Master NVIDIA Certified Professional Agentic AI : (NCP-AAI)

Explore deployment models from saas to on-premises and edge, and master integration surfaces—apis, tools, data connectors, and policy gateways—for agentic ai.

Explore how Agentic AI creates real business value through automation, speed, consistency, traceability, and scalability, with knowledge management, customer operations, and document automation from pilot to scale.

Analyze how agents reason, plan, and memory interact to prevent failures, using reflection, memory models (working and long-term), session context, and memory hygiene.

Explore how batching and concurrency affect GPU inference, balancing latency, throughput, and cost, and learn when Triton and TensorRT optimize production workloads.

Explore NVIDIA's full stack for agentic AI, from hardware and CUDA acceleration to Triton serving, Nemo and NIM deployment, enabling scalable, reliable production inference.

Define evaluation harnesses and run them against test cases to measure correctness, structure, grounding, and regression; monitor production with latency, cost, output stability, and failure diagnosis via telemetry and tracing.

Explore safety and guardrails for autonomous agents, including threat models, prompt injection defenses, and human-in-the-loop approval workflows. Implement audit logs, safety filters, and compliance measures for responsible operation.

Set up a development environment to run llm inference endpoints, embedding models for vector search, local models, and state persistence, validated by the SMOC test.

Create a Python virtual environment, upgrade pip, and install dependencies, then copy .env.example to .env and add your LLM API key from Grok or a similar provider.

Validate your AI setup with an environment smoke test; build a minimal acting agent that loops a basic action and verify LLM inference, embeddings, and memory store.

Explore optional labs from 1.1 to 10.3 that teach Agentic AI concepts, tool calling, function schemas, and structured JSON outputs, with hands-on practice and exam requirements.

explore how agent states and observations enable context-aware decisions, with state dictionary updates, step counter, and tool interfaces built on schema validation and argument checks.

Advance agentic ai labs by mastering complex state management, smarter action policies, and deeper environment interactions, with planning, memory persistence, and retrieval augmented generation workflows.