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Databricks Certified Data Engineer Professional Sample Questions:

1. A table named user_ltv is being used to create a view that will be used by data analysts on various teams. Users in the workspace are configured into groups, which are used for setting up data access using ACLs.
The user_ltv table has the following schema:
email STRING, age INT, ltv INT
The following view definition is executed:

An analyst who is not a member of the marketing group executes the following query:
SELECT * FROM email_ltv
Which statement describes the results returned by this query?

A) Three columns will be returned, but one column will be named "redacted" and contain only null values.
B) Only the email and ltv columns will be returned; the email column will contain the string
"REDACTED" in each row.
C) Only the email and itv columns will be returned; the email column will contain all null values.
D) The email, age. and ltv columns will be returned with the values in user ltv.
E) The email and ltv columns will be returned with the values in user itv.

2. A security team wants to enforce data protection for a customer table containing customer PII data. To comply with local policies, sales team members should only see customers from their region, while non-admin users should have email addresses masked. Which implementation approach should be used when using Unity Catalog row filters and column masks?

A) Use table ACLs to restrict access using tags with GRANT SELECT ON table_name WITH TAG command, and rely on application-level filtering for sensitive data based on user region.
B) Create SQL UDFs for row filtering based on user region and column masking based on group membership, then apply them using ALTER TABLE SET ROW FILTER and ALTER COLUMN SET MASK commands.
C) Implement row filters with SQL UDFs based on user region only since column masks cannot be combined with row filters on the same table, then apply them be recreating the table with DROP TABLE and CREATE TABLE SET ROW FILTER commands.
D) Create a view with dynamic WHERE clauses for region filtering and use string replacement functions for email masking using ALTER COLUMN SET MASK command.

3. A data engineer is configuring a Lakeflow Declarative Pipeline to process CDC (Change Data Capture) data from a source. The source events sometimes arrive out of order, and multiple updates may occur with the same update_timestamp but with different update_sequence_id.
What should the data engineer do to ensure events are sequenced correctly?

A) Use SEQUENCE BY STRUCT(event_timestamp, update_sequence_id) in AUTO CDC APIs.
B) Use dropDuplicates() to remove out-of-order and duplicate records in LDP.
C) Use a window function to sort update_sequence_id within the same partition, i.e., update_timestamp in the LDP pipeline.
D) Set track_history_column_list to [event_timestamp, event_id] in AUTO CDC APIs.

4. The data engineering team has configured a Databricks SQL query and alert to monitor the values in a Delta Lake table. The recent_sensor_recordings table contains an identifying sensor_id alongside the timestamp and temperature for the most recent 5 minutes of recordings.
The below query is used to create the alert:

The query is set to refresh each minute and always completes in less than 10 seconds. The alert is set to trigger when mean (temperature) > 120. Notifications are triggered to be sent at most every 1 minute.
If this alert raises notifications for 3 consecutive minutes and then stops, which statement must be true?

A) The total average temperature across all sensors exceeded 120 on three consecutive executions of the query
B) The average temperature recordings for at least one sensor exceeded 120 on three consecutive executions of the query
C) The maximum temperature recording for at least one sensor exceeded 120 on three consecutive executions of the query
D) The source query failed to update properly for three consecutive minutes and then restarted
E) The recent_sensor_recordingstable was unresponsive for three consecutive runs of the query

5. The view updates represents an incremental batch of all newly ingested data to be inserted or updated in the customers table.
The following logic is used to process these records.
MERGE INTO customers
USING (
SELECT updates.customer_id as merge_ey, updates .*
FROM updates
UNION ALL
SELECT NULL as merge_key, updates .*
FROM updates JOIN customers
ON updates.customer_id = customers.customer_id
WHERE customers.current = true AND updates.address <> customers.address ) staged_updates ON customers.customer_id = mergekey WHEN MATCHED AND customers. current = true AND customers.address <> staged_updates.address THEN UPDATE SET current = false, end_date = staged_updates.effective_date WHEN NOT MATCHED THEN INSERT (customer_id, address, current, effective_date, end_date) VALUES (staged_updates.customer_id, staged_updates.address, true, staged_updates.effective_date, null) Which statement describes this implementation?

A) The customers table is implemented as a Type 2 table; old values are overwritten and new customers are appended.
B) The customers table is implemented as a Type 0 table; all writes are append only with no changes to existing values.
C) The customers table is implemented as a Type 2 table; old values are maintained but marked as no longer current and new values are inserted.
D) The customers table is implemented as a Type 1 table; old values are overwritten by new values and no history is maintained.

Solutions: