AdView Inc. wants to build a single SageMaker Data Wrangler flow that joins an S3 CSV of campaign logs with a Redshift table of customer profiles and then exports the cleaned, joined dataset to Parquet. The Redshift cluster is in a private subnet. What is the correct sequence of actions to implement this in Data Wrangler while ensuring connectivity and correctness?

The correct option outlines the necessary steps for Data Wrangler to access a private Redshift cluster and perform the join. Data Wrangler can directly connect to Redshift using JDBC credentials. For Data Wrangler to access resources in a private subnet, it must be configured to run within a VPC that has network connectivity (subnets, security groups) to the Redshift cluster. The associated IAM role needs permissions for both S3 and Redshift. The incorrect options propose unfeasible or less direct methods. Data Wrangler does not automatically create a Redshift database proxy. Exporting both datasets to S3 and using Glue ETL for the join bypasses Data Wrangler's capabilities for data preparation and transformation. Performing a federated query from Redshift to S3 and expecting Data Wrangler to push the join down without network configuration is incorrect; Data Wrangler needs direct access to both data sources for its join transformation.