Amazon Web Services SAA-C03 Exam With Confidence Using Practice Dumps

The SELECT INTO OUTFILE S3 feature allows you to export Amazon Aurora MySQL data directly to Amazon S3 with minimal operational overhead. This method is efficient and cost-effective for archiving historical data.

You can configure S3 Lifecycle rules to transition the exported data to lower-cost storage (e.g., S3 Glacier or S3 Standard-IA) and eventually delete it after 5 years.

No need for additional ETL tools like Glue or DataBrew unless complex transformations are required.

A spread placement group is designed to deploy each instance on distinct underlying hardware, reducing the risk of simultaneous failures. By spreading instances across multiple Availability Zones, you achieve high availability and fault tolerance, meeting the 99.99% uptime requirement. Spread placement groups are ideal for critical applications that require maximum resilience to single hardware failures. Neither cluster nor partition strategies offer the same guarantee of separation combined with cross-AZ distribution.

Reference Extract from AWS Documentation / Study Guide:

"Spread placement groups are recommended for applications that have a small number of critical instances that should be kept separate from each other. Instances in a spread placement group are placed on distinct underlying hardware, and when deployed across multiple Availability Zones, provide high availability."

Source: AWS Certified Solutions Architect – Official Study Guide, Compute and High Availability section; Amazon EC2 Placement Groups Documentation.

AWS Lake Formation provides centralized data access control, including fine-grained (column-level) permissions for data stored in S3 and accessed through services like Amazon Athena.

Using a Lake Formation blueprint to ingest data from Aurora MySQL into the data lake keeps ingestion and governance integrated. When QuickSight uses Athena as the data source, Athena enforces Lake Formation’s column-level permissions automatically. This allows the marketing team to see only the authorized subset of columns without custom access-control logic.

Options A, B, and C rely on manually limiting columns at ingestion time or using IAM or S3 bucket policies, which do not provide true column-level authorization for SQL queries and require significantly more manual work and maintenance.