NVIDIA NCA-AIIO Q&A - in .pdf

  • NCA-AIIO pdf
  • Exam Code: NCA-AIIO
  • Exam Name: NVIDIA-Certified Associate AI Infrastructure and Operations
  • Updated: Oct 15, 2026
  • Q & A: 125 Questions and Answers
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  • Exam Code: NCA-AIIO
  • Exam Name: NVIDIA-Certified Associate AI Infrastructure and Operations
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  • Updated: Oct 15, 2026
  • Q & A: 125 Questions and Answers
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  • Exam Code: NCA-AIIO
  • Exam Name: NVIDIA-Certified Associate AI Infrastructure and Operations
  • Updated: Oct 15, 2026
  • Q & A: 125 Questions and Answers
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NVIDIA NCA-AIIO Exam Syllabus Topics:
TopicDetails
Topic 1
  • Essential AI knowledge: Exam Weight: This section of the exam measures the skills of IT professionals and covers foundational AI concepts. It includes understanding the NVIDIA software stack, differentiating between AI, machine learning, and deep learning, and comparing training versus inference. Key topics also involve explaining the factors behind AI's rapid adoption, identifying major AI use cases across industries, and describing the purpose of various NVIDIA solutions. The section requires knowledge of the software components in the AI development lifecycle and an ability to contrast GPU and CPU architectures.
Topic 2
  • AI Operations: This section of the exam measures the skills of data center operators and encompasses the management of AI environments. It requires describing essentials for AI data center management, monitoring, and cluster orchestration. Key topics include articulating measures for monitoring GPUs, understanding job scheduling, and identifying considerations for virtualizing accelerated infrastructure. The operational knowledge also covers tools for orchestration and the principles of MLOps.
Topic 3
  • AI Infrastructure: This section of the exam measures the skills of IT professionals and focuses on the physical and architectural components needed for AI. It involves understanding the process of extracting insights from large datasets through data mining and visualization. Candidates must be able to compare models using statistical metrics and identify data trends. The infrastructure knowledge extends to data center platforms, energy-efficient computing, networking for AI, and the role of technologies like NVIDIA DPUs in transforming data centers.

Reference: https://www.nvidia.com/en-us/learn/certification/ai-infrastructure-operations-associate/

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NVIDIA-Certified Associate AI Infrastructure and Operations Sample Questions:
Question #1

The foundation of the NVIDIA software stack is the DGX OS. Which of the following Linux distributions is DGX OS built upon?

  • A. Red Hat
  • B. CentOS
  • C. Ubuntu
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Correct Answer: C  🗳️

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Question #2

What is CUDA

  • A. NVIDIA's parallel programming toolkit.
  • B. NVIDIA's deep learning libraries.
  • C. NVIDIA's containerization SDK.
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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Question #3

In an AI cluster, what is the purpose of job scheduling?

  • A. To assign workloads to available compute resources.
  • B. To install, update, and configure cluster software.
  • C. To gather and analyze cluster data on a regular schedule.
  • D. To monitor and troubleshoot cluster performance.
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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Question #4

In gradient-based optimization, feature scaling is crucial because it:

  • A. Stabilizes gradient magnitude across dimensions
  • B. Removes outliers
  • C. Eliminates overfitting
  • D. Increases dataset size
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Correct Answer: A  🗳️

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Question #5

How many 1 Gb Ethernet in-band network connections are in a DGX H100 system?

  • A. 0
  • B. 1
  • C. 2
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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