Manifold AI Learning

NVIDIA NCP-AAI Agentic AI Professional What to Study AFTER Registering for the Exam (Complete Guide)

•
  • nvidia-ncpaai
  • agentic-ai
Get NVIDIA NCP AAI prep Pack

Just registered for NVIDIA NCP-AAI certification?

Here's your complete study guide.

Exam details: • 60-70 multiple choice questions • 120 minutes (2 hours) • Pass score: 70% (need 42-49 correct) • Cost: $200 • Valid for 2 years

What's tested (10 domains):

HIGH-WEIGHT (43%): • Agent Architecture and Design - 15% • Agent Development - 15% • Evaluation and Tuning - 13%

MEDIUM-WEIGHT (27%): • Cognition, Planning, and Memory - 10% • Knowledge Integration and Data - 10% • NVIDIA Platform Implementation - 7%

LOW-WEIGHT (30%): • Run, Monitor, and Maintain - 7% • Deployment and Scaling - 5% • Safety, Ethics, and Compliance - 5% • Human-AI Interaction and Oversight - 5%

📊 6-WEEK STUDY PLAN:

Weeks 1-2: Architecture + Development (30% of exam)

  • ReAct frameworks, LangGraph workflows
  • Tool integration, error handling
  • BUILD: 3 agents with different patterns

Week 3: Evaluation + Cognition (23% of exam)

  • Evaluation pipelines, A/B testing
  • CoT reasoning, memory management
  • EVALUATE: Your agents

Week 4: Data + NVIDIA Platform (17% of exam)

  • RAG systems, vector databases
  • NeMo Guardrails, NIM, TensorRT-LLM, Triton
  • BUILD: RAG system with guardrails

Week 5: Deployment + Operations (12% of exam)

  • Containerization, monitoring, logging
  • DEPLOY: One agent to production

Week 6: Safety + Human Interaction + Review (10%)

  • Compliance, bias mitigation
  • REVIEW: All topics

📚 STUDY RESOURCES:

NVIDIA Official Courses (FREE - MUST TAKE): → Building Agentic AI Applications with LLMs → Building RAG Agents with LLMs → Deploying RAG Pipelines for Production at Scale → Evaluating RAG and Semantic Search Systems

Documentation: → LangGraph (complete docs) → NVIDIA NeMo → LangChain agents → Agent Intelligence Toolkit

Papers: → ReAct paper → "Understanding the Planning of LLM Agents: A Survey"

Hands-On (MOST IMPORTANT): → Build 5+ agents → Implement RAG with evaluation → Deploy and monitor

🎯 EXAM STRATEGY:

Focus on high-weight topics: Architecture (15%) + Development (15%) + Evaluation (13%) = 43% of exam

NVIDIA platform is critical: NeMo Guardrails, NIM, TensorRT-LLM, Triton = 7% of exam

The exam tests PRODUCTION knowledge: Not "What is X?" but "How would you solve Y in production?"

Timeline: 4-6 weeks if working full-time with agents 2-3 weeks if building production agents daily 8-10 weeks if newer to the field

Resources