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

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Databricks Certified Data Engineer Professional Sample Questions (Q72-Q77):

NEW QUESTION # 72
Which of the following technologies can be used to identify key areas of text when parsing Spark Driver log4j output?

Answer: C

Explanation:
Regex, or regular expressions, are a powerful way of matching patterns in text. They can be used to identify key areas of text when parsing Spark Driver log4j output, such as the log level, the timestamp, the thread name, the class name, the method name, and the message. Regex can be applied in various languages and frameworks, such as Scala, Python, Java, Spark SQL, and Databricks notebooks.


NEW QUESTION # 73
Which statement describes the default execution mode for Databricks Auto Loader?

Answer: B

Explanation:
Databricks Auto Loader simplifies and automates the process of loading data into Delta Lake.
The default execution mode of the Auto Loader identifies new files by listing the input directory. It incrementally and idempotently loads these new files into the target Delta Lake table. This approach ensures that files are not missed and are processed exactly once, avoiding data duplication. The other options describe different mechanisms or integrations that are not part of the default behavior of the Auto Loader.


NEW QUESTION # 74
A data engineer is optimizing a MERGE operation on an 800GB UC-managed table that experiences frequent updates and deletions. Which two actions should the engineer prioritize to improve MERGE performance? (Choose two.)

Answer: B,D

Explanation:
Liquid clustering on the merge join keys improves data locality and reduces the amount of data scanned during MERGE operations, which is especially effective for large, frequently updated tables. Enabling deletion vectors avoids rewriting entire Parquet files for updates and deletes, significantly reducing I/O and improving MERGE performance on Unity Catalog-managed tables.


NEW QUESTION # 75
A query is taking too long to run. After investigating the Spark UI, the data engineer discovered a significant amount of disk spill. The compute instance being used has a core-to-memory ratio of
1:2. What are the two steps the data engineer should take to minimize spillage? (Choose two.)

Answer: A,B

Explanation:
Databricks recommends addressing disk spilling--which occurs when Spark tasks run out of memory--by increasing memory per core and controlling partition size. Selecting an instance type with a higher memory-to-core ratio (A) provides each task with more available RAM, directly reducing the chance of spilling to disk. Additionally, reducing spark.sql.files.maxPartitionBytes (D) creates smaller partitions, preventing any single task from holding too much data in memory.
Increasing partition size (C) or disk capacity (B) does not solve memory bottlenecks, and bandwidth (E) affects network I/O, not spill behavior. Therefore, the correct actions are A and D.


NEW QUESTION # 76
A developer has successfully configured their credentials for Databricks Repos and cloned a remote Git repository. They do not have privileges to make changes to the main branch, which is the only branch currently visible in their workspace. Which approach allows this user to share their code updates without the risk of overwriting the work of their teammates?

Answer: C

Explanation:
In Databricks Repos, when a user does not have privileges to make changes directly to the main branch of a cloned remote Git repository, the recommended approach is to create a new branch within the Databricks workspace. The developer can then make changes in this new branch, commit those changes, and push the new branch to the remote Git repository. This workflow allows for isolated development without affecting the main branch, enabling the developer to propose changes via a pull request from the new branch to the main branch in the remote repository. This method adheres to common Git collaboration workflows, fostering code review and collaboration while ensuring the integrity of the main branch.


NEW QUESTION # 77
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