A workload has predictable, steady traffic and requires consistent performance. Which deployment strategy is most suitable?

A scheduled AWS Lambda function that runs every hour

Provisioned throughput in Amazon Bedrock

On-demand invocation with AWS Lambda

Batch inference with Amazon SageMaker

A company is building a text classification system that needs to categorize customer feedback into predefined categories. The system processes millions of short text messages daily and requires low latency. The team is considering using a large foundation model but is concerned about cost and latency. Which approach should they take?

Use a container-based deployment of the large model with GPU acceleration.

Use a smaller pre-trained model that is fine-tuned for the specific classification task.

Use model cascading with the large model for all requests to ensure accuracy.

Use the large foundation model with on-demand invocation to handle the high volume.

What is a primary benefit of using a smaller pre-trained model for a specific task?

Reduced resource usage and lower inference cost

Guaranteed support for all languages

Ability to handle any task without fine-tuning

When is it appropriate to use a smaller pre-trained model instead of a larger one?

When the model must handle multiple languages

When the workload has very low latency requirements

When the task requires complex reasoning and high accuracy

When the task is simple and the smaller model meets the required quality

AWSCertified Generative AI Developer - Professional

Domain 2Objective 2

Task 2.2: Implement Model Deployment Strategies. AIP-C01 Practice Questions (Page 2)

Part of the Content Domain 2: Implementation and Integration domain, which accounts for 26% of the AIP-C01 exam.

25questions here

5free pages

9concepts

26%of the exam

Questions 6–10

6foundation · easy
Which technique reduces the memory footprint of a large language model by storing weights with lower numerical precision?
Select an answer first
7foundation · easy
A workload has predictable, steady traffic and requires consistent performance. Which deployment strategy is most suitable?
Select an answer first
8application · medium
A company is building a text classification system that needs to categorize customer feedback into predefined categories. The system processes millions of short text messages daily and requires low latency. The team is considering using a large foundation model but is concerned about cost and latency. Which approach should they take?
Select an answer first
9foundation · easy
What is a primary benefit of using a smaller pre-trained model for a specific task?
Select an answer first
10foundation · easy
When is it appropriate to use a smaller pre-trained model instead of a larger one?
Select an answer first

Finished these 5 questions?

Review the revealed explanations, or continue through the curriculum.

Next 5 questions →Back to AIP-C01

Free Basic Practice is a study aid with revealable answers — not a scored exam. Examers.io is independent and not affiliated with or endorsed by AWS. “AIP-C01” is a trademark of its owner, used for identification only.

Task 2.2: Implement Model Deployment Strategies. AIP-C01 Practice Questions (Page 2)

Questions 6–10

Which technique reduces the memory footprint of a large language model by storing weights with lower numerical precision?

A workload has predictable, steady traffic and requires consistent performance. Which deployment strategy is most suitable?

What is a primary benefit of using a smaller pre-trained model for a specific task?

When is it appropriate to use a smaller pre-trained model instead of a larger one?

Keep studying — Content Domain 2: Implementation and Integration