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 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.
• B. Create a BigQuery table that uses the SUM() function and the DATE_DIFF() function.
• C. Create a materialized view in BigQuery that uses the SUM() function and the DATE_SUB() function.
• D. Create a BigQuery table that uses the SUM() function and the _PARTITIONDATE filter.

Your organization uses scheduled queries to perform transformations on data stored in BigQuery. You discover that one of your scheduled queries has failed. You need to troubleshoot the issue as quickly as possible. What should you do?

• A. Navigate to the Logs Explorer page in Cloud Logging. Use filters to find the failed job, and analyze the error details.
• B. Navigate to the Scheduled queries page in the Google Cloud console. Select the failed job, and analyze the error details.
• C. Request access from your admin to the BigQuery information_schema. Query the jobs view with the failed job ID, and analyze error details.
• D. Set up a log sink using the gcloud CLI to export BigQuery audit logs to BigQuery. Query those logs to identify the error associated with the failed job I

Your company wants to implement a data transformation (ETL) pipeline for their BigQuery data warehouse.
You need to identify a managed transformation solution that allows users to develop with SQL and JavaScript, has version control, allows for modular code, and has data quality checks. What should you do?

• A. Use Dataform to define the transformations in SQLX.
• B. Use Dataproc to create an Apache Spark cluster and implement the transformations by using PySpark SQL.
• C. Create BigQuery scheduled queries to define the transformations in SQL.
• D. Create a Cloud Composer environment, and orchestrate the transformations by using the BigQueryinsertJob operator.

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 Dataflow job using the Dataflow job builder.
• B. 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.
• C. 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.
• D. Push event information to a Pub/Sub topic. Create a BigQuery subscription in Pub/Sub.

Following a recent company acquisition, you inherited an on- premises data infrastructure that needs to move to Google Cloud. The acquired system has 250 Apache Airflow directed acyclic graphs (DAGs) orchestrating data pipelines. You need to migrate the pipelines to a Google Cloud managed service with minimal effort. What should you do?

• A. Create a new Cloud Composer environment and copy DAGS to the Cloud Composer dags/folder.
• B. Convert each DAG to a Cloud Workflow and automate the execution with Cloud Scheduler.
• C. Create a Google Kubernetes Engine (GKE) standard cluster and deploy Airflow as a workload. Migrate all DAGs to the new Airflow environment.
• D. Create a Cloud Data Fusion instance. For each DAG, create a Cloud Data Fusion pipeline.