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

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

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Databricks Certified Data Engineer Professional Sample Questions (Q145-Q150):

NEW QUESTION # 145
A data company uses Databricks Unity Catalog and has multiple enterprise data sources, including PostgreSQL, Snowflake, and SQL Server. The central data platform team wants to configure Lakehouse Federation so analysts can query external tables directly in Databricks using Databricks SQL, without duplicating data. Which steps are necessary to configure Lakehouse Federation in a secure and governed manner?

A. Configure connections and foreign catalog in Unity Catalog, then grant access to foreign catalogs, schemas, and tables using Unity Catalog permissions.
B. Use Partner Connect to create linked datasets, and apply table ACLs at the source system to govern access through Databricks.
C. Create external locations and storage credentials to connect to each database, then register foreign tables in Unity Catalog.
D. Mirror the external datasets into Delta Lake using Auto Loader, and govern them using Data Lineage and System Tables.

Answer: A

Explanation:
Lakehouse Federation is configured by defining secure connections to external data sources and registering them as foreign catalogs in Unity Catalog. Access is then governed using Unity Catalog permissions at the catalog, schema, and table levels, enabling analysts to query external tables securely without data duplication.

NEW QUESTION # 146
A Delta Lake table representing metadata about content from user has the following schema:
user_id LONG, post_text STRING, post_id STRING, longitude FLOAT, latitude FLOAT, post_time TIMESTAMP, date DATE Based on the above schema, which column is a good candidate for partitioning the Delta Table?

A. latitude
B. User_id
C. Post_id
D. Date
E. Post_time

Answer: D

Explanation:
Partitioning a Delta Lake table improves query performance by organizing data into partitions based on the values of a column. In the given schema, the date column is a good candidate for partitioning for several reasons:
Time-Based Queries: If queries frequently filter or group by date, partitioning by the date column can significantly improve performance by limiting the amount of data scanned. Granularity: The date column likely has a granularity that leads to a reasonable number of partitions (not too many and not too few). This balance is important for optimizing both read and write performance.
Data Skew: Other columns like post_id or user_id might lead to uneven partition sizes (data skew), which can negatively impact performance.
Partitioning by post_time could also be considered, but typically date is preferred due to its more manageable granularity.

NEW QUESTION # 147
When a new Databricks project starts, the central IP team provisions the required infrastructure using Terraform and a Service Principal. This includes creating a Databricks workspace, a Unity Catalog linked to an External Location, and a Databricks group containing all project team members. Project teams must store all assets - e.g., tables and volumes, as Managed assets in Unity Catalog. This model hides infrastructure complexity while giving teams autonomy within their catalog. They can create and manage schemas, tables, volumes, and related objects but cannot rename, delete, or change catalog permissions, those remain under IT's control. Which rights should the project group be granted to enable this model?

A. The group should be made OWNER of the catalog.
B. The group needs to have USE CATALOG and USE SCHEMA on the catalog.
C. The group needs to have ALL PRIVILEGES on the catalog.
D. The group needs to have ALL PRIVILEGES and the MANAGE on the catalog.

Answer: B

Explanation:
Granting the project group USE CATALOG (and USE SCHEMA on existing schemas) allows team members to work within the catalog without giving them ownership or administrative control.
This enables teams to create and manage schemas, tables, and volumes inside the catalog while preventing them from renaming, deleting, or modifying catalog-level permissions, which remain under central IT governance.

NEW QUESTION # 148
A data engineering team needs to implement a tagging system for their tables as part of an automated ETL process, and needs to apply tags programmatically to tables in Unity Catalog.
Which SQL command adds tags to a table programmatically?

A. SET TAGS FOR table_name AS ('key1' = 'value1', 'key2' = 'value2');
B. ALTER TABLE table_name SET TAGS ('key1' = 'value1', 'key2' = 'value2');
C. COMMENT ON TABLE table_name TAGS ('key1' = 'value1', 'key2' = 'value2');
D. APPLY TAGS ON table_name VALUES ('key1' = 'value1', 'key2' = 'value2');

Answer: B

Explanation:
Unity Catalog supports programmatic tagging through the ALTER TABLE statement. Using SET TAGS allows tags to be added or updated directly on a table as part of automated ETL workflows, making it the correct and supported SQL command for managing table tags.

NEW QUESTION # 149
A data engineer has created a transactions Delta table on Databricks that should be used by the analytics team. The analytics team wants to use the table with another tool that requires Apache Iceberg format. What should the data engineer do?

A. Require the analytics team to use a tool that supports Delta table.
B. Create an Iceberg copy of the transactions Delta table which can be used by the analytics team.
C. Convert the transactions Delta table to Iceberg and enable uniform so that the table can be read as a Delta table.
D. Enable uniform on the transactions table to 'iceberg' so that the table can be read as an Iceberg table.

Answer: C

Explanation:
Delta Lake introduced Delta Universal Format (Delta UniForm), which allows seamless interoperability between Delta Lake and Apache Iceberg. This means a Delta table can be converted into an Iceberg table while maintaining Delta capabilities.

NEW QUESTION # 150
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