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You manage a web application that stores data in a Cloud SQL database. You need to improve the read performance of the application by offloading read traffic from the primary database instance. You want to implement a solution that minimizes effort and cost. What should you do?

• A. Use Cloud CDN to cache frequently accessed data.
• B. Migrate the database to a larger Cloud SQL instance.
• C. Enable automatic backups, and create a read replica of the Cloud SQL instance.
• D. Store frequently accessed data in a Memorystore instance.

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

You are working with a small dataset in Cloud Storage that needs to be transformed and loaded into BigQuery for analysis. The transformation involves simple filtering and aggregation operations. You want to use the most efficient and cost-effective data manipulation approach. What should you do?

• A. Use Dataproc to create an Apache Hadoop cluster, perform the ETL process using Apache Spark, and load the results into BigQuery.
• B. Use BigQuery's SQL capabilities to load the data from Cloud Storage, transform it, and store the results in a new BigQuery table.
• C. Create a Cloud Data Fusion instance and visually design an ETL pipeline that reads data from Cloud Storage, transforms it using built-in transformations, and loads the results into BigQuery.
• D. Use Dataflow to perform the ETL process that reads the data from Cloud Storage, transforms it using Apache Beam, and writes the results to BigQuery.

You need to create a data pipeline that streams event information from applications in multiple Google Cloud regions into BigQuery for near real-time analysis. The data requires transformation before loading. You want to create the pipeline using a visual interface. What should you do?

• A. Push event information to Cloud Storage, and create an external table in BigQuery. Create a BigQuery scheduled job that executes once each day to apply transformations.
• B. Push event information to a Pub/Sub topic. Create a Dataflow job using the Dataflow job builder.
• C. Push event information to a Pub/Sub topic. Create a BigQuery subscription in Pub/Sub.
• D. Push event information to a Pub/Sub topic. Create a Cloud Run function to subscribe to the Pub/Sub topic, apply transformations, and insert the data into BigQuery.

Your company uses Looker as its primary business intelligence platform. You want to use LookML to visualize the profit margin for each of your company's products in your Looker Explores and dashboards. You need to implement a solution quickly and efficiently. What should you do?

• A. Create a derived table that pre-calculates the profit margin for each product, and include it in the Looker model.
• B. Create a new dimension that categorizes products based on their profit margin ranges (e.g., high, medium, low).
• C. Apply a filter to only show products with a positive profit margin.
• D. Define a new measure that calculates the profit margin by using the existing revenue and cost fields.