Google ADP Value Pack (Frequently Bought Together)

Your company is building a near real-time streaming pipeline to process JSON telemetry data from small appliances. You need to process messages arriving at a Pub/Sub topic, capitalize letters in the serial number field, and write results to BigQuery. You want to use a managed service and write a minimal amount of code for underlying transformations. What should you do?

• A. Use a Pub/Sub to Cloud Storage subscription, write a Cloud Run service that is triggered when objects arrive in the bucket, performs the transformations, and writes the results to BigQuery.
• B. Use a Pub/Sub to BigQuery subscription, write results directly to BigQuery, and schedule a transformation query to run every five minutes.
• C. Use a Pub/Sub push subscription, write a Cloud Run service that accepts the messages, performs the transformations, and writes the results to BigQuery.
• D. Use the "Pub/Sub to BigQuery" Dataflow template with a UDF, and write the results to BigQuery.

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 need to design a data pipeline to process large volumes of raw server log data stored in Cloud Storage.
The data needs to be cleaned, transformed, and aggregated before being loaded into BigQuery for analysis.
The transformation involves complex data manipulation using Spark scripts that your team developed. You need to implement a solution that leverages your team's existing skillset, processes data at scale, and minimizes cost. What should you do?

• A. Use Cloud Data Fusion to visually design and manage the pipeline.
• B. Use Dataflow with a custom template for the transformation logic.
• C. Use Dataproc to run the transformations on a cluster.
• D. Use Dataform to define the transformations in SQLX.

Your team wants to create a monthly report to analyze inventory data that is updated daily. You need to aggregate the inventory counts by using only the most recent month of data, and save the results to be used in a Looker Studio dashboard. What should you do?

• A. Create a BigQuery table that uses the SUM() function and the _PARTITIONDATE filter.
• B. Create a BigQuery table that uses the SUM() function and the DATE_DIFF() function.
• C. Create a saved query in the BigQuery console that uses the SUM() function and the DATE_SUB() function. Re-run the saved query every month, and save the results to a BigQuery table.
• D. Create a materialized view in BigQuery that uses the SUM() function and the DATE_SUB() function.

Your organization is conducting analysis on regional sales metrics. Data from each regional sales team is stored as separate tables in BigQuery and updated monthly. You need to create a solution that identifies the top three regions with the highest monthly sales for the next three months. You want the solution to automatically provide up-to-date results. What should you do?

• A. Create a BigQuery materialized view that performs a union across all of the regional sales tables. Use the rank() window function to query the new materialized view.
• B. Create a BigQuery materialized view that performs a cross join across all of the regional sales tables. Use the row_number() window function to query the new materialized view.
• C. Create a BigQuery table that performs a union across all of the regional sales tables. Use the row_number() window function to query the new table.
• D. Create a BigQuery table that performs a cross join across all of the regional sales tables. Use the rank() window function to query the new table.