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

Section	Weight	Objectives
Topic 1: Debugging and Deploying	10%	- Troubleshoot and debug pipelines - Deploy using Asset Bundles, CLI, and APIs - Implement CI/CD and DevOps practices
Topic 2: Data Transformation, Cleansing, and Quality	10%	- Apply data cleansing and validation rules - Enforce data quality standards - Implement schema evolution and management
Topic 3: Ensuring Data Security and Compliance	10%	- Implement access control and permissions - Secure data at rest and in transit - Ensure data privacy and compliance
Topic 4: Monitoring and Alerting	10%	- Monitor pipeline performance and health - Set up alerts and notifications - Track data lineage and metrics
Topic 5: Data Modelling	6%	- Implement dimensional and relational models - Design Medallion Architecture - Optimize table design and partitioning
Topic 6: Cost & Performance Optimisation	13%	- Optimize compute and storage resources - Apply cost management best practices - Improve query and pipeline performance
Topic 7: Data Ingestion & Acquisition	7%	- Use Auto Loader and structured streaming - Ingest data from diverse sources - Handle incremental and batch data loads
Topic 8: Data Governance	7%	- Use Unity Catalog for governance - Enforce data policies and standards - Manage data assets and metadata
Topic 9: Data Sharing and Federation	5%	- Use Delta Sharing for secure data sharing - Implement Lakehouse Federation - Manage cross-platform data access
Topic 10: Developing Code for Data Processing using Python and SQL	22%	- Write efficient and maintainable code - Use Databricks-specific libraries and APIs - Implement complex data processing logic

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q42-Q47):

NEW QUESTION # 42
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. ALTER TABLE table_name SET TAGS ('key1' = 'value1', 'key2' = 'value2');
B. COMMENT ON TABLE table_name TAGS ('key1' = 'value1', 'key2' = 'value2');
C. APPLY TAGS ON table_name VALUES ('key1' = 'value1', 'key2' = 'value2');
D. SET TAGS FOR table_name AS ('key1' = 'value1', 'key2' = 'value2');

Answer: A

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 # 43
A CHECK constraint has been successfully added to the Delta table named activity_details using the following logic:

A batch job is attempting to insert new records to the table, including a record where latitude =
45.50 and longitude = 212.67.
Which statement describes the outcome of this batch insert?

A. The write will fail completely because of the constraint violation and no records will be inserted into the target table.
B. The write will include all records in the target table; any violations will be indicated in the boolean column named valid_coordinates.
C. The write will insert all records except those that violate the table constraints; the violating records will be reported in a warning log.
D. The write will insert all records except those that violate the table constraints; the violating records will be recorded to a quarantine table.
E. The write will fail when the violating record is reached; any records previously processed will be recorded to the target table.

Answer: A

Explanation:
The CHECK constraint is used to ensure that the data inserted into the table meets the specified conditions. In this case, the CHECK constraint is used to ensure that the latitude and longitude values are within the specified range. If the data does not meet the specified conditions, the write operation will fail completely and no records will be inserted into the target table. This is because Delta Lake supports ACID transactions, which means that either all the data is written or none of it is written. Therefore, the batch insert will fail when it encounters a record that violates the Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from constraint, and the target table will not be updated.

NEW QUESTION # 44
A data governance team at a large enterprise is improving data discoverability across its organization. The team has hundreds of tables in their Databricks Lakehouse with thousands of columns that lack proper documentation. Many of these tables were created by different teams over several years, with missing context about column meanings and business logic. The data governance team needs to quickly generate comprehensive column descriptions for all existing tables to meet compliance requirements and improve data literacy across the organization. They want to leverage modern capabilities to automatically generate meaningful descriptions rather than manually documenting each column, which would take months to complete. Which approach should the team use in Databricks to automatically generate column comments and descriptions for existing tables?

A. Use Delta Lake's DESCRIBE HISTORY command to analyze table evolution and infer column purposes from historical changes.
B. Write custom PySpark code using df.describe() and df.schema to programmatically generate basic statistical descriptions for each column.
C. Use the DESCRIBE TABLE command to extract existing schema information and manually write descriptions based on column names and data types.
D. Navigate to the table in Databricks Catalog Explorer, select the table schema view, and use the AI Generate option which leverages artificial intelligence to automatically create meaningful column descriptions based on column names, data types, sample values, and data patterns.

Answer: D

Explanation:
The Catalog Explorer provides an AI-powered "AI Generate" capability that automatically creates intelligent column descriptions by analyzing column names, data types, sample values, and observed data patterns. This approach enables rapid, scalable documentation of existing tables, significantly improving data discoverability and compliance without manual effort.

NEW QUESTION # 45
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. All team members work in the same Databricks Git folder and perform Git operations (pull, push, commit, branch switching) directly in that shared folder.
C. 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.
D. Team members edit notebooks directly in the workspace's shared folder and periodically copy changes into a Git folder for version control.

Answer: C

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 # 46
When evaluating the Ganglia Metrics for a given cluster with 3 executor nodes, which indicator would signal proper utilization of the VM's resources?

A. CPU Utilization is around 75%
B. Network I/O never spikes
C. Bytes Received never exceeds 80 million bytes per second
D. Total Disk Space remains constant
E. The five Minute Load Average remains consistent/flat

Answer: A

Explanation:
In the context of cluster performance and resource utilization, a CPU utilization rate of around
75% is generally considered a good indicator of efficient resource usage. This level of CPU utilization suggests that the cluster is being effectively used without being overburdened or underutilized. A consistent 75% CPU utilization indicates that the cluster's processing power is being effectively employed while leaving some headroom to handle spikes in workload or additional tasks without maxing out the CPU, which could lead to performance degradation. A five Minute Load Average that remains consistent/flat (Option A) might indicate underutilization or a bottleneck elsewhere.
Monitoring network I/O (Options B and C) is important, but these metrics alone don't provide a complete picture of resource utilization efficiency.
Total Disk Space (Option D) remaining constant is not necessarily an indicator of proper resource utilization, as it's more related to storage rather than computational efficiency.

NEW QUESTION # 47
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