A research lab trains a deep neural network for protein folding prediction. The model requires massive parallel matrix computations that exceed the capacity of their current GPU cluster. They need to scale up to thousands of nodes with high-speed interconnect. Which approach best aligns with the convergence of AI and HPC?

Refactor the model to run on a single high-memory server to avoid distributed computing overhead

Migrate the training workload to a cloud-based HPC cluster with InfiniBand networking and GPU-accelerated nodes

Use a traditional analytics platform to run the training job as a batch query

Replace the neural network with a simpler statistical model that runs on a standard server

How do AI, HPC, and analytics work together in a unified ecosystem?

HPC is used only for data storage

Each technology operates independently with no interaction

AI replaces both HPC and analytics

HPC processes data, analytics derives insights, and AI uses those insights to make predictions and recommendations

What is the core goal of artificial intelligence (AI)?

To replace all human decision-making with automated rules

To execute complex calculations faster than any human

To simulate human intelligence in machines, enabling them to perform tasks that typically require human cognition

To store and retrieve large volumes of data efficiently

A genomics company uses AI to identify genetic markers for diseases. Each training run on their current servers takes a week. They need to reduce this to a day to iterate faster. What is the most appropriate HPC strategy to achieve this?

Buy a single server with the fastest CPU available to speed up each training run

Optimize the AI model to use less memory so it runs faster on the existing hardware

Use a data analytics platform to preprocess the data faster, hoping the training will speed up

Use a job scheduler to run multiple training jobs in parallel on a cluster of GPU nodes

What is the primary focus of analytics?

Designing user interfaces for data visualization

Writing code to automate business processes

Storing data in a secure and compliant manner

Extracting insights from data to inform decision-making

NetAppCertified AI Expert

Domain 1Objective 5

Describe Convergence of AI, High-Performance Computing, and Analytics CERTIFIED-AI-EXPERT Practice Questions (Page 5)

Part of the AI Overview domain, which accounts for 15% of the CERTIFIED-AI-EXPERT exam. NetApp does not publish an official question count, but from its 90-minute exam (~35–60 total, ~5–9 in this domain), expect 1–2 from this objective — we provide 31 practice questions to prepare you well beyond it. (estimate)

31questions here

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7concepts

15%of the exam

Questions 21–25

21application · medium
A research lab trains a deep neural network for protein folding prediction. The model requires massive parallel matrix computations that exceed the capacity of their current GPU cluster. They need to scale up to thousands of nodes with high-speed interconnect. Which approach best aligns with the convergence of AI and HPC?
Select an answer first
22foundation · easy
How do AI, HPC, and analytics work together in a unified ecosystem?
Select an answer first
23foundation · easy
What is the core goal of artificial intelligence (AI)?
Select an answer first
24application · medium
A genomics company uses AI to identify genetic markers for diseases. Each training run on their current servers takes a week. They need to reduce this to a day to iterate faster. What is the most appropriate HPC strategy to achieve this?
Select an answer first
25foundation · easy
What is the primary focus of analytics?
Select an answer first

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