You work for a healthcare company. You have a daily ETL pipeline that extracts patient data from a legacy system, transforms it, and loads it into BigQuery for analysis. The pipeline currently runs manually using a shell script. You want to automate this process and add monitoring to ensure pipeline observability and troubleshooting insights. You want one centralized solution, using open-source tooling, without rewriting the ETL code. What should you do?

• A. Create a Cloud Run function that runs the pipeline daily. Monitor the functions execution using Cloud Monitoring.
• B. Create a direct acyclic graph (DAG) in Cloud Composer to orchestrate a pipeline trigger daily. Monitor the pipeline's execution using the Apache Airflow web interface and Cloud Monitoring.
• C. Use Cloud Scheduler to trigger a Dataproc job to execute the pipeline daily. Monitor the job's progress using the Dataproc job web interface and Cloud Monitoring.
• D. Configure Cloud Dataflow to implement the ETL pipeline, and use Cloud Scheduler to trigger the Dataflow pipeline daily. Monitor the pipelines execution using the Dataflow job monitoring interface and Cloud Monitoring.

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

Your company has an on-premises file server with 5 TB of data that needs to be migrated to Google Cloud.
The network operations team has mandated that you can only use up to 250 Mbps of the total available bandwidth for the migration. You need to perform an online migration to Cloud Storage. What should you do?

• A. Use the gcloud storage cp command to copy all files from on- premises to Cloud Storage using the -- daisy-chain option.
• B. Use the gcloud storage cp command to copy all files from on- premises to Cloud Storage using the --no- clobber option.
• C. Use Storage Transfer Service to configure an agent-based transfer. Set the appropriate bandwidth limit for the agent pool.
• D. Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google Cloud.

You have created a LookML model and dashboard that shows daily sales metrics for five regional managers to use. You want to ensure that the regional managers can only see sales metrics specific to their region. You need an easy-to-implement solution. What should you do?

• A. Add anaccess_filterExplore filter on theregion_namedimension by using thesales_regionuser attribute.
• B. Create asales_regionuser attribute, and assign each manager's region as the value of their user attribute.
• C. Create separate Looker dashboards for each regional manager. Set the default dashboard filter to the corresponding region for each manager.
• D. Create five different Explores with thesql_always_filterExplore filter applied on theregion_namedimension. Set eachregion_namevalue to the corresponding region for each manager.

You need to design a data pipeline that ingests data from CSV, Avro, and Parquet files into Cloud Storage.
The data includes raw user input. You need to remove all malicious SQL injections before storing the data in BigQuery. Which data manipulation methodology should you choose?

• A. ELT
• B. ETLT
• C. ETL
• D. EL