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Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:

Databricks Certified Data Engineer Professional Sample Questions:

Question 1

Which of the following technologies can be used to identify key areas of text when parsing Spark Driver log4j output?

A. Julia
B. C++
C. Scala Datasets
D. pyspsark.ml.feature
E. Regex

Question 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. Create a view with dynamic WHERE clauses for region filtering and use string replacement functions for email masking using ALTER COLUMN SET MASK command.
B. 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.
C. 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.
D. 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.

Question 3

An organization processes customer data from web and mobile applications. Data includes names, emails, phone numbers, and location history. Data arrives both as batch files (from SFTP daily) and streaming JSON events (from Kafka in real-time).
To comply with data privacy policies, the following requirements must be met:
- Personally Identifiable Information (PII) such as email, phone
number, and IP address must be masked or anonymized before storage.
- Both batch and streaming pipelines must apply consistent PII
handling.
- Masking logic must be auditable and reproducible.
- The masked data must remain usable for downstream analytics.
How should the data engineer design a compliant data pipeline on Databricks that supports both batch and streaming modes, applies data masking to PII, and maintains traceability for audits?

A. Load batch data with notebooks and ingest streaming data with SQL Warehouses; use Unity Catalog column masks on Silver tables to redact fields after storage.
B. Allow PII to be stored unmasked in Bronze for lineage tracking, then apply masking logic in Gold tables used for reporting.
C. Ingest both batch and streaming data using Lakeflow Declarative Pipelines, and apply masking via Unity Catalog column masks at read time to avoid modifying the data during ingestion.
D. Use Lakeflow Declarative Pipelines for batch and streaming ingestion, define a PII masking function, and apply it during Bronze ingestion before writing to Delta Lake.

Question 4

An analytics team wants to run a short-term experiment in Databricks SQL on the customer transactions Delta table (about 20 billion records) created by the data engineering team. Which strategy should the data engineering team use to ensure minimal downtime and no impact on the ongoing ETL processes?

A. Shallow clone the table for the analytics team.
B. Create a new table for the analytics team using a CTAS statement.
C. Deep clone the table for the analytics team.
D. Give the analytics team direct access to the production table.

Question 5

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

Solutions: