You created a customer support application that sends several forms of data to Google Cloud. Your application is sending:
1. Audio files from phone interactions with support agents that will be accessed during trainings.
2. CSV files of users' personally identifiable information (PII) that will be analyzed with SQL.
3. A large volume of small document files that will power other applications.
You need to select the appropriate tool for each data type given the required use case, while following Google- recommended practices. Which should you choose?

• A. Filestore Bigtable BigQuery
• B. Filestore Cloud SQL for PostgreSQL Datastore
• C. Cloud Storage CloudSQL for PostgreSQL Bigtable
• D. Cloud Storage BigQuery Firestore

Your company is migrating their batch transformation pipelines to Google Cloud. You need to choose a solution that supports programmatic transformations using only SQL. You also want the technology to support Git integration for version control of your pipelines. What should you do?

• A. Use Dataform workflows.
• B. Use Cloud Composer operators.
• C. Use Dataflow pipelines.
• D. Use Cloud Data Fusion pipelines.

You are migrating data from a legacy on-premises MySQL database to Google Cloud. The database contains various tables with different data types and sizes, including large tables with millions of rows and transactional dat a. You need to migrate this data while maintaining data integrity, and minimizing downtime and cost. What should you do?

• A. Use Database Migration Service to replicate the MySQL database to a Cloud SQL for MySQL instance.
• B. Export the MySQL database to CSV files, transfer the files to Cloud Storage by using Storage Transfer Service, and load the files into a Cloud SQL for MySQL instance.
• C. Use Cloud Data Fusion to migrate the MySQL database to MySQL on Compute Engine.
• D. Set up a Cloud Composer environment to orchestrate a custom data pipeline. Use a Python script to extract data from the MySQL database and load it to MySQL on Compute Engine.

You need to create a new data pipeline. You want a serverless solution that meets the following requirements:
* Data is streamed from Pub/Sub and is processed in real-time.
* Data is transformed before being stored.
* Data is stored in a location that will allow it to be analyzed with SQL using Looker.
Which Google Cloud services should you recommend for the pipeline?

• A. Dataproc Serverless Bigtable
• B. Dataflow BigQuery
• C. Cloud Composer Cloud SQL for MySQL
• D. BigQuery Analytics Hub

You manage a Cloud Storage bucket that stores temporary files created during data processing. These temporary files are only needed for seven days, after which they are no longer needed. To reduce storage costs and keep your bucket organized, you want to automatically delete these files once they are older than seven days. What should you do?

• A. Develop a batch process using Dataflow that runs weekly and deletes files based on their age.
• B. Set up a Cloud Scheduler job that invokes a weekly Cloud Run function to delete files older than seven days.
• C. Configure a Cloud Storage lifecycle rule that automatically deletes objects older than seven days.
• D. Create a Cloud Run function that runs daily and deletes files older than seven days.