Oracle to Aurora PostgreSQL Migration with DMS and SCT

• A.Use AWS Snowball Edge to transfer the database files and then use SCT to convert the schema.
• ✓ B.Use AWS Schema Conversion Tool (SCT) to convert the schema and AWS Database Migration Service (DMS) to migrate the data.
• C.Use AWS Database Migration Service (DMS) with the Oracle as source and Aurora PostgreSQL as target.
• D.Use AWS Schema Conversion Tool (SCT) to convert the schema and then manually export/import data.

Why B: AWS SCT converts Oracle-specific schema objects (including hierarchical queries and stored procedures) to PostgreSQL-compatible code, while AWS DMS handles the ongoing data migration with minimal downtime. This combination is the standard AWS approach for heterogeneous database migrations, as SCT addresses schema conversion and DMS handles data transfer.

• ✓ A.AWS Database Migration Service (DMS)
• B.AWS Schema Conversion Tool (SCT)
• C.AWS Data Pipeline
• D.AWS Snowball

Why A: AWS DMS can perform a live migration from Oracle to Aurora PostgreSQL with minimal downtime by continuously replicating ongoing changes from the source database to the target using Change Data Capture (CDC). For a 2 TB database, DMS supports full-load plus CDC, allowing the source to remain operational during the initial load and then switching over with only a brief outage to apply final changes.

• A.Use AWS DMS with full load only, then cut over during a maintenance window.
• ✓ B.Use AWS DMS with ongoing replication (change data capture) from Oracle to Aurora.
• C.Export the database to Amazon S3 using Oracle Data Pump, then import into Aurora.
• D.Use pg_dump to export the database and pg_restore to import into Aurora.

Why B: AWS DMS with ongoing replication (change data capture) is the correct strategy because it allows continuous synchronization of changes from the source Oracle database to the target Aurora PostgreSQL, minimizing downtime to just the final cutover window. This approach ensures data consistency by applying transactional changes in near real-time, which is essential for a 2 TB database where a full load alone would require a lengthy maintenance window and risk data divergence.

• A.Increase the target Aurora storage to 2 TB to avoid storage issues
• ✓ B.Enable change data capture (CDC) on the source Oracle database
• ✓ C.Enable data validation on the DMS task
• D.Disable foreign key constraints on the target during migration
• E.Use AWS Schema Conversion Tool to convert the schema

Why B: Option B is correct because enabling Change Data Capture (CDC) on the source Oracle database allows AWS DMS to capture ongoing changes after the full load, enabling a near-zero downtime migration by continuously replicating transactions to the target Aurora PostgreSQL. Option C is correct because enabling data validation on the DMS task ensures that the data migrated is consistent between the source and target, verifying that no data loss or corruption occurred during the migration process.

• A.Use AWS Snowball to transfer the database files.
• B.Set up AWS Direct Connect between on-premises and AWS.
• ✓ C.Use AWS DMS to perform a full load and ongoing replication from Oracle to Aurora.
• ✓ D.Use AWS SCT to convert the Oracle schema to PostgreSQL-compatible schema.
• ✓ E.Validate the migrated data and test the application against the new database.

Why C: AWS DMS supports heterogeneous migrations from Oracle to Aurora PostgreSQL, performing a full load followed by ongoing change data capture (CDC) using Oracle's redo logs to replicate changes with minimal downtime. This makes it the correct service for both the initial data transfer and continuous replication during migration.