Study Guide

NVIDIA Certified Professional: Agentic AI Study Guide

Use the saved domain outline to connect agent architecture, design, and development, evaluation, tuning, and quality optimization, knowledge integration, data handling, cognition, planning, and memory, nvidia platform implementation and production operations to scenario-based questions and explanations.

Download App Free Practice Exam Key Terms Glossary

How the Exam Is Structured

NVIDIA Certified Professional: Agentic AI (NCP-AAI) validates agent architecture, design, and development, evaluation, tuning, and quality optimization, knowledge integration, data handling, cognition, planning, and memory, nvidia platform implementation and production operations. The ExamPal practice bank includes 131 premium questions and 40 free questions mapped across the official blueprint.

Domain	Weight	Focus
Domain 1: Agent Architecture, Design, and Development	20%	Task 1.1: Select appropriate AI agent architectural patterns for business and technical requirements; Compare architectural patterns
Domain 2: Evaluation, Tuning, and Quality Optimization	18%	Task 2.1: Define evaluation strategies for AI agents and compound AI systems; Select evaluation approaches
Domain 3: Knowledge Integration, Data Handling, Cognition, Planning, and Memory	22%	Task 3.1: Design retrieval-augmented generation and knowledge access architectures; Select retrieval architectures
Domain 4: NVIDIA Platform Implementation and Production Operations	16%	Task 4.1: Implement AI architectures using NVIDIA AI platform capabilities; Identify platform components
Domain 5: Deployment, Scaling, Safety, and Compliance	12%	Task 5.1: Design deployment architectures for reliability and scale; Select deployment patterns
Domain 6: Human-AI Interaction and Oversight	12%	Task 6.1: Design user interaction patterns for AI-assisted workflows; Select interface patterns

20% of exam

Domain 1: Agent Architecture, Design, and Development

Covers core agent architecture choices, workflow design, context/state management, tool use, prompting, and multimodal/streaming experiences. This domain emphasizes selecting patterns that meet business and technical requirements while supporting robust, adaptable agent behavior.

Task 1.1: Select appropriate AI agent architectural patterns for business and technical requirements

Compare architectural patterns

Match architecture to requirements

Identify architecture tradeoffs

Recommend architectures for use cases

Task 1.2: Design agent workflows that balance reactive and deliberative behavior

Distinguish response patterns

18% of exam

Domain 2: Evaluation, Tuning, and Quality Optimization

Covers evaluation design, metrics, dataset creation, tuning, experimentation, and monitoring for degradation and bias. This domain emphasizes aligning quality optimization with product goals such as accuracy, latency, cost, and safety.

Task 2.1: Define evaluation strategies for AI agents and compound AI systems

Select evaluation approaches

Align evaluation with product goals

Differentiate evaluation scope

Establish success criteria

Task 2.2: Choose and interpret task-appropriate metrics

Select quality metrics

22% of exam

Domain 3: Knowledge Integration, Data Handling, Cognition, Planning, and Memory

Covers retrieval-augmented generation, document processing, enterprise data integration, storage and retrieval technologies, planning and reasoning, and memory architectures. This domain focuses on grounding agent behavior in reliable knowledge and supporting adaptive cognition.

Task 3.1: Design retrieval-augmented generation and knowledge access architectures

Select retrieval architectures

Match retrieval to requirements

Design grounded knowledge flows

Optimize retrieval pipelines

Task 3.2: Define document processing, chunking, and embedding strategies

Choose chunking methods

16% of exam

Domain 4: NVIDIA Platform Implementation and Production Operations

Covers implementing AI architectures with NVIDIA platform capabilities, production-ready inference and orchestration, observability, operations, and lifecycle management. The domain emphasizes reliable delivery, monitoring, and maintainability of AI systems in production.

Task 4.1: Implement AI architectures using NVIDIA AI platform capabilities

Identify platform components

Map requirements to capabilities

Select implementation patterns

Integrate platform services

Task 4.2: Build production-ready inference and orchestration pipelines

Design inference paths

12% of exam

Domain 5: Deployment, Scaling, Safety, and Compliance

Covers deployment architecture, performance and capacity optimization, security and safety controls, and governance/compliance requirements. This domain emphasizes reliable scaling, least-privilege controls, and audit-ready deployment practices.

Task 5.1: Design deployment architectures for reliability and scale

Select deployment patterns

Choose resilient architectures

Match scaling strategies

Design for operational load

Task 5.2: Optimize system performance and capacity

Balance performance targets

12% of exam

Domain 6: Human-AI Interaction and Oversight

Covers user interaction design, transparency and trust, human oversight and escalation, and feedback-driven governance. This domain focuses on making AI-assisted workflows understandable, controllable, and safely supervised by humans.

Task 6.1: Design user interaction patterns for AI-assisted workflows

Select interface patterns

Match interaction design

Design for ambiguity resolution

Support multimodal interaction

Task 6.2: Improve usability, transparency, and trust

Present evidence in outputs

Key Terms to Know

These terms are loaded from the shared terminology pack and appear across the question explanations.

JSON-over-HTTP: A communication pattern that sends JSON payloads over standard HTTP, often used for interoperable web APIs.
Milvus: An open-source distributed vector database designed for large-scale similarity search and horizontal scalability.
Protocol Buffers: A compact binary serialization format used with gRPC that supports efficient messaging and schema evolution.
ROUGE: A summarization evaluation metric that measures overlap between generated text and reference summaries.
ReAct agent: An agent architecture that alternates between reasoning steps and action steps to solve tasks iteratively.
agent coordination: The process by which multiple agents synchronize actions, share information, and work toward a common goal.
automated retraining: A pipeline that updates or fine-tunes models automatically based on new data, feedback, or evaluation results.
case-based reasoning: A problem-solving approach that stores past successful cases and retrieves similar examples to guide new decisions.
chain-of-thought prompting: A prompting method that encourages a model to generate intermediate reasoning steps before producing an answer.
chunking: The process of splitting large documents or inputs into smaller segments for processing, indexing, or summarization.
context management: Techniques for selecting, storing, and presenting relevant prior information to a model during interaction.
context-aware suggestions: Recommendations or prompts generated based on the current conversation state, user intent, or surrounding context.
continuous improvement loop: A recurring process that collects user feedback, analyzes it, and applies changes to improve system performance over time.
conversational UI: A user interface that supports natural multi-turn dialogue between a user and a system or agent.
cross-encoder: A model that jointly encodes a query and candidate document to produce a high-quality relevance score for reranking.
distributed architecture: A system design where computation and storage are spread across multiple machines to improve scale and resilience.
expert reviewers: Human evaluators with domain knowledge who assess model outputs for quality, correctness, or usefulness.
faithfulness: An evaluation criterion measuring whether generated content accurately reflects source information without hallucination.

Official Materials and Guidance

This page is built from NVIDIA official materials and ExamPal shared release pack, the shared syllabus, topic tree, terminology pack, free pack, and premium pack.

-Guidance: NVIDIA official certification page/outline saved locally
-Domain outline: Agent architecture/design 15%; Agent development 15%; Evaluation/tuning 13%; Deployment/scaling 13%; Cognition/planning/memory 10%; Knowledge/data handling 10%; NVIDIA platform 7%; Run/monitor/maintain 5%; Safety/ethics/compliance 5%; Human-AI oversight 5%.

Download App Official source Start Free Practice Exam