Learn with Google : ADP training material for 100% pass

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 the "Pub/Sub to BigQuery" Dataflow template with a UDF, and write 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 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.

Your organization has several datasets in their data warehouse in BigQuery. Several analyst teams in different departments use the datasets to run queries. Your organization is concerned about the variability of their monthly BigQuery costs. You need to identify a solution that creates a fixed budget for costs associated with the queries run by each department. What should you do?

• A. Assign each analyst to a separate project associated with their department. Create a single reservation by using BigQuery editions. Assign all projects to the reservation.
• B. Assign each analyst to a separate project associated with their department. Create a single reservation for each department by using BigQuery editions. Create assignments for each project in the appropriate reservation.
• C. Create a single reservation by using BigQuery editions. Assign all analysts to the reservation.
• D. Create a custom quota for each analyst in BigQuery.

You are a data analyst working with sensitive customer data in BigQuery. You need to ensure that only authorized personnel within your organization can query this data, while following the principle of least privilege. What should you do?

• A. Update dataset privileges by using the SQL GRANT statement.
• B. Enable access control by using IAM roles.
• C. Encrypt the data by using customer-managed encryption keys (CMEK).
• D. Export the data to Cloud Storage, and use signed URLs to authorize access.

You are designing a BigQuery data warehouse with a team of experienced SQL developers. You need to recommend a cost- effective, fully-managed, serverless solution to build ELT processes with SQL pipelines.
Your solution must include source code control, environment parameterization, and data quality checks. What should you do?

• A. Use Dataproc to run MapReduce jobs for distributed data processing.
• B. Use Cloud Data Fusion to visually design and manage the pipelines.
• C. Use Dataform to build, orchestrate, and monitor the pipelines.
• D. Use Cloud Composer to orchestrate and run data workflows.

You are predicting customer churn for a subscription-based service. You have a 50 PB historical customer dataset in BigQuery that includes demographics, subscription information, and engagement metrics. You want to build a churn prediction model with minimal overhead. You want to follow the Google-recommended approach. What should you do?

• A. Export the data from BigQuery to a local machine. Use scikit- learn in a Jupyter notebook to build the churn prediction model.
• B. Use the BigQuery Python client library in a Jupyter notebook to query and preprocess the data in BigQuery. Use the CREATE MODEL statement in BigQueryML to train the churn prediction model.
• C. Create a Looker dashboard that is connected to BigQuery. Use LookML to predict churn.
• D. Use Dataproc to create a Spark cluster. Use the Spark MLlib within the cluster to build the churn prediction model.