Using the Looker Scheduler with user attribute filters is the Google-recommended approach to efficiently automate the delivery of a customized dashboard. User attribute filters allow you to dynamically customize the dashboard's content based on the recipient's attributes, ensuring each stakeholder sees data relevant to them. This approach is scalable, does not require creating separate models or custom scripts, and leverages Looker's built-in functionality to automate recurring deliveries effectively.

To calculate the weekly moving average of sales by location:
* The query must group bystore_id(partitioning the calculation by each store).
* TheORDER BY dateensures the sales are evaluated chronologically.
* TheROWS BETWEEN 6 PRECEDING AND CURRENT ROWspecifies a rolling window of 7 rows (1 week if each row represents daily data).
* TheAVG(total_sales)computes the average sales over the defined rolling window.
Chosen querymeets these requirements:
* PARTITION BY store_idgroups the calculation by each store.
* ORDER BY dateorders the rows correctly for the rolling average.
* ROWS BETWEEN 6 PRECEDING AND CURRENT ROWensures the 7-day moving average.
Extract from Google Documentation: From "Analytic Functions in BigQuery" (https://cloud.google.com
/bigquery/docs/reference/standard-sql/analytic-function-concepts):"Use ROWS BETWEEN n PRECEDING AND CURRENT ROW with ORDER BY a time column to compute moving averages over a fixed number of rows, such as a 7-day window, partitioned by a grouping key like store_id."

Comprehensive and Detailed in Depth Explanation:
Why D is correct:Creating a BigQuery external table directly from the Google Sheet allows for real-time updates.
Joining the external table with the actual cost table in BigQuery performs the calculation.
Connected Sheets allows the team to access and analyze the results directly in Google Sheets, with the data being updated.
Why other options are incorrect:A: Saving as a CSV file loses the live connection and daily updates.
B: Downloading and uploading as a CSV file adds unnecessary steps and loses the live connection.
C: Same issue as B, losing the live connection.

Using Database Migration Service (DMS) to replicate the MySQL database to a Cloud SQL for MySQL instance is the best approach. DMS is a fully managed service designed for migrating databases to Google Cloud with minimal downtime and cost. It supports continuous data replication, ensuring data integrity during the migration process, and handles schema and data transfer efficiently. This solution is particularly suited for large tables and transactional data, as it maintains real-time synchronization between the source and target databases, minimizing downtime for the migration.

Comprehensive and Detailed in Depth Explanation:
Why C is correct:Dataflow is a fully managed service for transforming and enriching data in both batch and streaming modes.
Dataflow is googles recomended way to transform large datasets.
It is designed for parallel processing, making it suitable for large datasets.
Why other options are incorrect:A: Manual downloading and scripting is not scalable or efficient.
B: Cloud Run functions are for stateless applications, not large data transformations.
D: While Cloud Data fusion could work, Dataflow is more optimized for large scale data transformation.