MLS-C01 · Question #234
A company wants to deliver digital car management services to its customers. The company plans to analyze data to predict the likelihood of users changing cars. The company has 10 TB of data that is s
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Question
A company wants to deliver digital car management services to its customers. The company plans to analyze data to predict the likelihood of users changing cars. The company has 10 TB of data that is stored in an Amazon Redshift cluster. The company's data engineering team is using Amazon SageMaker Studio for data analysis and model development. Only a subset of the data is relevant for developing the machine learning models. The data engineering team needs a secure and cost-effective way to export the data to a data repository in Amazon S3 for model development. Which solutions will meet these requirements? (Choose two.)
Options
- ALaunch multiple medium-sized instances in a distributed SageMaker Processing job. Use the
- BLaunch multiple medium-sized notebook instances with a PySpark kernel in distributed mode.
- CUse AWS Secrets Manager to store the Amazon Redshift credentials. From a SageMaker Studio
- DUse AWS Secrets Manager to store the Amazon Redshift credentials. Launch a SageMaker
- EUse SageMaker Data Wrangler to query and plot the relevant data and to export the relevant data
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