How difficult is the NCP-AAI certification exam?

The exam is intermediate-level and requires 1-2 years of hands-on experience with production agentic AI systems. It covers 10 domains including architecture, development, deployment, and governance. Candidates should have strong knowledge of agent frameworks, RAG pipelines, and NVIDIA's AI platform.

What is the passing score for this certification?

NVIDIA does not publicly disclose the exact passing score. The exam contains 60-70 questions and candidates have 120 minutes to complete it.

How long does the certification remain valid?

The NCP-AAI certification is valid for 2 years from the date of issuance. To maintain your certification, you must retake and pass the exam before it expires.

What training is recommended to prepare for this exam?

NVIDIA offers several recommended courses: 'Building RAG Agents With LLMs' ($90, 8 hours), 'Evaluating RAG and Semantic Search Systems' ($30, 3 hours), and instructor-led workshops on agentic AI applications, knowledge integration, and production deployment ($500 each, 8 hours).

Do I need prior NVIDIA certifications to take this exam?

No, there are no prerequisite certifications required. However, you should have 1-2 years of experience in AI/ML roles with hands-on involvement in production-level agentic AI projects.

What technologies should I be familiar with before taking this exam?

You should have experience with NVIDIA's platform (NIM, NeMo Retriever, NeMo Guardrails), LangChain/LangGraph frameworks, RAG architectures, vector databases, Python programming, and prompt engineering techniques.

Is this certification recognized by employers?

Yes, this is an official NVIDIA certification that validates cutting-edge skills in agentic AI development. Upon passing, you receive a digital badge and optional certificate. NVIDIA is a recognized leader in AI technology, making this credential valuable for career advancement.

Can I take the exam remotely?

Yes, the exam is delivered online and is remotely proctored via the Certiverse platform, allowing you to take it from anywhere with a stable internet connection.

What is the exam fee?

The exam costs $200 USD. You'll need to create a Certiverse account to register for the exam.

NVIDIA· プロフェッショナル

NVIDIA-Certified Professional: Agentic AI

NCP-AAI

The NCP-AAI exam tests whether you can design, build, evaluate and operate multi-agent systems on the NVIDIA stack, including memory, deployment, safety and oversight. On preporato.com you prepare with timed practice exams explained question by question, a lesson-by-lesson course, flashcards and hands-on labs.

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含まれる内容

模擬試験

本番形式の模擬試験7セット

526問以上

質問 1/150/15 回答済み

Your multi-agent platform runs in Docker containers. It must expose one external HTTPS endpoint, spread requests evenly across agent replicas and add replicas automatically as traffic grows. Which combination of tools and configuration best satisfies these requirements?

Kubernetes with ClusterIP services and a Horizontal Pod Autoscaler that scales on CPU usage.

Kubernetes with an Ingress, a LoadBalancer service in front and a Horizontal Pod Autoscaler.

Docker Compose with published host ports and a fixed number of replicas defined in the compose file.

Kubernetes with NodePort services and a fixed replica count sized for the expected peak load.

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NVIDIA-Certified Professional: Agentic AI

含まれる内容

本番形式の模擬試験7セット

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間隔反復デッキ

ラボ· 31

Build Two Agents That Talk via the A2A Protocol

Add Long-Term Memory to an AI Agent: LangGraph + Milvus

Build an AI Agent 3 Ways: ReAct vs Tool Calling vs Plan-and-Execute

Build an MCP Tool Server & Connect a LangChain Agent

Build a Multi-Agent Supervisor with LangGraph

Build a RAG Pipeline with NVIDIA NIM

Build a ReAct Agent with NVIDIA NIM

Build NeMo Guardrails for an AI Agent: Jailbreak & Topical Rails

Evaluate an Agent with LLM-as-Judge

Model Routing & Cost Cascade with NIM

Structured Output & Function Calling with NIM

Visual Q&A with NVIDIA VLMs

Multimodal RAG with NeMo Retriever

LLM Observability: Trace, Cost and Debug a RAG Assistant

Build an Agent From Scratch: The Tool Loop Without a Framework

Evaluate an AI Agent: Trajectories, Tool Calls and an LLM Judge

Evaluate a RAG Pipeline: Retrieval Metrics, Faithfulness and a Regression Gate

RAG Chunking Shoot-Out: Measure Which Chunking Strategy Retrieves

Hybrid Search and Reranking: BM25, Embeddings, RRF and an LLM Reranker

Query Rewriting and HyDE: Better Queries for the Search You Have

Document AI: Turn PDFs, Tables and Scans into Clean RAG Chunks

Context Engineering: Token Budgets, Compaction and Running Notes for Long Chats

Web Research Agent: Search, Read and Cite Without Being Fooled by the Web

Code-Executing Data Agent: Let an LLM Answer Questions With pandas, Safely

Prompt Regression Testing in CI: A Golden Set That Blocks Bad Prompt Changes

Vector Index Internals: Exact Search, IVF, HNSW, Product Quantization and Filtered Search with FAISS

Long Context vs RAG: Accuracy, Cost and Latency on the Same 24 Questions

Build an MCP Client and Host: Handshake, Tool Discovery, Error Handling and Approvals

Human-in-the-Loop Agent: Approval Interrupts, Durable Pauses, Crash-Safe Tools and Escalation

Planner-Executor Agents with Self-Critique: Plans, Validators, Replanning and Stopping Rules

Optimise Prompts with DSPy: Signatures, Metrics, Few-Shot Compilers and Held-Out Tests

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