Your company uses Looker to generate and share reports with various stakeholders. You have a complex dashboard with several visualizations that needs to be delivered to specific stakeholders on a recurring basis, with customized filters applied for each recipient. You need an efficient and scalable solution to automate the delivery of this customized dashboard. You want to follow the Google- recommended approach. What should you do?

• A. Create a script using the Looker Python SDK, and configure user attribute filter values. Generate a new scheduled plan for each stakeholder.
• B. Create a separate LookML model for each stakeholder with predefined filters, and schedule the dashboards using the Looker Scheduler.
• C. Use the Looker Scheduler with a user attribute filter on the dashboard, and send the dashboard with personalized filters to each stakeholder based on their attributes.
• D. Embed the Looker dashboard in a custom web application, and use the application's scheduling features to send the report with personalized filters.

Your organization needs to store historical customer order dat
a. The data will only be accessed once a month for analysis and must be readily available within a few seconds when it is accessed. You need to choose a storage class that minimizes storage costs while ensuring that the data can be retrieved quickly. What should you do?

• A. Store the data in Cloud Storage using Standard storage.
• B. Store the data in Cloud Storage using Archive storage.
• C. Store the data in Cloud Storage using Coldline storage.
• D. Store the data in Cloud Storage using Nearline storage.

Your company's customer support audio files are stored in a Cloud Storage bucket. You plan to analyze the audio files' metadata and file content within BigQuery to create inference by using BigQuery ML. You need to create a corresponding table in BigQuery that represents the bucket containing the audio files. What should you do?

• A. Create an external table.
• B. Create a native table.
• C. Create a temporary table.
• D. Create an object table.

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. Create separate Looker dashboards for each regional manager. Set the default dashboard filter to the corresponding region for each manager.
• B. Create asales_regionuser attribute, and assign each manager's region as the value of their user attribute.
• C. Add anaccess_filterExplore filter on theregion_namedimension by using thesales_regionuser attribute.
• 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 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. Write custom scripts in Python to validate and clean the data outside of Google Cloud. Load the cleaned data into BigQuery.
• B. 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.
• C. Use Dataflow to create a streaming pipeline that includes validation and transformation steps.
• D. Use Cloud Run functions to trigger data validation and cleaning routines when new data arrives in Cloud Storage.