Your company uses Looker as its primary business intelligence platform. You want to use LookML to visualize the profit margin for each of your company's products in your Looker Explores and dashboards.
You need to implement a solution quickly and efficiently. What should you do?

• A. Apply a filter to only show products with a positive profit margin.
• B. Define a new measure that calculates the profit margin by using the existing revenue and cost fields.
• C. Create a derived table that pre-calculates the profit margin for each product, and include it in the Looker model.
• D. Create a new dimension that categorizes products based on their profit margin ranges (e.g., high, medium, low).

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 Cloud Storage, and create an external table in BigQuery. Create a BigQuery scheduled job that executes once each day to apply transformations.
• B. Push event information to a Pub/Sub topic. Create a BigQuery subscription in Pub/Sub.
• 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 Dataflow job using the Dataflow job builder.

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

You are working on a data pipeline that will validate and clean incoming data before loading it into BigQuery for real-time analysis. You want to ensure that the data validation and cleaning is performed efficiently and can handle high volumes of dat a. What should you do?

• A. Load the raw data into BigQuery using Cloud Storage as a staging area, and use SQL queries in BigQuery to validate and clean the data.
• B. Use Dataflow to create a streaming pipeline that includes validation and transformation steps.
• C. Write custom scripts in Python to validate and clean the data outside of Google Cloud. Load the cleaned data into BigQuery.
• D. Use Cloud Run functions to trigger data validation and cleaning routines when new data arrives in Cloud Storage.

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