Nca - AI Infrastructure and Operations

NCA-AIIO

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Question Types
Multiple choiceFill in the blankDiagramsCase studies

What's Included

201
Practice Questions
1
Exam Versions
1
Languages
Translation Beta
Jan 1, 2024
Release Date
Aug 11, 2025Last Updated

Complete Exam Package

201 NCA-AIIO practice questions with detailed explanations

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Study Mode, Timed Practice, and Flashcard Review

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Exam Details

Duration60 min
Passing Score70%
Languages English
LevelAssociate
TestingOnline Proctored
Valid For2 years
Release DateJan 1, 2024
Exam Cost$125

What topics are on the NCA-AIIO exam?

1

Essential AI Knowledge 33%

1.1
AI Fundamentals
2 subtopics
1.1.1AI, Machine Learning, and Deep Learning Concepts
1.1.2AI Use Cases and Industry Applications
Learning Objectives
  • Explain AI terminology and fundamental concepts
  • Identify major AI use cases across various industries
  • Understand the factors driving AI adoption
  • Differentiate between AI, machine learning, and deep learning
1.2
GPU Computing Architecture
2 subtopics
1.2.1GPU vs CPU Architecture
1.2.2NVIDIA GPU Generations
Learning Objectives
  • Describe how GPU computing contributes to AI advancement
  • Compare and contrast GPU and CPU architectures
  • Understand NVIDIA GPU technology evolution
1.3
NVIDIA Software Stack
2 subtopics
1.3.1AI Development Software Components
1.3.2Frameworks and Libraries
Learning Objectives
  • Understand the NVIDIA software stack components
  • Describe the purpose of various NVIDIA software solutions
  • Identify software components in the AI development lifecycle
Domain Hands-on Skills
Identifying appropriate AI use casesSelecting GPU vs CPU for workloadsUnderstanding NVIDIA software ecosystem
Common Mistakes to Avoid
  • Confusing AI, ML, and DL definitions
  • Not understanding training vs inference requirements
  • Overlooking software stack importance
2

AI Infrastructure 34%

3

AI Operations 33%

How do I earn the Nca - AI Infrastructure and Operations certification?

Official Pathway Guidance

Track: AI Infrastructure and Operations

Prerequisites

  • NoneNo formal prerequisitesOptional

Next Steps

Current Level

Alternative Paths

Career Progression

Entry Roles

  • Junior AI Infrastructure Engineer
  • GPU Systems Administrator
  • AI Operations Specialist
  • Data Center Technician

Mid Level Roles

  • AI Infrastructure Engineer
  • MLOps Engineer
  • GPU Cluster Administrator
  • AI Platform Engineer

Senior Roles

  • Senior AI Infrastructure Architect
  • Principal MLOps Engineer
  • AI Infrastructure Team Lead
  • Data Center Architect (AI-focused)

Complementary Certifications

  • CKA (Certified Kubernetes Administrator)
  • LFCS (Linux Foundation Certified System Administrator)
  • AWS/Azure/GCP ML Specialty
  • Docker Certified Associate

Certification Maintenance

  • Recertification Options:
    Retake the current version of the examPass any Professional-level NVIDIA certificationComplete specified continuing education credits

Recommended Learning Pathway

How do I study for the NCA-AIIO Exam?

Practice the Nca - AI Infrastructure and Operations with our Exam Simulator

Free practice questions, exam guides, and real exam‑style explanations in our Planet Cert simulator.

Free Resources

https://docs.nvidia.com/documentationhttps://catalog.ngc.nvidia.com/container_registryhttps://developer.nvidia.com/blog/bloghttps://www.nvidia.com/en-us/on-demand/video_library

Community Resources

https://forums.developer.nvidia.com/forumhttps://www.reddit.com/r/nvidia/communityhttps://blogs.nvidia.com/ai-podcast/podcast

Hands-on Labs

https://www.nvidia.com/en-us/launchpad/NVIDIAhttps://colab.research.google.com/Google

Study Tips

  • Focus on understanding GPU vs CPU architecture differences
  • Learn the NVIDIA software stack components thoroughly
  • Understand different DGX system configurations
  • Practice identifying appropriate infrastructure for different AI workloads
  • Study MLOps principles and tools like Kubeflow and MLflow
  • Review data center concepts including cooling and power management
  • Understand container orchestration with Kubernetes
  • Learn about GPU monitoring metrics and tools

What's changed on this exam?

Status: ACTIVE

Upcoming Events

March 2025
GTC 2025
Major announcements expected for Blackwell availability
Various dates 2025
NVIDIA AI Summit
Regional events with certification information

Technology Coverage

NVIDIA H200 Tensor Core GPUGA

Core topic for current infrastructure questions

Released: 2024-Q2
NVIDIA AI Enterprise5.0

Updated software stack questions

Released: 2024-Q3
NVIDIA GPU Operator23.9.0

Kubernetes GPU management topics

Released: 2024-Q1
Multi-Instance GPU (MIG)3.0

Resource partitioning and multi-tenancy questions

Released: 2023-Q4

Industry Trends

Who should take this exam?

Recommended Experience

  • Basic understanding of data center infrastructure
  • Familiarity with Linux operating systems
  • General IT knowledge
  • Understanding of networking concepts

Experience Level: Entry-level

How do I register & what's the exam fee?

Exam Cost$125 USD
Testing CentersOnline Proctored
Online ProctoringAvailable

How long is the certification valid?

Valid For2 years
Recertification
  • Retake the current version of the exam
  • Pass a higher-level NVIDIA certification

What jobs can I get with this?

Career Benefits

  • Validates foundational AI infrastructure knowledge
  • Entry point to NVIDIA certification pathway
  • Demonstrates GPU computing competency
  • Enhances credibility in AI operations roles

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