Professional-Cloud-Database-Engineer Exam Dumps : Google Cloud Certified - Professional Cloud Database Engineer

Create extracts from your on-premises databases periodically, and push these extracts to Cloud Storage.

Upload the changes into BigQuery, and merge them with existing tables.

Use Cloud Data Fusion and scheduled workflows to extract data from MySQL. Transform this data into the appropriate schema, and load this data into your BigQuery database.

Use Datastream to connect to your on-premises database and create a stream. Have Datastream write to Cloud Storage. Then use Dataflow to process the data into BigQuery.

Use Database Migration Service to replicate data to a Cloud SQL for MySQL instance. Create federated tables in BigQuery on top of the replicated instances to transform and load the data into your BigQuery database.

Deploy 2 Cloud SQL instances in the us-central1 region with HA enabled, and create read replicas in us-east1 and us-west1.

Deploy 2 Cloud SQL instances in the us-central1 region, and create read replicas in us-east1 and us-west1.

Deploy 4 Cloud SQL instances in the us-central1 region with HA enabled, and create read replicas in us-central1, us-east1, and us-west1.

Deploy 4 Cloud SQL instances in the us-central1 region, and create read replicas in us-central1, us-east1 and us-west1.

Create secondary indexes on the replica.

Create additional read replicas, and partition your analytics users to use different read replicas.

Disable replication on the read replica, and set the flag for parallel replication on the read replica. Re-enable replication and optimize performance by setting flags on the primary instance.

Disable replication on the primary instance, and set the flag for parallel replication on the primary instance. Re-enable replication and optimize performance by setting flags on the read replica.

Move your analytics workloads to BigQuery, and set up a streaming pipeline to move data and update BigQuery.