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

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

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Databricks Certified Data Engineer Professional Sample Questions (Q194-Q199):

NEW QUESTION # 194
Which statement describes Delta Lake optimized writes?

Answer: D

Explanation:
Delta Lake optimized writes involve a shuffle operation before writing out data to the Delta table.
The shuffle operation groups data by partition keys, which can lead to a reduction in the number of output files and potentially larger files, instead of multiple smaller files. This approach can significantly reduce the total number of files in the table, improve read performance by reducing the metadata overhead, and optimize the table storage layout, especially for workloads with many small files.


NEW QUESTION # 195
A data engineer is masking a column containing email addresses. The goal is to produce output strings of identical length for all rows, while generating different outputs for different email values.
Which SQL function should be used to achieve this?

Answer: D

Explanation:
The hash() function in Databricks SQL returns a deterministic fixed-length integer (or hexadecimal string) derived from the input. When applied to sensitive identifiers like email addresses, it produces a unique value for each distinct input while ensuring uniform output size, making it suitable for anonymization where referential consistency is required.
Functions like mask() perform pattern-based substitutions that change string lengths, and sha1() or sha2() produce long hexadecimal strings of varying lengths (depending on hash size), which may not match requirements for fixed-length masking.
Therefore, the correct choice for fixed-length, deterministic pseudonymization of email addresses is hash(email), as it maintains analytical usability while anonymizing sensitive data.


NEW QUESTION # 196
A data engineer is brining an existing production Databricks job under asset bundle management and wants to ensure that:
- The job's current configuration is captured as YAML, and all
referenced files are included in their bundle project.
- Future changes to the bundle's YAML will update the existing job in-
place (not create a new job)
How should the data engineer successfully move the production job under asset bundle management?

Answer: C

Explanation:
Generating the bundle from the existing job captures the full job configuration as YAML and pulls in all referenced files into the bundle project. Binding the generated job resource to the existing Databricks job establishes a persistent link, ensuring that future bundle deployments update the same production job in place rather than creating a new one.


NEW QUESTION # 197
A data architect is implementing Delta Sharing as part of their data governance strategy to enable secure data collaboration with external partners and internal business units. The architect must establish a permission framework that allows designated data stewards to create shares for their respective domains while maintaining security boundaries and audit compliance. Which specific permissions and roles must be assigned to enable users to create, configure, and manage Delta Shares while maintaining proper security governance and access controls?

Answer: A

Explanation:
Creating and managing Delta Shares requires elevated governance privileges at the metastore level. Assigning users as metastore admins or granting them the CREATE SHARE privilege allows designated data stewards to create and configure shares within defined security boundaries, while preserving centralized auditability and access control.


NEW QUESTION # 198
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?

Answer: A


NEW QUESTION # 199
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