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.)

A. Launch multiple medium-sized instances in a distributed SageMaker Processing job. Use the prebuilt Docker images for Apache Spark to query and plot the relevant data and to export the relevant data from Amazon Redshift to Amazon S3.
B. Launch multiple medium-sized notebook instances with a PySpark kernel in distributed mode. Download the data from Amazon Redshift to the notebook cluster. Query and plot the relevant data. Export the relevant data from the notebook cluster to Amazon S3.
C. Use AWS Secrets Manager to store the Amazon Redshift credentials. From a SageMaker Studio notebook, use the stored credentials to connect to Amazon Redshift with a Python adapter. Use the Python client to query the relevant data and to export the relevant data from Amazon Redshift to Amazon S3.
D. Use AWS Secrets Manager to store the Amazon Redshift credentials. Launch a SageMaker extra-large notebook instance with block storage that is slightly larger than 10 TB. Use the stored credentials to connect to Amazon Redshift with a Python adapter. Download, query, and plot the relevant data. Export the relevant data from the local notebook drive to Amazon S3.
E. Use SageMaker Data Wrangler to query and plot the relevant data and to export the relevant data from Amazon Redshift to Amazon S3.