Databricks documents delta.deletedFileRetentionDuration as a Delta table property and shows that Delta table properties are modified with SET TBLPROPERTIES . The documented value format is an interval expression such as ' interval 7 days ' , so ' interval 15 days ' is the correct way to set the retention window on the table itself. ( Databricks Documentation ) Option C controls a specific VACUUM run and is shown in hours here, not as a persistent table-level retention setting. Option D sets a Spark configuration rather than the table property required by the question.
Option B is not the documented API for setting this Delta retention property. ( Databricks Documentation )
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This is the correct answer because Cmd 1 is written in Python and uses a list comprehension to extract the country names from the geo_lookup table and store them in a Python variable named countries af. This variable will contain a list of strings, not a PySpark DataFrame or a SQL view. Cmd 2 is written in SQL and tries to create a view named sales af by selecting from the sales table where city is in countries af. However, this command will fail because countries af is not a valid SQL entity and cannot be used in a SQL query. To fix this, a better approach would be to use spark.sql() to execute a SQL query in Python and pass the countries af variable as a parameter. Verified References: [Databricks Certified Data Engineer Professional], under
"Language Interoperability" section; Databricks Documentation, under "Mix languages" section.

The Query Profiler in Databricks SQL and notebooks provides a detailed breakdown of query performance metrics. The "Total wall-clock duration" metric directly represents the total elapsed time from query start to completion, including all execution, planning, and waiting stages. In contrast, "Aggregated task time" reflects the cumulative duration across all parallel tasks, which does not equal the total elapsed wall time since tasks often run concurrently. Using job duration from Spark UI can underestimate or overestimate runtime when queries span multiple jobs. Therefore, the Query Profiler's total wall-clock duration is the officially documented method to determine actual query execution time.