Question 1

Which AWS service or feature can help an AI development team quickly deploy and consume a foundation model (FM) within the team's VPC?

Options :

A : Amazon Personalize

B : Amazon SageMaker JumpStart

C : PartyRock, an Amazon Bedrock Playground

D : Amazon SageMaker endpoints

Answer: B

Question 2

A financial services company is developing several machine learning models to handle fraud detection, credit scoring, and customer segmentation. To ensure consistent use of data and foster collaboration among its data scientists, the company needs an efficient way to share and manage the variables used in these models across different teams and projects. The goal is to create a centralized repository where these variables can be accessed and reused to maintain uniformity, reduce redundancy, and improve productivity.Which of the following AWS tools would be the most suitable for achieving this objective?

Options :

A : SageMaker Feature Store

B : SageMaker Data Wrangler

C : SageMaker Clarify

D : SageMaker Model Monitor

Answer: A

Question 3

A company is using an Amazon Bedrock base model to summarize documents for an internal use case. The company trained a custom model to improve the summarization quality. Which action must the company take to use the custom model through Amazon Bedrock?

Options :

A : Purchase Provisioned Throughput for the custom model.

B : Deploy the custom model in an Amazon SageMaker endpoint for real-time inference.

C : Register the model with the Amazon SageMaker Model Registry.

D : Grant access to the custom model in Amazon Bedrock.

Answer: B

Question 4

A company has deployed an ML model. The company wants to provide external customers withsecure access to the model through the customers' own applications.Which solution will meet these requirements?

Options :

A : Use a custom script in the customers' application for authentication.

B : Store model credentials and share them with the customers directly for authentication.

C : Create a secure API endpoint that customers can use.

D : Embed the model directly into the customers' applications.

Answer: C

Question 5

A company wants to build an ML model by using Amazon SageMaker. The company needs to share and manage variables for model development across multiple teams. Which SageMaker feature meets these requirements?

Options :

A : Amazon SageMaker Feature Store

B : Amazon SageMaker Data Wrangler

C : Amazon SageMaker Clarify

D : Amazon SageMaker Model Cards

Answer: A

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