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

SectionWeightObjectives
Topic 1: Security and Governance~10%- Implement row-level security, column masking, and compliance
- Manage Unity Catalog permissions and ACLs
Topic 2: Developing Code for Data Processing using Python and SQL~22%- Build pipelines with Lakeflow Spark Declarative Pipelines and Auto Loader
- Manage dependencies, libraries, and UDFs
- Implement scalable Python/SQL code and project structures
Topic 3: Data Modeling~10%- Design scalable Delta Lake schemas and clustering
- Apply dimensional modeling techniques
Topic 4: Streaming Workloads and Change Data Capture~11%- Apply AUTO CDC APIs and exactly-once semantics
- Implement reliable streaming pipelines
Topic 5: Data Transformation, Cleansing, and Quality~12%- Apply advanced Spark transformations
- Enforce data quality and quarantine bad data
Topic 6: Data Sharing and Federation~8%- Configure Delta Sharing and Lakehouse Federation
Topic 7: Monitoring, Logging, and Troubleshooting~8%- Diagnose common pipeline and job failures
- Use Spark UI, Query Profiler, and system tables
Topic 8: CI/CD, Testing, and Deployment~6%- Deploy with Declarative Automation Bundles, CLI, and REST API
- Implement testing and deployment pipelines
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 (Q227-Q232):

NEW QUESTION # 227
A junior data engineer has configured a workload that posts the following JSON to the Databricks REST API endpoint 2.0/jobs/create.

Assuming that all configurations and referenced resources are available, which statement describes the result of executing this workload three times?

Answer: B

Explanation:
Databricks jobs create will create a new job with the same name each time it is run.
In order to overwrite the extsting job you need to run databricks jobs reset


NEW QUESTION # 228
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: D

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 # 229
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:
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 # 230
When monitoring a complex workload, being able to see the query plan is critical to understanding what the workload is doing. Where can the visualization of the query plan be found?

Answer: D

Explanation:
The Spark UI provides detailed visibility into how queries are executed. The SQL/DataFrame tab displays the logical and physical query plans, allowing engineers to visualize execution details and understand how the workload is processed across stages and operators.


NEW QUESTION # 231
Incorporating unit tests into a PySpark application requires upfront attention to the design of your jobs, or a potentially significant refactoring of existing code.
Which statement describes a main benefit that offset this additional effort?

Answer: C


NEW QUESTION # 232
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