NVIDIA Agentic AI Professional Certification Path
Step-by-step overview of NVIDIA's Agentic AI certification path, covering AI agents, multi-agent systems, planning, tool use, evaluation, governance, deployment, and preparation strategies for building production-ready Agentic AI applications.
NVIDIA Agent-Based AI (NCP-AGENT) Professional Certification Path
1. Nvidia-Certified Professional: Agent-Based AI (NCP-AGENT)
- cost: $250 (~ €200)
- duration: 120 minutes
- Question: 80
- validity: 2 Years
- Exam: https://www.certiverse.com/#/dashboard
- Module 1: Introduction to Agentic Workflows
- Module 2: Reflection Design Pattern
- Module 3: Tool use
- Module 4: Practical Tips for Building Agentic AI
- Module 5: Patterns for Highly Autonomous Agents
Coverage:
| Topic Area | Weight | What You’re Expected to Know |
|---|---|---|
| Agent Architecture and Design | 15% | Multi-agent systems, agent workflows, orchestration patterns, tool use, communication protocols, reasoning loops, autonomous vs semi-autonomous agents |
| Agent Development | 15% | Building agents with frameworks, integrating APIs/tools, prompt engineering, RAG pipelines, function calling, workflow chaining |
| Evaluation and Tuning | 13% | Benchmarking agents, evaluation metrics, hallucination reduction, latency optimization, cost/performance tradeoffs, prompt tuning |
| Deployment and Scaling | 13% | Containerization, inference scaling, distributed systems, GPU utilization, Kubernetes, production deployment strategies |
| Cognition, Planning, and Memory | 10% | Chain-of-thought, planning algorithms, memory systems, context windows, episodic vs semantic memory, reasoning strategies |
| Knowledge Integration and Data Handling | 10% | Vector databases, embeddings, RAG, structured/unstructured data pipelines, retrieval optimization |
| NVIDIA Platform Implementation | 7% | NVIDIA AI stack, CUDA, TensorRT, NIMs, NeMo, Triton Inference Server, DGX ecosystem |
| Run, Monitor, and Maintain | 5% | Observability, logging, tracing, telemetry, drift detection, monitoring inference systems |
| Safety, Ethics, and Compliance | 5% | AI governance, bias mitigation, security, privacy, guardrails, compliance principles |
| Human-AI Interaction and Oversight | 5% | Human-in-the-loop systems, feedback mechanisms, approval workflows, UX for AI agents |