NVIDIA-Certified Professional: Agentic AI (NCP-AAI)

About This Certification

The Agentic AI LLMs professional certification is an intermediate-level credential that validates a candidate’s ability to architect, develop, deploy, and govern advanced agentic AI solutions, with a focus on multi-agent interaction, distributed reasoning, scalability, and ethical safeguards. The exam is online and proctored remotely, includes 60–70 questions, and has a 120-minute time limit.

Please carefully review our certification FAQs and exam policies before scheduling your exam.

Certification Exam Details

Duration: 120 minutes

Price: $200

Certification level: Professional

Subject: Agentic AI

Number of questions: 60–70

Prerequisites: 1–2 years of experience in AI/ML roles and hands-on work with production-level agentic AI projects. Strong knowledge of agent development, architecture, orchestration, multi-agent frameworks, and the integration of tools and models across various platforms. Experience with evaluation, observability, deployment, user interface design, reliability guardrails, and rapid prototyping platforms is also essential for ensuring robust and scalable agentic AI solutions.

Language: English

Validity: This certification is valid for two years from issuance. Recertification may be achieved by retaking the exam.

Credentials: Upon passing the exam, participants will receive a digital badge and optional certificate indicating the certification level and topic.

Topics Covered in the Exam

  • Agent Design and Cognition: Architect agents, apply reasoning and planning, manage memory, and coordinate multi-agent workflows.
  • Knowledge Integration and Agent Development: Implement retrieval pipelines, handle data, engineer prompts, and build multimodal, reliable agents.
  • NVIDIA Platform Implementation and Deployment: Use NVIDIA tools to optimize inference, deploy at scale, and manage production workflows.
  • Evaluation, Monitoring, and Maintenance: Benchmark and tune performance, monitor live systems, troubleshoot issues, and ensure continuous improvement.
  • Human, Ethical, and Compliance Considerations: Design human-in-the-loop systems, enforce safety and compliance guardrails, and uphold responsible AI practices.

Candidate Audiences

  • Software developers
  • Software engineers
  • Solutions architects
  • Machine learning engineers
  • Data scientists
  • AI strategists
  • AI specialists

Exam Study Guide

Review study guide