New Release Databricks-Certified-Professional-Data-Engineer Databricks Certification Questions

The code uses the DELETE FROM command to delete records from the users table that match a condition based on a join with another table called delete_requests, which contains all users that have requested deletion. The DELETE FROM command deletes records from a Delta Lake table by creating a new version of the table that does not contain the deleted records. However, this does not guarantee that the records to be deleted are no longer accessible, because Delta Lake supports time travel, which allows querying previous versions of the table using a timestamp or version number. Therefore, files containing deleted records may still be accessible with time travel until a vacuum command is used to remove invalidated data files from physical storage. Verified References: [Databricks Certified Data Engineer Professional], under “Delta Lake” section; Databricks Documentation, under “Delete from a table” section; Databricks Documentation, under “Remove files no longer referenced by a Delta table” section.

The key requirement is to detect when the daily average of percent_null is greater than 5% for three consecutive days .

Option A only checks the last 24 hours , not consecutive days . It would trigger too frequently and cause spam.

Option C calculates an average across all records in the last 3 days , but this could be skewed by one high or low day — it does not ensure consecutive daily violations .

Option D simply counts days where the threshold was exceeded, but it does not guarantee that those days were consecutive . This could incorrectly trigger on non-adjacent violations.

Option B is correct:

It aggregates hourly values into daily averages .

It checks that the last 3 consecutive days all had averages above 5%.

It avoids redundant alerts by using Notification Frequency: Just once.

This matches Databricks Lakehouse Monitoring best practices , where SQL alerts should be designed to aggregate metrics to the correct granularity (daily here) and ensure consecutive threshold violations before triggering.

Reference (Databricks Lakehouse Monitoring, SQL Alerts Best Practices):

Use DATE_TRUNC to compute metrics at the correct time granularity.

To detect consecutive-day issues, filter the last N daily aggregates and check conditions across all rows.

Always configure alerts with controlled notification frequency to prevent alert fatigue.

This is the correct answer because it allows manual confirmation that these three requirements have been met. The requirements are that all tables containing Personal Identifiable Information (PII) must be clearly annotated, which includes adding column comments, table comments, and setting the custom table property “contains_pii” = true. The DESCRIBE EXTENDED command is used to display detailed information about a table, such as its schema, location, properties, and comments. By using this command on the dev.pii_test table, one can verify that the table has been created with the correct column comments, table comment, and custom table property as specified in the SQL DDL statement. Verified References: [Databricks Certified Data Engineer Professional], under “Lakehouse” section; Databricks Documentation, under “DESCRIBE EXTENDED” section.

According to Databricks’ documentation, Open Sharing allows secure sharing of Delta tables to any recipient via a REST-based protocol without requiring a Databricks account. However, the Open Sharing protocol is limited to static Delta tables —it does not support sharing of Unity Catalog objects like Volumes, Machine Learning models, or notebooks . Only Databricks-to-Databricks sharing supports dynamic data sharing with update visibility and streaming reads. Thus, the inability to share non-table objects in Open Sharing represents a known limitation of the protocol. Option A accurately reflects this constraint as described in Delta Sharing design principles and documentation.