AWS Certified Data Engineer – Associate (DEA-C01) — Question 178

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? (Choose two.)

Answer options

• A. Use Amazon Redshift ML to generate inventory recommendations.
• 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.
• 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.

Correct answer: A, B

Explanation

The correct answers A and B are appropriate because Amazon Redshift ML can be used to generate real-time inventory recommendations, and SQL can invoke a remote SageMaker endpoint for predictions. Options C, D, and E do not directly address the requirement for real-time recommendations and predictions, instead focusing on other functionalities that do not meet the immediate needs of the company.