Databricks-Certified-Professional-Data-Engineer Exam Dumps : Databricks Certified Data Engineer Professional Exam

 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 code creates a view called email_ltv that selects the email and ltv columns from a table called user_ltv, which has the following schema: email STRING, age INT, ltv INT. The code also uses the CASE WHEN expression to replace the email values with the string “REDACTED” if the user is not a member of the marketing group. The user who executes the query is not a member of the marketing group, so they will only see the email and ltv columns, and the email column will contain the string “REDACTED” in each row. Verified References: [Databricks Certified Data Engineer Professional], under “Lakehouse” section; Databricks Documentation, under “CASE expression” section.

When a table is dropped in Delta Lake, the table is removed from the catalog and the data is deleted. This is because Delta Lake is a transactional storage layer that provides ACID guarantees. When a table is dropped, the transaction log is updated to reflect the deletion of the table and the data is deleted from the underlying storage. References: