NCA-AIIO · Associate · 60 min · 50 questions · $125

AI Infrastructure and Operations

Full course Full course: 104 original questions with NVIDIA-sourced explanations and a specific reason for every wrong answer, a learning guide for every official objective, 22 simulator labs, a glossary and a timed mock exam.

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Today's study plan

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Readiness by domain

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Mock exam

50 questions · 60 minutes · weighted like the real exam.

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Exam details and more tools

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Practice options: choose a domain, length, or drill only missed or bookmarked questions (104 questions).
Full dashboard: streak, mock history, missed and bookmarked lists, export or import your progress.
Learning guides: one short guide per official objective, every point backed by an NVIDIA quote.
Hands-on labs: 22 guided simulator labs, each mapped to official objectives and NVIDIA sources.
Glossary: 50 official terms in plain language.
Ask Aegis: the tutor button at the bottom right answers from the course content with citations.
Lab simulator: 22 guided fault-injection labs with a coaching terminal. Advanced mode there adds incident drills, the cluster fleet view and live-explain telemetry.
Xid error reference: every in-use NVIDIA Xid code with NVIDIA's recommended actions.
HGX hardware guide: a beginner's tour of an 8-GPU server.

Exam facts

Level: Associate
Duration: One hour
Questions: 50
Price: $125
Delivery: Online, remotely proctored
Language: English
Validity: Two years from issuance
Scoring: Pass/fail (no numeric score is reported)
Prerequisites: A basic understanding of data center infrastructure.

Exam facts verified against NVIDIA's NCA-AIIO page on 2026-10-08. Always confirm on nvidia.com before booking.

Official blueprint

Essential AI Knowledge — 38%

1.1 Describe the NVIDIA software stack used in an AI environment.
1.2 Compare and contrast training and inference architecture requirements and considerations.
1.3 Differentiate the concepts of AI, machine learning, and deep learning.
1.4 Explain the factors contributing to recent rapid improvements and adoption of AI.
1.5 Explain the key AI use cases and industries.
1.6 Explain the purpose and use case of various NVIDIA solutions.
1.7 Describe the software components related to the life cycle of AI development and deployment.
1.8 Compare and contrast GPU and CPU architectures.

AI Infrastructure — 40%

2.1 Identify hardware requirements for specific AI training task use cases
2.2 Scale a GPU infrastructure for different use cases
2.3 Identify key concepts, and high-level specifications related to power and cooling requirements within a datacenter
2.4 Articulate the key advantages, challenges, and considerations related to on-prem vs cloud infrastructures
2.5 Identify key components and considerations of a cluster of an accelerated infrastructure
2.6 Identify facility requirements
2.7 Determine networking requirements for AI workloads
2.8 Identify and describe DC networking protocols and key concepts
2.9 Identify high speed DC network options and their use cases
2.10 Explain the purpose and benefits of a DPU in a datacenter

AI Operations — 22%

3.1 Describe AI data center management and monitoring essentials.
3.2 Describe AI cluster orchestration and job scheduling essentials.
3.3 Articulate the key measures and criteria related to monitoring GPUs.
3.4 Identify the key considerations for virtualizing accelerated infrastructure.

Coverage of every official objective

Coverage of official objectives
Obj.	Official objective	Guide	Practice Qs	Labs	Glossary terms
1.1	Describe the NVIDIA software stack used in an AI environment.	Guide	6	CUDA Stack Verification, NGC Container Flow, NVIDIA AI Software Stack	4
1.2	Compare and contrast training and inference architecture requirements and considerations.	Guide	5	Distributed Training (DDP), Training vs Inference Serving	3
1.3	Differentiate the concepts of AI, machine learning, and deep learning.	Guide	6	AI, ML & DL Foundations	6
1.4	Explain the factors contributing to recent rapid improvements and adoption of AI.	Guide	3	AI, ML & DL Foundations	4
1.5	Explain the key AI use cases and industries.	Guide	4	AI, ML & DL Foundations	2
1.6	Explain the purpose and use case of various NVIDIA solutions.	Guide	6	NVIDIA AI Software Stack	2
1.7	Describe the software components related to the life cycle of AI development and deployment.	Guide	4	Training vs Inference Serving, NVIDIA AI Software Stack	2
1.8	Compare and contrast GPU and CPU architectures.	Guide	5	AI, ML & DL Foundations	4
2.1	Identify hardware requirements for specific AI training task use cases	Guide	3	AI Infrastructure Planning	0
2.2	Scale a GPU infrastructure for different use cases	Guide	4	AI Infrastructure Planning	3
2.3	Identify key concepts, and high-level specifications related to power and cooling requirements within a datacenter	Guide	4	AI Infrastructure Planning	3
2.4	Articulate the key advantages, challenges, and considerations related to on-prem vs cloud infrastructures	Guide	4	DPU Offload & Cloud vs On-Prem	2
2.5	Identify key components and considerations of a cluster of an accelerated infrastructure	Guide	4	NVLink Topology, XID Fault Drill, Storage Bottleneck, GPUDirect Storage	4
2.6	Identify facility requirements	Guide	4	AI Infrastructure Planning	2
2.7	Determine networking requirements for AI workloads	Guide	6	AllReduce Deep Dive, NCCL Fallback Drill	2
2.8	Identify and describe DC networking protocols and key concepts	Guide	5	InfiniBand Fabric, RoCEv2 + PFC/ECN	6
2.9	Identify high speed DC network options and their use cases	Guide	4	NVLink Topology, XID Fault Drill, InfiniBand Fabric	6
2.10	Explain the purpose and benefits of a DPU in a datacenter	Guide	4	DPU Offload & Cloud vs On-Prem	2
3.1	Describe AI data center management and monitoring essentials.	Guide	8	ECC Error Lifecycle, XID Fault Drill, DCGM Monitoring	3
3.2	Describe AI cluster orchestration and job scheduling essentials.	Guide	5	Slurm Scheduler, Kubernetes GPU Ops	1
3.3	Articulate the key measures and criteria related to monitoring GPUs.	Guide	6	ECC Error Lifecycle, DCGM Monitoring	3
3.4	Identify the key considerations for virtualizing accelerated infrastructure.	Guide	4	MIG Partitioning, GPU Virtualization	3

Every row is an official objective from NVIDIA's exam page. Every guide point and practice explanation cites a verbatim quote from an NVIDIA source; a CI check fails if any item lacks an objective tag or an NVIDIA source, or if question counts drift from the official domain weights.

Study roadmap

Baseline. Read the official exam page and study guide, then take a short practice set to see where you stand.
AI Infrastructure (40%). Work through its objectives, then drill its practice questions until readiness is above 80%.
Essential AI Knowledge (38%). Work through its objectives, then drill its practice questions until readiness is above 80%.
AI Operations (22%). Work through its objectives, then drill its practice questions until readiness is above 80%.
Exam rehearsal. Take a full timed mock exam, review every miss, repeat until you score comfortably above your target.