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

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

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Real Databricks Databricks-Certified-Data-Engineer-Professional Exam Questions [2026]-Secrets To Pass Exam In First Try

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q22-Q27):

NEW QUESTION # 22
A platform team lead is responsible for automating the individual teams attribution towards SQL Warehouse usage. The requirement is to identify the SQL warehouse usage at the individual user's level and generate a daily report to be shared with an executive team that includes leaders from all business units. How should the platform lead generate an automated report that can be shared daily?

A. Restrict users from running any SQL query unless they provide all the query details so that the attribution can be calculated and shared with the executive team.
B. Let the users run the SQL query and then directly report the usage to the executives. The ownership of the SQL warehouse usage will be with the individual teams.
C. Use the system tables to capture the audit and billing usage data and create a dashboard with daily refresh schedules and shared with the executive team.
D. Use the system tables to capture the audit and billing usage data and share the queries with the executive team. This enables the executives to execute the query and see the latest results any time.

Answer: C

Explanation:
System tables provide authoritative audit and billing data needed for per-user SQL Warehouse attribution. Creating a dashboard with a scheduled daily refresh automates report generation and ensures executives receive consistent, up-to-date insights without needing to run queries themselves.

NEW QUESTION # 23
Which statement describes Delta Lake optimized writes?

A. Optimized writes logical partitions instead of directory partitions partition boundaries are only represented in metadata fewer small files are written.
B. Before a job cluster terminates, OPTIMIZE is executed on all tables modified during the most recent job.
C. An asynchronous job runs after the write completes to detect if files could be further compacted; yes, an OPTIMIZE job is executed toward a default of 1 GB.
D. A shuffle occurs prior to writing to try to group data together resulting in fewer files instead of each executor writing multiple files based on directory partitions.

Answer: D

Explanation:
Delta Lake optimized writes involve a shuffle operation before writing out data to the Delta table.
The shuffle operation groups data by partition keys, which can lead to a reduction in the number of output files and potentially larger files, instead of multiple smaller files. This approach can significantly reduce the total number of files in the table, improve read performance by reducing the metadata overhead, and optimize the table storage layout, especially for workloads with many small files.

NEW QUESTION # 24
Which of the following technologies can be used to identify key areas of text when parsing Spark Driver log4j output?

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

Answer: C

Explanation:
Regex, or regular expressions, are a powerful way of matching patterns in text. They can be used Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from to identify key areas of text when parsing Spark Driver log4j output, such as the log level, the timestamp, the thread name, the class name, the method name, and the message. Regex can be applied in various languages and frameworks, such as Scala, Python, Java, Spark SQL, and Databricks notebooks.

NEW QUESTION # 25
Two data engineers are working on the same Databricks notebook in separate branches. Both have edited the same section of code. When one tries to merge the other's branch into their own using the Databricks Git folders UI, a merge conflict occurs on that notebook file. The UI highlights the conflict and presents options for resolution. How should the data engineers resolve this merge conflict using Databricks Git folders?

A. Use the Git folders UI to manually edit the notebook file, selecting the desired lines from both versions and removing the conflict markers, then mark the conflict as resolved.
B. Abort the merge, discard all local changes, and try the merge operation again without reviewing the conflicting code.
C. Delete the conflicted notebook file via the Databricks workspace UI, commit the deletion, and recreate the notebook from scratch in a new commit to bypass the conflict entirely.
D. Use the Git CLI in the cluster's web terminal to force-push the conflicted merge (git push -force), overriding the remote branch with the local version and discarding changes.

Answer: A

Explanation:
In the Databricks Git folders integration, when merge conflicts arise in notebooks, the UI provides a visual diff editor that highlights conflicting code segments. Users can manually choose which changes to keep from each branch, edit directly in the notebook UI, and remove conflict markers.
After resolving, the engineer must mark the conflict as resolved, save, and commit the final version.
This process ensures that both contributors' valid code segments are merged correctly and version history is maintained.
Forcing a push (C) or deleting notebooks (B) introduces data loss or versioning issues. Aborting without review (A) violates collaborative best practices. Therefore, D is the only correct and Databricks-approved way to resolve notebook merge conflicts.

NEW QUESTION # 26
A junior developer complains that the code in their notebook isn't producing the correct results in the development environment. A shared screenshot reveals that while they're using a notebook versioned with Databricks Repos, they're using a personal branch that contains old logic. The desired branch named dev-2.3.9 is not available from the branch selection dropdown.
Which approach will allow this developer to review the current logic for this notebook?

A. Use Repos to pull changes from the remote Git repository and select the dev-2.3.9 branch.
B. Use Repos to make a pull request use the Databricks REST API to update the current branch to dev-2.3.9
C. Use Repos to merge the current branch and the dev-2.3.9 branch, then make a pull request to sync with the remote repository
D. Use Repos to checkout the dev-2.3.9 branch and auto-resolve conflicts with the current branch
E. Merge all changes back to the main branch in the remote Git repository and clone the repo again

Answer: A

Explanation:
This is the correct answer because it will allow the developer to update their local repository with the latest changes from the remote repository and switch to the desired branch. Pulling changes will not affect the current branch or create any conflicts, as it will only fetch the changes and not merge them. Selecting the dev-2.3.9 branch from the dropdown will checkout that branch and display its contents in the notebook.

NEW QUESTION # 27
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