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GCP Professional Data Engineer Practice Question

You are planning the migration of a 40-TB Oracle 11g on-premises transactional database to Cloud SQL for PostgreSQL. The application is revenue-critical and may experience no more than 20 minutes of downtime at cut-over. Business owners also mandate a 15-minute recovery-point objective (RPO) and a 2-hour recovery-time objective (RTO). A Dedicated Interconnect circuit to Google Cloud is already in place. After migration, the data engineering team must execute scripted row-count and checksum queries to confirm that the target contains an identical data set before users are redirected.

Which end-to-end approach best meets all stated constraints while following Google-recommended phases for infrastructure preparation, data transfer, validation, and final cut-over?

    1. Provision a regional Cloud Spanner instance; 2) Use BigQuery Data Transfer Service to load Oracle export files into BigQuery and then into Spanner; 3) Validate with Dataform assertions; 4) Reconfigure the application to use Spanner.
    1. Create a highly-available Cloud SQL for PostgreSQL instance; 2) Use Database Migration Service in continuous-migration mode to perform the initial load from Oracle and begin change-data-capture replication over Dedicated Interconnect; 3) Monitor replication lag until it is under 15 minutes, then stop application writes; 4) Run the scripted row-count and checksum queries on both databases, verify parity, and execute DMS cut-over to promote the Cloud SQL instance; 5) Update application connection strings and decommission the DMS job.
    1. Order a Transfer Appliance, copy the Oracle data files to it, and ship it to Google; 2) Restore the files into a single-node Cloud SQL for PostgreSQL instance; 3) Run validation queries; 4) Switch traffic after shutting down the on-prem database.
    1. Perform an Oracle Data Pump export while the application remains online; 2) Transfer the dump files to Cloud Storage via gsutil over Interconnect; 3) Import the data into Cloud SQL for PostgreSQL using pgloader; 4) Run validation queries and point applications to Cloud SQL.
GCP Professional Data Engineer
Designing data processing systems
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