DEA-C01 · Question #192
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…
The correct answer is A. Use Amazon Redshift ML to generate inventory recommendations. B. Use SQL to invoke a remote SageMaker endpoint for prediction. Amazon Redshift ML integrates machine learning (ML) directly into the Redshift environment, allowing you to build and use ML models with SQL commands. By leveraging Redshift ML, the company can make real-time inventory recommendations based on historical and current data…
Question
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.)
Options
- AUse Amazon Redshift ML to generate inventory recommendations.
- BUse SQL to invoke a remote SageMaker endpoint for prediction.
- CUse Amazon Redshift ML to schedule regular data exports for offline model training.
- DUse SageMaker Autopilot to create inventory management dashboards in Amazon Redshift.
- EUse Amazon Redshift as a file storage system to archive old inventory management reports.
How the community answered
(55 responses)- A75% (41)
- C13% (7)
- D7% (4)
- E5% (3)
Explanation
Amazon Redshift ML integrates machine learning (ML) directly into the Redshift environment, allowing you to build and use ML models with SQL commands. By leveraging Redshift ML, the company can make real-time inventory recommendations based on historical and current data directly within Redshift. Redshift can invoke external services, such as a SageMaker real-time endpoint, using SQL queries. This allows the company to send real-time data from Redshift to SageMaker and receive predictions (e.g., inventory forecasting) in real time, meeting the need for real-time predictions.
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