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 a Pub/Sub topic. Create a Cloud Run function to subscribe to the Pub/Sub topic, apply transformations, and insert the data into BigQuery.
• B. Push event information to a Pub/Sub topic. Create a BigQuery subscription in Pub/Sub.
• C. 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.
• D. Push event information to a Pub/Sub topic. Create a Dataflow job using the Dataflow job builder.

You need to transfer approximately 300 TB of data from your company's on-premises data center to Cloud Storage. You have 100 Mbps internet bandwidth, and the transfer needs to be completed as quickly as possible. What should you do?

• A. Use the gcloud storage command to transfer the data over the internet.
• B. Compress the data, upload it to multiple cloud storage providers, and then transfer the data to CloudStorage.
• C. Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google.
• D. Use Cloud Client Libraries to transfer the data over the internet.

You have a BigQuery dataset containing sales dat
a. This data is actively queried for the first 6 months. After that, the data is not queried but needs to be retained for 3 years for compliance reasons. You need to implement a data management strategy that meets access and compliance requirements, while keeping cost and administrative overhead to a minimum. What should you do?

• A. Use BigQuery long-term storage for the entire dataset. Set up a Cloud Run function to delete the data from BigQuery after 3 years.
• B. Partition a BigQuery table by month. After 6 months, export the data to Coldline storage. Implement a lifecycle policy to delete the data from Cloud Storage after 3 years.
• C. Set up a scheduled query to export the data to Cloud Storage after 6 months. Write a stored procedure to delete the data from BigQuery after 3 years.
• D. Store all data in a single BigQuery table without partitioning or lifecycle policies.

You work for an ecommerce company that has a BigQuery dataset that contains customer purchase history, demographics, and website interactions. You need to build a machine learning (ML) model to predict which customers are most likely to make a purchase in the next month. You have limited engineering resources and need to minimize the ML expertise required for the solution. What should you do?

• A. Use BigQuery ML to create a logistic regression model for purchase prediction.
• B. Export the data to Cloud Storage, and use AutoML Tables to build a classification model for purchase prediction.
• C. Use Vertex Al Workbench to develop a custom model for purchase prediction.
• D. Use Colab Enterprise to develop a custom model for purchase prediction.

Your organization uses a BigQuery table that is partitioned by ingestion time. You need to remove data that is older than one year to reduce your organization's storage costs. You want to use the most efficient approach while minimizing cost. What should you do?

• A. Set the table partition expiration period to one year using the ALTER TABLE statement in SQL.
• B. Create a view that filters out rows that are older than one year.
• C. Require users to specify a partition filter using the alter table statement in SQL.
• D. Create a scheduled query that periodically runs an update statement in SQL that sets the "deleted" column to "yes" for data that is more than one year old. Create a view that filters out rows that have been marked deleted.