A team runs a continuous, predictable AI inference service that requires 24/7 availability. Which compute pricing model is most cost-effective for this scenario?

Bursting to a higher instance type during peak hours

A company runs a nightly batch job that trains a recommendation model. The job is not time-sensitive and can be interrupted. The current on-demand GPU instances are expensive. The company wants to reduce costs without changing the job's code. What is the most effective strategy?

Use a larger instance type to finish faster.

Use reserved instances for the batch job.

Move the job to a CPU-only instance.

Use spot instances for the batch job.

A company is designing a new AI pipeline that ingests large amounts of sensor data, processes it in near-real-time, and stores the results for 90 days. The data is accessed frequently for the first week, then rarely. The pipeline runs continuously. Which approach optimizes cost without compromising the near-real-time processing requirement?

Use spot instances for processing and store data in archive storage from day one.

Use a high-memory compute instance for processing and store all data in hot storage.

Use a balanced compute instance for processing and store data in cool storage after one week.

Use a GPU instance for processing and enable deduplication on storage.

When evaluating the cost of AI components, which approach can achieve cost savings without compromising model performance?

Moving all data to a lower-cost storage tier regardless of access frequency

Selecting the appropriate instance type based on the workload's specific requirements

Reducing the number of GPUs used for training

Using a smaller instance type for all workloads to reduce compute cost

A company runs a web service that uses AI for image classification. The service has predictable traffic: high during the day and low at night. The current deployment uses a fixed number of GPU instances. The company wants to reduce costs while maintaining performance during peak hours. What should they do?

Implement autoscaling to adjust the number of instances based on traffic.

Purchase reserved instances for the peak capacity.

Switch to CPU-only instances to reduce cost.

Use spot instances for all GPU instances to reduce cost.

NetAppCertified AI Expert

Domain 5Objective 4

Describe Solutions to Optimize Cost CERTIFIED-AI-EXPERT Practice Questions (Page 2)

Part of the AI Common Challenges domain, which accounts for 22% of the CERTIFIED-AI-EXPERT exam. NetApp does not publish an official question count, but from its 90-minute exam (~35–60 total, ~8–13 in this domain), expect 1–2 from this objective — we provide 26 practice questions to prepare you well beyond it. (estimate)

26questions here

6free pages

5concepts

22%of the exam

Questions 6–10

6foundation · medium
A team runs a continuous, predictable AI inference service that requires 24/7 availability. Which compute pricing model is most cost-effective for this scenario?
Select an answer first
7application · medium
A company runs a nightly batch job that trains a recommendation model. The job is not time-sensitive and can be interrupted. The current on-demand GPU instances are expensive. The company wants to reduce costs without changing the job's code. What is the most effective strategy?
Select an answer first
8application · medium
A company is designing a new AI pipeline that ingests large amounts of sensor data, processes it in near-real-time, and stores the results for 90 days. The data is accessed frequently for the first week, then rarely. The pipeline runs continuously. Which approach optimizes cost without compromising the near-real-time processing requirement?
Select an answer first
9foundation · medium
When evaluating the cost of AI components, which approach can achieve cost savings without compromising model performance?
Select an answer first
10application · medium
A company runs a web service that uses AI for image classification. The service has predictable traffic: high during the day and low at night. The current deployment uses a fixed number of GPU instances. The company wants to reduce costs while maintaining performance during peak hours. What should they do?
Select an answer first

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Describe Solutions to Optimize Cost CERTIFIED-AI-EXPERT Practice Questions (Page 2)

Questions 6–10

A team runs a continuous, predictable AI inference service that requires 24/7 availability. Which compute pricing model is most cost-effective for this scenario?

A company runs a nightly batch job that trains a recommendation model. The job is not time-sensitive and can be interrupted. The current on-demand GPU instances are expensive. The company wants to reduce costs without changing the job's code. What is the most effective strategy?

When evaluating the cost of AI components, which approach can achieve cost savings without compromising model performance?

Keep studying — AI Common Challenges