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

Section	Weight	Objectives
Topic 1: Developing Code for Data Processing using Python and SQL	~22%	- Manage dependencies, libraries, and UDFs - Implement scalable Python/SQL code and project structures - Build pipelines with Lakeflow Spark Declarative Pipelines and Auto Loader
Topic 2: Data Transformation, Cleansing, and Quality	~12%	- Apply advanced Spark transformations - Enforce data quality and quarantine bad data
Topic 3: Data Modeling	~10%	- Design scalable Delta Lake schemas and clustering - Apply dimensional modeling techniques
Topic 4: Security and Governance	~10%	- Manage Unity Catalog permissions and ACLs - Implement row-level security, column masking, and compliance
Topic 5: Monitoring, Logging, and Troubleshooting	~8%	- Use Spark UI, Query Profiler, and system tables - Diagnose common pipeline and job failures
Topic 6: CI/CD, Testing, and Deployment	~6%	- Deploy with Declarative Automation Bundles, CLI, and REST API - Implement testing and deployment pipelines
Topic 7: Cost and Performance Optimization	~13%	- Leverage system tables and observability tools - Optimize queries, clusters, and storage
Topic 8: Streaming Workloads and Change Data Capture	~11%	- Apply AUTO CDC APIs and exactly-once semantics - Implement reliable streaming pipelines
Topic 9: Data Sharing and Federation	~8%	- Configure Delta Sharing and Lakehouse Federation

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Databricks Certified Data Engineer Professional Sample Questions (Q202-Q207):

NEW QUESTION # 202
A data engineering team is collaborating on a Databricks project where each team member needs to develop and test code independently before merging changes into the main branch.
They want to avoid accidental overwrites or branch switching issues while ensuring that all work is version- controlled and can be integrated into their CI/CD pipeline.
How should the data engineer achieve collaboration?

A. All team members work in the same Databricks Git folder and perform Git operations (pull, push, commit, branch switching) directly in that shared folder.
B. Team members edit notebooks directly in the workspace's shared folder and periodically copy changes into a Git folder for version control.
C. Team members use the Databricks CLI to clone the Git repository and perform Git operations from a cluster's web terminal.
D. Each team member creates their own Databricks Git folder, mapped to the same remote Git repository, and works in their own development branch within their personal folder.

Answer: D

Explanation:
Using separate Databricks Git folders per user mapped to the same remote repository allows each team member to work independently on their own branch without interfering with others.
This prevents accidental overwrites or branch conflicts while ensuring all changes are version- controlled and easily integrated into CI/CD workflows.

NEW QUESTION # 203
A data engineer needs to capture pipeline settings from an existing in the workspace, and use them to create and version a JSON file to create a new pipeline. Which command should the data engineer enter in a web terminal configured with the Databricks CLI?

A. Use the get command to capture the settings for the existing pipeline; remove the pipeline_id and rename the pipeline; use this in a create command
B. Use list pipelines to get the specs for all pipelines; get the pipeline spec from the return results parse and use this to create a pipeline
C. Use the alone command to create a copy of an existing pipeline; use the get JSON command to get the pipeline definition; save this to git
D. Stop the existing pipeline; use the returned settings in a reset command

Answer: A

Explanation:
The Databricks CLI provides a way to automate interactions with Databricks services. When dealing with pipelines, you can use the databricks pipelines get --pipeline-id command to capture the settings of an existing pipeline in JSON format. This JSON can then be modified by removing the pipeline_id to prevent conflicts and renaming the pipeline to create a new pipeline. The modified JSON file can then be used with the databricks pipelines create command to create a new pipeline with those settings.

NEW QUESTION # 204
What statement is true regarding the retention of job run history?

A. It is retained for 60 days, during which you can export notebook run results to HTML
B. It is retained for 60 days, after which logs are archived
C. It is retained until you export or delete job run logs
D. It is retained for 90 days or until the run-id is re-used through custom run configuration
E. It is retained for 30 days, during which time you can deliver job run logs to DBFS or S3

Answer: A

Explanation:
https://docs.databricks.com/en/workflows/jobs/monitor-job-runs.html

NEW QUESTION # 205
The data architect has mandated that all tables in the Lakehouse should be configured as external (also known as "unmanaged") Delta Lake tables.
Which approach will ensure that this requirement is met?

A. When configuring an external data warehouse for all table storage, leverage Databricks for all ELT.
B. When tables are created, make sure that the EXTERNAL keyword is used in the CREATE TABLE statement.
C. When data is saved to a table, make sure that a full file path is specified alongside the Delta format.
D. When the workspace is being configured, make sure that external cloud object storage has been mounted.
E. When a database is being created, make sure that the LOCATION keyword is used.

Answer: B

Explanation:
To create an external or unmanaged Delta Lake table, you need to use the EXTERNAL keyword in the CREATE TABLE statement. This indicates that the table is not managed by the catalog and the data files are not deleted when the table is dropped. You also need to provide a LOCATION clause to specify the path where the data files are stored.
For example:
CREATE EXTERNAL TABLE events ( date DATE, eventId STRING, eventType STRING, data STRING) USING DELTA LOCATION `/mnt/delta/events'; This creates an external Delta Lake table named events that references the data files in the
`/mnt/delta/events' path. If you drop this table, the data files will remain intact and you can recreate the table with the same statement.

NEW QUESTION # 206
How are the operational aspects of Lakeflow Declarative Pipelines different from Spark Structured Streaming?

A. Lakeflow Declarative Pipelines manage the orchestration of multi-stage pipelines automatically, while Structured Streaming requires external orchestration for complex dependencies.
B. Lakeflow Declarative Pipelines automatically handle schema evolution, while Structured Streaming always requires manual schema management.
C. Lakeflow Declarative Pipelines can write to Delta Lake format, while Structured Streaming cannot.
D. Structured Streaming can process continuous data streams, while Lakeflow Declarative Pipelines cannot.

Answer: A

Explanation:
Databricks documentation explains that Lakeflow Declarative Pipelines build upon Structured Streaming but add higher-level orchestration and automation capabilities. They automatically manage dependencies, materialization, and recovery across multi-stage data flows without requiring external orchestration tools such as Airflow or Azure Data Factory. In contrast, Structured Streaming operates at a lower level, where developers must manually handle orchestration, retries, and dependencies between streaming jobs. Both support Delta Lake outputs and schema evolution; however, Lakeflow Declarative Pipelines simplify management by declaratively defining transformations and data quality expectations. Hence, the correct distinction is A -- automated orchestration and management in Lakeflow Declarative Pipelines.

NEW QUESTION # 207
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