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

Section	Weight	Objectives
Data Transformation, Cleansing, and Quality	10%	- Apply data cleansing and validation rules - Implement schema evolution and management - Enforce data quality standards
Data Governance	7%	- Use Unity Catalog for governance - Enforce data policies and standards - Manage data assets and metadata
Cost & Performance Optimisation	13%	- Optimize compute and storage resources - Improve query and pipeline performance - Apply cost management best practices
Data Sharing and Federation	5%	- Manage cross-platform data access - Implement Lakehouse Federation - Use Delta Sharing for secure data sharing
Monitoring and Alerting	10%	- Track data lineage and metrics - Set up alerts and notifications - Monitor pipeline performance and health
Data Ingestion & Acquisition	7%	- Use Auto Loader and structured streaming - Ingest data from diverse sources - Handle incremental and batch data loads
Debugging and Deploying	10%	- Implement CI/CD and DevOps practices - Deploy using Asset Bundles, CLI, and APIs - Troubleshoot and debug pipelines
Developing Code for Data Processing using Python and SQL	22%	- Implement complex data processing logic - Write efficient and maintainable code - Use Databricks-specific libraries and APIs
Data Modelling	6%	- Optimize table design and partitioning - Design Medallion Architecture - Implement dimensional and relational models
Ensuring Data Security and Compliance	10%	- Implement access control and permissions - Secure data at rest and in transit - Ensure data privacy and compliance

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q103-Q108):

NEW QUESTION # 103
A data engineer needs to create an application that will collect information about the latest job run including the repair history. How should the data engineer format the request?

A. Call/api/2.1/jobs/runs/get with the job_id and include_history parameters
B. Call/api/2.1/jobs/runs/list with the job_id and include_history parameters
C. Call/api/2.1/jobs/runs/list with the run_id and include_history parameters
D. Call/api/2.1/jobs/runs/get with the run_id and include_history parameters

Answer: B

Explanation:
To retrieve information about the latest job runs along with their repair history, you use the jobs/runs/list endpoint with the job_id and include_history=true. This endpoint returns a list of runs for a specific job, including details about retries and repair attempts, which is not available via runs/get that retrieves a single run by run_id.

NEW QUESTION # 104
A table is registered with the following code:

Both users and orders are Delta Lake tables. Which statement describes the results of querying recent_orders?

A. All logic will execute when the table is defined and store the result of joining tables to the DBFS; this stored data will be returned when the table is queried.
B. Results will be computed and cached when the table is defined; these cached results will incrementally update as new records are inserted into source tables.
C. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.
D. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.
E. The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.

Answer: A

Explanation:
Table is created and data of join will be stored on DBFS and it will be returned on query time.

NEW QUESTION # 105
A data engineer wants to join a stream of advertisement impressions (when an ad was shown) with another stream of user clicks on advertisements to correlate when impressions led to monetizable clicks.
In the code below, Impressions is a streaming DataFrame with a watermark ("event_time", "10 minutes")

The data engineer notices the query slowing down significantly.
Which solution would improve the performance?

A. Joining on event time constraint: clickTime >= impressionTime - interval 3 hours and removing watermarks
B. Joining on event time constraint: clickTime + 3 hours < impressionTime - 2 hours
C. Joining on event time constraint: clickTime == impressionTime using a leftOuter join
D. Joining on event time constraint: clickTime >= impressionTime AND clickTime <= impressionTime interval 1 hour

Answer: D

NEW QUESTION # 106
A junior data engineer on your team has implemented the following code block.

The view new_events contains a batch of records with the same schema as the events Delta table. The event_id field serves as a unique key for this table.
When this query is executed, what will happen with new records that have the same event_id as an existing record?

A. They are inserted.
B. They are deleted.
C. They are ignored.
D. They are merged.
E. They are updated.

Answer: C

Explanation:
This is the correct answer because it describes what will happen with new records that have the same event_id as an existing record when the query is executed. The query uses the INSERT INTO command to append new records from the view new_events to the table events. However, the INSERT INTO command does not check for duplicate values in the primary key column (event_id) and does not perform any update or delete operations on existing records. Therefore, if there are new records that have the same event_id as an existing record, they will be ignored and not inserted into the table events.

NEW QUESTION # 107
A data engineer needs to implement column masking for a sensitive column in a Unity Catalog- managed table. The masking logic must dynamically check if users belong to specific groups defined in a separate table (group_access) that maps groups to allowed departments. Which approach should the engineer use to efficiently enforce this requirement?

A. Use a row filter to restrict access based on the user's group.
B. Create a UDF that hardcodes allowed groups and apply it as a column mask.
C. Create a view without selecting the sensitive column.
D. Apply a column mask that references the group_access mapping table in its UDF.

Answer: D

Explanation:
Databricks Unity Catalog supports dynamic column masking, where masking logic can be implemented using SQL functions or UDFs that reference external mapping tables or metadata for context-aware access control.
By referencing the group_access table inside the masking function, the mask dynamically evaluates whether a requesting user belongs to an authorized group. If permitted, the original column value is returned; otherwise, a masked value (such as NULL or asterisks) is shown.
This method enables fine-grained, data-driven masking policies while maintaining a single authoritative access mapping source.
Hardcoding values (A) reduces flexibility, and row filters (D) apply to entire rows rather than specific columns. Therefore, C correctly aligns with Databricks best practices for dynamic masking.

NEW QUESTION # 108
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