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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Modelling | 6% | - Optimize table design and partitioning - Implement dimensional and relational models - Design Medallion Architecture |
| Topic 2: Ensuring Data Security and Compliance | 10% | - Secure data at rest and in transit - Ensure data privacy and compliance - Implement access control and permissions |
| Topic 3: Cost & Performance Optimisation | 13% | - Apply cost management best practices - Improve query and pipeline performance - Optimize compute and storage resources |
| Topic 4: Data Sharing and Federation | 5% | - Manage cross-platform data access - Use Delta Sharing for secure data sharing - Implement Lakehouse Federation |
| Topic 5: Debugging and Deploying | 10% | - Deploy using Asset Bundles, CLI, and APIs - Troubleshoot and debug pipelines - Implement CI/CD and DevOps practices |
| Topic 6: Data Governance | 7% | - Enforce data policies and standards - Manage data assets and metadata - Use Unity Catalog for governance |
| Topic 7: Data Ingestion & Acquisition | 7% | - Ingest data from diverse sources - Handle incremental and batch data loads - Use Auto Loader and structured streaming |
| Topic 8: 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 |
| Topic 9: Data Transformation, Cleansing, and Quality | 10% | - Implement schema evolution and management - Enforce data quality standards - Apply data cleansing and validation rules |
| Topic 10: Monitoring and Alerting | 10% | - Track data lineage and metrics - Monitor pipeline performance and health - Set up alerts and notifications |
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NEW QUESTION # 61
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?
Answer: B
Explanation:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from 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 # 62
A member of the data engineering team has submitted a short notebook that they wish to schedule as part of a larger data pipeline. Assume that the commands provided below produce the logically correct results when run as presented.
Which command should be removed from the notebook before scheduling it as a job?
Answer: C
Explanation:
When scheduling a Databricks notebook as a job, it's generally recommended to remove or modify commands that involve displaying output, such as using the display() function. Displaying data using display() is an interactive feature designed for exploration and visualization within the notebook interface and may not work well in a production job context.
The finalDF.explain() command, which provides the execution plan of the DataFrame transformations and actions, is often useful for debugging and optimizing queries. While it doesn't display interactive visualizations like display(), it can still be informative for understanding how Spark is executing the operations on your DataFrame.
NEW QUESTION # 63
A data engineer needs to implement column masking for a sensitive column in a Unity Catalog- managed table. The masking logic must dynamically check if users belong to specific groups defined in a separate table (group_access) that maps groups to allowed departments. Which approach should the engineer use to efficiently enforce this requirement?
Answer: A
Explanation:
Databricks Unity Catalog supports dynamic column masking, where masking logic can be implemented using SQL functions or UDFs that reference external mapping tables or metadata for context-aware access control.
By referencing the group_access table inside the masking function, the mask dynamically evaluates whether a requesting user belongs to an authorized group. If permitted, the original column value is returned; otherwise, a masked value (such as NULL or asterisks) is shown.
This method enables fine-grained, data-driven masking policies while maintaining a single authoritative access mapping source.
Hardcoding values (A) reduces flexibility, and row filters (D) apply to entire rows rather than specific columns. Therefore, C correctly aligns with Databricks best practices for dynamic masking.
NEW QUESTION # 64
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?
Answer: B
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 # 65
A data engineer is designing a secure data sharing strategy for their organization. The company needs to share sensitive customer analytics data with two different partners. Partner A uses Databricks with Unity Catalog enabled, while Partner B uses Apache Spark on AWS without Databricks. How should the company implement secure data sharing for these scenarios?
Answer: C
Explanation:
Databricks-to-Databricks sharing with Unity Catalog provides the most seamless and secure option for Partner A by enabling native governance, fine-grained access controls, and a no-token exchange model. For Partner B, which does not use Databricks, the open sharing protocol enables secure access from external Spark environments using standard authentication mechanisms such as bearer tokens or OIDC federation, while still enforcing sharing policies and protecting sensitive data.
NEW QUESTION # 66
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