Aegis-GPU

NCP-AAI · Professional · 120 min · 60–70 questions · $200

Agentic AI

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More study tools

  • Ask Aegis: the tutor button at the bottom right answers from the course content with citations.
  • Xid error reference: every in-use NVIDIA Xid code with NVIDIA's recommended actions.
  • HGX hardware guide: a beginner's tour of an 8-GPU server.

Exam facts

Level
Professional
Duration
120 minutes
Questions
60–70
Price
$200
Delivery
Online, remotely proctored
Language
English
Validity
Two years from issuance
Scoring
Pass/fail (no numeric score is reported)
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.

Exam facts verified against NVIDIA's NCP-AAI page on 2026-10-08. The official page gives one descriptive paragraph per domain (no bulleted objectives); it is reproduced as that domain's single objective. Official domain weights sum to 98%, not 100% (reproduced as published). Always confirm on nvidia.com before booking.

Official blueprint

Weights on NVIDIA's page sum to 98%, not 100%; shown as published.

Agent Architecture and Design — 15%
  1. 1.1 Foundational structuring and design of agentic AI systems, focusing on how agents interact, reason, and communicate within their environments
Agent Development — 15%
  1. 2.1 Practical building, integration, and enhancement of agents
Evaluation and Tuning — 13%
  1. 3.1 Measuring, comparing, and optimizing agent performance
Deployment and Scaling — 13%
  1. 4.1 Operationalizing and scaling agentic systems
Cognition, Planning, and Memory — 10%
  1. 5.1 Core cognitive processes underlying intelligent agent behavior, including reasoning strategies, decision-making, and memory management
Knowledge Integration and Data Handling — 10%
  1. 6.1 Integration of external knowledge and the management of diverse data types
NVIDIA Platform Implementation — 7%
  1. 7.1 Leveraging NVIDIA’s AI hardware and software platforms for agentic AI systems
Run, Monitor, and Maintain — 5%
  1. 8.1 Ongoing operation, monitoring, and maintenance of agentic systems post-deployment
Safety, Ethics, and Compliance — 5%
  1. 9.1 Principles and practices that ensure agentic AI systems operate responsibly, uphold ethical standards, and comply with legal and regulatory frameworks
Human-AI Interaction and Oversight — 5%
  1. 10.1 The design and implementation of systems that facilitate effective human oversight and interaction with agents

Study roadmap

  1. Baseline. Read the official exam page and study guide, then skim every domain below to find gaps.
  2. Agent Architecture and Design (15%). Study the NVIDIA training and docs linked below for this domain; keep notes per objective.
  3. Agent Development (15%). Study the NVIDIA training and docs linked below for this domain; keep notes per objective.
  4. Evaluation and Tuning (13%). Study the NVIDIA training and docs linked below for this domain; keep notes per objective.
  5. Deployment and Scaling (13%). Study the NVIDIA training and docs linked below for this domain; keep notes per objective.
  6. Cognition, Planning, and Memory (10%). Study the NVIDIA training and docs linked below for this domain; keep notes per objective.
  7. Knowledge Integration and Data Handling (10%). Study the NVIDIA training and docs linked below for this domain; keep notes per objective.
  8. NVIDIA Platform Implementation (7%). Study the NVIDIA training and docs linked below for this domain; keep notes per objective.
  9. Run, Monitor, and Maintain (5%). Study the NVIDIA training and docs linked below for this domain; keep notes per objective.
  10. Safety, Ethics, and Compliance (5%). Study the NVIDIA training and docs linked below for this domain; keep notes per objective.
  11. Human-AI Interaction and Oversight (5%). Study the NVIDIA training and docs linked below for this domain; keep notes per objective.
  12. Exam rehearsal. Re-read your weakest domain notes and the study guide.

Official resources

From NVIDIA