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NVIDIA Certified Associate - AI Infrastructure and Operations exam prep

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•3 min read
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Srilakshmi Sripathi
Welcome to my reMomentum Lab!!! Following a dedicated period of family care, I am currently on a return to work journey calling it reMomentum focused on mastering the modern data and AI landscape. Lab part- with an aim to become Data platform Engineer who understands HPC. Documenting my technical evolution and return to industry through deep dives into Data Engineering and Cloud Architecture. This log serves as a transparent, technical record of my pursuit of the Terraform → CKA → Nvidia Software Stack trifecta. My focus is on building resilient, cost-effective infrastructure and production-grade ML pipelines, bridging the gap between raw data engineering and operational excellence.
Part of seriesCertifications

Exam Blueprint

Signup for NCA-AIIO certification here.

Sign up for Nvidia Academy here for official high level overview.

Checkout Exam Guide for additional resource here.

Disclimer: I am not documenting everything that was provided in the course or exam guide. This blog is for my own milestone recognition and future reference. I cannot provide course instruction or support to replace what was being offered by Nvidia Academy.

Essential AI Knowledge: 38%

  • Describe the NVIDIA software stack used in an AI environment.

  • Compare and contrast training and inference architecture requirements and considerations.

  • Differentiate the concepts of AI, machine learning, and deep learning.

  • Explain the factors contributing to recent rapid improvements and adoption of AI.

  • Explain the key AI use cases and industries.

  • Explain the purpose and use case of various NVIDIA solutions.

  • Describe the software components related to the life cycle of AI development and deployment.

  • Compare and contrast GPU and CPU architectures.

Flowchart illustrating six AI units: 1. AI Transformation Across Industries.2. Introduction to AI.3. Generative AI.4. Accelerating AI with GPUs.5. AI Software Ecosystem.6. Data Center and Cloud Computing. Includes sections on AI use cases, ML/DL/Gen AI, generating new data, accelerating workloads, development tools, and data centers.

AI Infrastructure: 40%

  • Identify hardware requirements for specific AI training task use cases.

  • Scale a GPU infrastructure for different use cases.

  • Identify key concepts, and high-level specifications related to power and cooling requirements within a datacenter.

  • Articulate the key advantages, challenges, and considerations related to on-prem vs cloud infrastructures.

  • Identify key components and considerations of a cluster of an accelerated infrastructureIdentify facility requirements.

  • Determine networking requirements for AI workloads.

  • Identify and describe DC networking protocols and key concepts.

  • Identify high speed DC network options and their use cases.

  • Explain the purpose and benefits of a DPU in a datacenter.

Diagram featuring six units related to AI and computing: Unit 7 on AI Compute Platforms, Unit 8 on Networking for AI, Unit 9 on Storage for AI, Unit 10 on Energy Efficient Computing, Unit 11 on Reference Architectures, and Unit 12 on AI in the Cloud. Each unit has an associated icon.

AI Operations: 22%

  • Describe AI data center management and monitoring essentials.

  • Describe AI cluster orchestration and job scheduling essentials.

  • Articulate the key measures and criteria related to monitoring GPUs.

  • Identify the key considerations for virtualizing accelerated infrastructure.

Two labeled units in a grid: Unit 13 "AI Data Center Management and Monitoring" with a gear icon, and Unit 14 "AI Cluster Orchestration and Job Scheduling" with a clock icon. NVIDIA logo at the bottom right.
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Srilakshmi Sripathi's strategic initiative established Jan 2026; a pivot towards HPC, AI Infrastructure, Platform engineering in the Data 3.0 era. From Kubernetes to Agentic AI, an honest, production-focused log of building resilient systems and reaching peak technical velocity.