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Question 109 - DEA-C01 discussion

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A retail company is using an Amazon Redshift cluster to support real-time inventory management. The company has deployed an ML model on a real-time endpoint in Amazon SageMaker.

The company wants to make real-time inventory recommendations. The company also wants to make predictions about future inventory needs.

Which solutions will meet these requirements? (Select TWO.)

A.

Use Amazon Redshift ML to generate inventory recommendations.

Answers
A.

Use Amazon Redshift ML to generate inventory recommendations.

B.

Use SQL to invoke a remote SageMaker endpoint for prediction.

Answers
B.

Use SQL to invoke a remote SageMaker endpoint for prediction.

C.

Use Amazon Redshift ML to schedule regular data exports for offline model training.

Answers
C.

Use Amazon Redshift ML to schedule regular data exports for offline model training.

D.

Use SageMaker Autopilot to create inventory management dashboards in Amazon Redshift.

Answers
D.

Use SageMaker Autopilot to create inventory management dashboards in Amazon Redshift.

E.

Use Amazon Redshift as a file storage system to archive old inventory management reports.

Answers
E.

Use Amazon Redshift as a file storage system to archive old inventory management reports.

Suggested answer: A, B

Explanation:

The company needs to use machine learning models for real-time inventory recommendations and future inventory predictions while leveraging both Amazon Redshift and Amazon SageMaker.

Option A: Use Amazon Redshift ML to generate inventory recommendations. Amazon Redshift ML allows you to build, train, and deploy machine learning models directly from Redshift using SQL statements. It integrates with SageMaker to train models and run inference. This feature is useful for generating inventory recommendations directly from the data stored in Redshift.

Option B: Use SQL to invoke a remote SageMaker endpoint for prediction. You can use SQL in Redshift to call a SageMaker endpoint for real-time inference. By invoking a SageMaker endpoint from within Redshift, the company can get real-time predictions on inventory, allowing for integration between the data warehouse and the machine learning model hosted in SageMaker.

Option C (offline model training) and Option D (creating dashboards with SageMaker Autopilot) are not relevant to the real-time prediction and recommendation requirements.

Option E (archiving inventory reports in Redshift) is not related to making predictions or recommendations.

Amazon Redshift ML Documentation

Invoking SageMaker Endpoints from SQL

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asked 29/10/2024
Moinuddin Mohammed
44 questions
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