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

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

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Certified-Data-Engineer-Professional Vorbereitung - Certified-Data-Engineer-Professional Zertifizierungsprüfung

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Databricks Certified Data Engineer Professional Certified-Data-Engineer-Professional Prüfungsfragen mit Lösungen (Q151-Q156):

151. Frage
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. Team members use the Databricks CLI to clone the Git repository and perform Git operations from a cluster's web terminal.
B. Team members edit notebooks directly in the workspace's shared folder and periodically copy changes into a Git folder for version control.
C. All team members work in the same Databricks Git folder and perform Git operations (pull, push, commit, branch switching) directly in that shared folder.
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.

Antwort: D

Begründung:
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.

152. Frage
A team of data engineer are adding tables to a DLT pipeline that contain repetitive expectations for many of the same data quality checks.
One member of the team suggests reusing these data quality rules across all tables defined for this pipeline.
What approach would allow them to do this?

A. Add data quality constraints to tables in this pipeline using an external job with access to pipeline configuration files.
B. Maintain data quality rules in a separate Databricks notebook that each DLT notebook of file.
C. Use global Python variables to make expectations visible across DLT notebooks included in the same pipeline.
D. Maintain data quality rules in a Delta table outside of this pipeline's target schema, providing the schema name as a pipeline parameter.

Antwort: D

Begründung:
Maintaining data quality rules in a centralized Delta table allows for the reuse of these rules across multiple DLT (Delta Live Tables) pipelines. By storing these rules outside the pipeline's target schema and referencing the schema name as a pipeline parameter, the team can apply the same set of data quality checks to different tables within the pipeline. This approach ensures consistency in data quality validations and reduces redundancy in code by not having to replicate the same rules in each DLT notebook or file.

153. Frage
The business intelligence team has a dashboard configured to track various summary metrics for retail stories. This includes total sales for the previous day alongside totals and averages for a variety of time periods. The fields required to populate this dashboard have the following schema:

For Demand forecasting, the Lakehouse contains a validated table of all itemized sales updated incrementally in near real-time. This table named products_per_order, includes the following fields:

Because reporting on long-term sales trends is less volatile, analysts using the new dashboard only require data to be refreshed once daily. Because the dashboard will be queried interactively by many users throughout a normal business day, it should return results quickly and reduce total compute associated with each materialization.
Which solution meets the expectations of the end users while controlling and limiting possible costs?

A. Use the Delta Cache to persists the products_per_order table in memory to quickly the dashboard with each query.
B. Configure a webhook to execute an incremental read against products_per_order each time the dashboard is refreshed.
C. Use Structure Streaming to configure a live dashboard against the products_per_order table within a Databricks notebook.
D. Populate the dashboard by configuring a nightly batch job to save the required values as a table overwritten with each update.
E. Define a view against the products_per_order table and define the dashboard against this view.

Antwort: D

154. Frage
Which statement regarding stream-static joins and static Delta tables is correct?

A. Stream-static joins cannot use static Delta tables because of consistency issues.
B. The checkpoint directory will be used to track updates to the static Delta table.
C. Each microbatch of a stream-static join will use the most recent version of the static Delta table as of the job's initialization.
D. The checkpoint directory will be used to track state information for the unique keys present in the join.
E. Each microbatch of a stream-static join will use the most recent version of the static Delta table as of each microbatch.

Antwort: E

Begründung:
This is the correct answer because stream-static joins are supported by Structured Streaming when one of the tables is a static Delta table. A static Delta table is a Delta table that is not updated by any concurrent writes, such as appends or merges, during the execution of a streaming query. In this case, each microbatch of a stream-static join will use the most recent version of the static Delta table as of each microbatch, which means it will reflect any changes made to the static Delta table before the start of each microbatch.

155. Frage
A data architect is designing a Databricks solution to efficiently process data for different business requirements. In which scenario should a data engineer use a materialized view compared to a streaming table?

A. Implementing a CDC (Change Data Capture) pipeline that needs to detect and respond to database changes within seconds.
B. Processing high-volume, continuous clickstream data from a website to monitor user behavior in real-time.
C. Precomputing complex aggregations and joins from multiple large tables to accelerate BI dashboard performance.
D. Ingesting data from Apache Kafka topics with sub-second processing requirements for immediate alerting.

Antwort: C

Begründung:
Materialized views in Databricks are optimized for precomputing and caching results of complex SQL queries, joins, and aggregations. They store query outputs physically and automatically refresh on a schedule or incremental change basis, drastically improving BI dashboard performance and reducing compute costs.
Conversely, streaming tables are designed for real-time data ingestion and processing, enabling event-driven analytics and low-latency use cases.
Databricks documentation explicitly recommends materialized views for analytical workloads with periodic updates and streaming tables for continuously updating sources. Therefore, the correct choice is C, where complex aggregations from large tables benefit most from materialized precomputation for fast reporting.

156. Frage
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