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

Section Weight Objectives Topic 1: Data Modeling and Storage 20% - Data Modeling

- Storage Optimization

- File Formats

Topic 2: Data Quality and Governance 12% - Data Quality

- Data Lineage

- Governance

Topic 3: Data Processing 28% - Spark SQL

- Data Transformation

- ETL Pipelines

- Structured Streaming

Topic 4: Monitoring and Troubleshooting 16% - Troubleshooting

- Monitoring

- Performance Optimization

Topic 5: Databricks Lakehouse Platform 24% - Delta Lake

- Unity Catalog

- Data Management

- Lakehouse Architecture

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q224-Q229):

NEW QUESTION # 224

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 auditing group executes the following query:

SELECT * FROM user_ltv_no_minors

Which statement describes the results returned by this query?

  • A. All records from all columns will be displayed with the values in user_ltv.
  • B. All age values less than 18 will be returned as null values all other columns will be returned with the values in user_ltv.
  • C. All columns will be displayed normally for those records that have an age greater than 18; records not meeting this condition will be omitted.
  • D. All values for the age column will be returned as null values, all other columns will be returned with the values in user_ltv.
  • E. All columns will be displayed normally for those records that have an age greater than 17; records not meeting this condition will be omitted.

Answer: C

Explanation:

Given the CASE statement in the view definition, the result set for a user not in the auditing group would be constrained by the ELSE condition, which filters out records based on age. Therefore, the view will return all columns normally for records with an age greater than 18, as users who are not in the auditing group will not satisfy the is_member('auditing') condition. Records not meeting the age > 18 condition will not be displayed.


NEW QUESTION # 225

A data engineer manages a Unity Catalog table customer_data in schema finance that includes sensitive fields like ssn and credit_score. Intern Group should only see masked values, while Analyst Group should only access rows for their assigned region. The data engineer needs to restrict access based on user role and region without duplicating data. How should the data engineer enforce this security policy?

  • A. Use Unity Catalog's row filters based on the user roles and column masks based on the region.
  • B. Create dynamic views for each user role and manage access with ACLs.
  • C. Use Unity Catalog's row filters based on the region and column masks based on user roles.
  • D. Create views using current_user() and is_account_group_member() functions, and apply masking logic inside the SQL SELECT clause for each sensitive column.

Answer: C

Explanation:

Unity Catalog row filters can restrict which rows are visible based on attributes such as the user's assigned region, while column masks can dynamically obfuscate sensitive fields like ssn and credit_score based on user roles. This enforces fine-grained, role-and region-based access control directly at the table level without duplicating data or relying on custom views.


NEW QUESTION # 226

Which statement describes a key benefit of an end-to-end test?

  • A. It closely simulates real world usage of your application.
  • B. It pinpoint errors in the building blocks of your application.
  • C. It provides testing coverage for all code paths and branches.
  • D. It makes it easier to automate your test suite

Answer: A

Explanation:

End-to-end testing is a methodology used to test whether the flow of an application, from start to finish, behaves as expected. The key benefit of an end-to-end test is that it closely simulates real- world, user behavior, ensuring that the system as a whole operates correctly.


NEW QUESTION # 227

A company has a task management system that tracks the most recent status of tasks. The system takes task events as input and processes events in near real-time using Lakeflow Declarative Pipelines. A new task event is ingested into the system when a task is created or the task status is changed. Lakeflow Declarative Pipelines provides a streaming table (tasks_status) for BI users to query.

The table represents the latest status of all tasks and includes 5 columns:

task_id (unique for each task)

task_name

task_owner

task_status

task_event_time

The table enables three properties: deletion vectors, row tracking, and change data feed (CDF).

A data engineer is asked to create a new Lakeflow Declarative Pipeline to enrich the tasks_status table in near real-time by adding one additional column representing task_owner's department, which can be looked up from a static dimension table (employee).

How should this enrichment be implemented?

  • A. Create a new Lakeflow Declarative Pipeline: use the readStream() function with the option skipChangeCommits to read the tasks_status table; enrich with the employee table; store the result in a new streaming table.
  • B. Create a new Lakeflow Declarative Pipeline: use the read() function to read tasks_status table; enrich with employee table; store the result in a materialized view.
  • C. Create a new Lakeflow Declarative Pipeline: use readStream() function with option readChangeFeed to read tasks_status table CDF; enrich with the employee table; create a new streaming table as the result table and use apply_changes() function to process the changes from the enriched CDF.
  • D. Create a new Lakeflow Declarative Pipeline: use the readStream() function to read tasks_status table; enrich with the employee table; store the result in a new streaming table.

Answer: C

Explanation:

Change Data Feed (CDF) allows downstream consumers to read incremental changes (inserts, updates, deletes) from a Delta table. The documentation explains that when streaming from a Delta table with CDF enabled, developers can use readStream().option("readChangeFeed","true") to capture incremental events. For maintaining a derived table with enrichment logic, the recommended practice is to use apply_changes(), which applies CDC semantics (insert/update/delete) correctly to the target streaming table. By joining with the static employee dimension, enriched rows are generated before being merged into the new streaming target. This ensures correctness, scalability, and minimal latency. Batch reads or skipping commits do not maintain correctness for CDC pipelines.


NEW QUESTION # 228

A healthcare analytics team is implementing a dimensional model in Delta Lake for patient care analysis. They have a date dimension table and are evaluating design options to ensure it supports a wide range of time-based analyses. Which design approach for the date dimension will support efficient time-based querying and aggregation?

  • A. Store the date as string in ISO format (YYYY-MM-DD) for readability.
  • B. Store only the date value and calculate all time attributes in queries.
  • C. Pre-calculate attributes like fiscal_period, quarter, month_name, day_of_week, and holiday.
  • D. Create separate dimension tables for different calendar systems (fiscal, academic, etc.)

Answer: C

Explanation:

Pre-calculating commonly used time attributes in the date dimension enables efficient filtering, grouping, and aggregation without requiring repeated computations at query time. This design supports a wide range of time-based analyses and improves query performance and usability in dimensional models.


NEW QUESTION # 229

......

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