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Question No 1

An AI practitioner trained a custom model on Amazon Bedrock by using a training dataset that contains confidential dat a. The AI practitioner wants to ensure that the custom model does not generate inference responses based on confidential data. How should the AI practitioner prevent responses based on confidential data?

Choose the Choices:

Delete the custom model. Remove the confidential data from the training dataset. Retrain the custom model.

Mask the confidential data in the inference responses by using dynamic data masking.

Encrypt the confidential data in the inference responses by using Amazon SageMaker.

Encrypt the confidential data in the custom model by using AWS Key Management Service (AWS KMS).

Question No 2

Which feature of Amazon OpenSearch Service gives companies the ability to build vector database applications?

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Integration with Amazon S3 for object storage

Support for geospatial indexing and queries

Scalable index management and nearest neighbor search capability

Ability to perform real - time analysis on streaming data

Question No 3

A company wants to display the total sales for its top - selling products across various retail locations in the past 12 months. Which AWS solution should the company use to automate the generation of graphs?

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Question No 4

A company wants to build an interactive application for children that generates new stories based on classic stories. The company wants to use Amazon Bedrock and needs to ensure that the results and topics are appropriate for children. Which AWS service or feature will meet these requirements?

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Question No 5

A company has developed an ML model for image classification. The company wants to deploy the model to production so that a web application can use the model. The company needs to implement a solution to host the model and serve predictions without managing any of the underlying infrastructure. Which solution will meet these requirements?

Choose the Choices:

Use Amazon SageMaker Serverless Inference to deploy the model.

Use Amazon CloudFront to deploy the model.

Use Amazon API Gateway to host the model and serve predictions.

Use AWS Batch to host the model and serve predictions.

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