A data engineering team needs to implement a tagging system for their tables as part of an automated ETL process, and needs to apply tags programmatically to tables in Unity Catalog.
Which SQL command adds tags to a table programmatically?

• A. ALTER TABLE table_name SET TAGS ('key1' = 'value1', 'key2' = 'value2');
• B. APPLY TAGS ON table_name VALUES ('key1' = 'value1', 'key2' = 'value2');
• C. SET TAGS FOR table_name AS ('key1' = 'value1', 'key2' = 'value2');
• D. COMMENT ON TABLE table_name TAGS ('key1' = 'value1', 'key2' = 'value2');

Which approach demonstrates a modular and testable way to use DataFrame transform for ETL code in PySpark?

• A.
  
• B.
  
• C.
  
• D.
  

A data organization has adopted Delta Sharing to securely distribute curated datasets from a Unity Catalog-enabled workspace. The data engineering team shares large Delta tables internally via Databricks-to-Databricks and externally via Open Sharing for aggregated reports. While testing, they encounter challenges related to access control, data update visibility, and shareable object types. What is a limitation of the Delta Sharing protocol or implementation when used with Databricks-to-Databricks or Open Sharing?

• A. Delta Sharing does not support Unity Catalog-enabled tables; only legacy Hive Metastore tables are shareable.
• B. Delta Sharing (both Databricks-to-Databricks and Open Sharing) allows recipients to modify the source data if they have select privileges.
• C. With Databricks-to-Databricks sharing, Unity Catalog recipients must re-ingest data manually using COPY INTO or REST APIs.
• D. With Open Sharing, recipients cannot access Volumes, Models, or notebooks -- only static Delta tables are supported.

The data engineering team maintains a table of aggregate statistics through batch nightly updates. This includes total sales for the previous day alongside totals and averages for a variety of time periods including the 7 previous days, year-to-date, and quarter-to-date. This table is named store_saies_summary and the schema is as follows:

The table daily_store_sales contains all the information needed to update store_sales_summary.
The schema for this table is:
store_id INT, sales_date DATE, total_sales FLOAT
If daily_store_sales is implemented as a Type 1 table and the total_sales column might be adjusted after manual data auditing, which approach is the safest to generate accurate reports in the store_sales_summary table?

• A. Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and append new rows nightly to the store_sales_summary table.
• B. Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and use upsert logic to update results in the store_sales_summary table.
• C. Use Structured Streaming to subscribe to the change data feed for daily_store_sales and apply changes to the aggregates in the store_sales_summary table with each update.
• D. Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and overwrite the store_sales_summary table with each Update.
• E. Implement the appropriate aggregate logic as a Structured Streaming read against the daily_store_sales table and use upsert logic to update results in the store_sales_summary table.

To reduce storage and compute costs, the data engineering team has been tasked with curating a series of aggregate tables leveraged by business intelligence dashboards, customer-facing applications, production machine learning models, and ad hoc analytical queries.
The data engineering team has been made aware of new requirements from a customer-facing application, which is the only downstream workload they manage entirely. As a result, an aggregate table used by numerous teams across the organization will need to have a number of fields renamed, and additional fields will also be added.
Which of the solutions addresses the situation while minimally interrupting other teams in the organization without increasing the number of tables that need to be managed?

• A. Create a new table with the required schema and new fields and use Delta Lake's deep clone functionality to sync up changes committed to one table to the corresponding table.
• B. Add a table comment warning all users that the table schema and field names will be changing on a given date; overwrite the table in place to the specifications of the customer-facing application.
• C. Send all users notice that the schema for the table will be changing; include in the communication the logic necessary to revert the new table schema to match historic queries.
• D. Configure a new table with all the requisite fields and new names and use this as the source for the customer-facing application; create a view that maintains the original data schema and table name by aliasing select fields from the new table.
• E. Replace the current table definition with a logical view defined with the query logic currently writing the aggregate table; create a new table to power the customer-facing application.