NVIDIA Certified Professional - Agentic AI
Validates the ability to architect, develop, deploy, and govern advanced agentic AI solutions with a focus on multi-agent interaction, distributed reasoning, scalability, and ethical safeguards using NVIDIA platforms. Covers agent architecture and design, agent development with tool calling and function calling, evaluation and tuning, deployment and scaling, cognition planning and memory management, knowledge integration and data handling with RAG pipelines, NVIDIA platform implementation with NIM and NeMo, run monitoring and maintenance, safety ethics and compliance, and human-AI interaction and oversight. The exam covers ten domains: Agent Architecture and Design (15%), Agent Development (15%), Evaluation and Tuning (13%), Deployment and Scaling (13%), Cognition, Planning, and Memory (10%), Knowledge Integration and Data Handling (10%), NVIDIA Platform Implementation (7%), Run, Monitor, and Maintain (5%), Safety, Ethics, and Compliance (5%), and Human-AI Interaction and Oversight (5%). Format: 60-70 multiple-choice questions, 120 minutes, proctored online.
Exam domains
- Agent Development15%
Building agent workflows with NeMo Agent Toolkit Functions and Function Groups, OpenAI-compatible function calling on Llama 3.1 NIMs, LangChain/LangGraph, and NIM Agent Blueprints. Covers Pydantic tool schemas, structured outputs, middleware, and FastAPI streaming endpoints.
- Agent Architecture and Design15%
Designing single- and multi-agent systems with patterns like ReAct, ReWOO, and hierarchical planning using NVIDIA NeMo Agent Toolkit (AIQ), LangGraph, and LlamaIndex. Covers router/grader/hallucination-checker decomposition, Tools-as-Services, and MCP/A2A inter-agent protocols.
- Evaluation and Tuning13%
Assessing agents with the NeMo Agent Toolkit evaluation harness — dataset loaders, evaluators, callbacks — plus LM Evaluation Harness and RAG evaluation in NeMo Microservices. Covers DPO/OpenPipe ART fine-tuning, SteerLM/RLHF alignment, and tool-call accuracy iteration.
- Deployment and Scaling13%
Packaging agents and underlying LLMs as NVIDIA NIM microservices on Triton Inference Server with TensorRT-LLM/vLLM backends, exposed via OpenAI-compatible APIs. Covers Kubernetes deployment with the GPU Operator, autoscaling, dynamic batching, and FastAPI agent servers.
Sources
Questions are grounded in 56 references from official and authoritative materials.