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You are building an application that allows users to customize their website and mobile experiences. The application will capture user information and preferences. User profiles have a dynamic schema, and users can add or delete information from their profile. You need to ensure that user changes automatically trigger updates to your downstream BigQuery data warehouse. What should you do?

A. Store your data in Bigtable, and use the user identifier as the key. Use one column family to store user profile data, and use another column family to store user preferences.

B. Use Cloud SQL, and create different tables for user profile data and user preferences from your recommendations model. Use SQL to join the user profile data and preferences

C. Use Firestore in Native mode, and store user profile data as a document. Update the user profile with preferences specific to that user and use the user identifier to query.

D. Use Firestore in Datastore mode, and store user profile data as a document. Update the user profile with preferences specific to that user and use the user identifier to query.

During an internal audit, you realized that one of your Cloud SQL for MySQL instances does not have high availability (HA) enabled. You want to follow Google-recommended practices to enable HA on your existing instance. What should you do?

A. Create a new Cloud SQL for MySQL instance, enable HA, and use the export and import option to migrate your data.

B. Create a new Cloud SQL for MySQL instance, enable HA, and use Cloud Data Fusion to migrate your data.

C. Use the gcloud instances patch command to update your existing Cloud SQL for MySQL instance.

D. Shut down your existing Cloud SQL for MySQL instance, and enable HA.

Your organization has hundreds of Cloud SQL for MySQL instances. You want to follow Google-recommended practices to optimize platform costs. What should you do?

A. Use Query Insights to identify idle instances.

B. Remove inactive user accounts.

C. Run the Recommender API to identify overprovisioned instances.

D. Build indexes on heavily accessed tables.

Your company is migrating the existing infrastructure for a highly transactional application to Google Cloud. You have several databases in a MySQL database instance and need to decide how to transfer the data to Cloud SQL. You need to minimize the downtime for the migration of your 500 GB instance. What should you do?

A. Create a Cloud SQL for MySQL instance for your databases, and configure Datastream to stream your database changes to Cloud SQL. Select the Backfill historical data check box on your stream configuration to initiate Datastream to backfill any data that is out of sync between the source and destination. Delete your stream when all changes are moved to Cloud SQL for MySQL, and update your application to use the new instance.

B. Create migration job using Database Migration Service. Set the migration job type to Continuous, and allow the databases to complete the full dump phase and start sending data in change data capture (CDC) mode. Wait for the replication delay to minimize, initiate a promotion of the new Cloud SQL instance, and wait for the migration job to complete. Update your application connections to the new instance.

C. Create migration job using Database Migration Service. Set the migration job type to One-time, and perform this migration during a maintenance window. Stop all write workloads to the source database and initiate the dump. Wait for the dump to be loaded into the Cloud SQL destination database and the destination database to be promoted to the primary database. Update your application connections to the new instance.

D. Use the mysqldump utility to manually initiate a backup of MySQL during the application maintenance window. Move the files to Cloud Storage, and import each database into your Cloud SQL instance. Continue to dump each database until all the databases are migrated. Update your application connections to the new instance.

You need to migrate existing databases from Microsoft SQL Server 2016 Standard Edition on a single Windows Server 2019 Datacenter Edition to a single Cloud SQL for SQL Server instance. During the discovery phase of your project, you notice that your on-premises server peaks at around 25,000 read IOPS. You need to ensure that your Cloud SQL instance is sized appropriately to maximize read performance. What should you do?

A. Create a SQL Server 2019 Standard on Standard machine type with 4 vCPUs, 15 GB of RAM, and 800 GB of solid-state drive (SSD).

B. Create a SQL Server 2019 Standard on High Memory machine type with at least 16 vCPUs, 104 GB of RAM, and 200 GB of SSD.

C. Create a SQL Server 2019 Standard on High Memory machine type with 16 vCPUs, 104 GB of RAM, and 4 TB of SSD.

D. Create a SQL Server 2019 Enterprise on High Memory machine type with 16 vCPUs, 104 GB of RAM, and 500 GB of SSD.