands-on labs or console work when the certification level expects operational experience.

What you'll practice

  • AI Application Development and Deployment~20%
  • Data Processing and Engineering~20%
  • Deep Learning Model Development~20%
  • Machine Learning Model Development~20%
  • Performance Optimization~20%

How this exam works

  • Timed mode — optional time limit per exam when configured.
  • Review mode — after submit, see score, explanations, and domain breakdown.
  • Vendor-neutral — independent study material; not affiliated with certification vendors.

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