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…
The correct answer is C. Use AWS Secrets Manager to store the Amazon Redshift credentials. From a SageMaker Studio E. Use SageMaker Data Wrangler to query and plot the relevant data and to export the relevant data. Option C: Using AWS Secrets Manager to store Redshift credentials is the secure best practice for credential management. From a SageMaker Studio notebook, you can use the AWS Data Wrangler library (or boto3/psycopg2) to query only the relevant subset of data from Redshift and…
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
How the community answered
(40 responses)- A10% (4)
- B3% (1)
- C68% (27)
- D20% (8)
Explanation
Option C: Using AWS Secrets Manager to store Redshift credentials is the secure best practice for credential management. From a SageMaker Studio notebook, you can use the AWS Data Wrangler library (or boto3/psycopg2) to query only the relevant subset of data from Redshift and write it directly to S3 - this is lightweight, runs within the existing notebook, and incurs no additional compute cost. Option E: SageMaker Data Wrangler has native Redshift connectivity; it can query, filter, visualize, and export only the relevant data to S3 with a point-and-click interface, minimizing engineering effort. Options A and B involve launching distributed compute clusters (SageMaker Processing jobs or EMR), which is operationally heavier and cost-inefficient for a simple export task. Option D describes a less direct approach requiring additional infrastructure.
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