The correct answer is: Create a data lake using AWS Lake Formation. Register the S3 bucket in Lake Formation, and create an AWS Glue connection to Amazon RDS. Use Lake Formations access control features to manage permissions across teams.

AWS Lake Formation provides centralized data cataloging and fine-grained, table-level, column-level, and row-level access control for data stored in S3 and other sources. By integrating Amazon RDS using an AWS Glue JDBC connection and registering the S3 data lake location, Lake Formation allows different teams to securely query a unified view of the data with minimal manual access management.

Why the other options are incorrect:

Migrating the purchase data to Amazon RDS centralizes the data but introduces high operational overhead, reduces scalability, and is not ideal for large-scale analytics workloads, especially for semi-structured or large-volume data.

Scheduling a Lambda function to copy data from RDS to S3 and using Athena enables ad hoc querying but lacks native fine-grained access control. S3 bucket policies are coarse-grained and difficult to manage securely at scale across multiple teams.

Creating an Amazon Redshift cluster and copying data provides powerful analytics but requires data replication and periodic ingestion pipelines. Redshift access controls are not as granular or centrally governed as those offered by Lake Formation, and this approach adds operational complexity.

Why this question matters: This question tests your understanding of modern data lake architectures and the role of Lake Formation in securing and governing access across diverse AWS data sources while minimizing manual overhead.

Tip: When a scenario mentions data stored in both S3 and RDS, requires fine-grained access controls, and wants low operational overhead, Lake Formation is usually the most secure and scalable answer.

Full AWS Practitioner Certification Question