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

SectionObjectives
Data Ingestion and Transformation- Handle batch and streaming data pipelines
- Ingest data using Apache Spark and Databricks
- Transform and clean datasets using Spark SQL and DataFrame APIs
Data Modeling and Storage- Schema evolution and data partitioning strategies
- Delta Lake table design and optimization
- Design scalable data lakehouse architectures
Production Pipelines and Orchestration- Pipeline reliability and fault tolerance
- Automate ETL pipelines and scheduling
- Build and manage workflows using Databricks Jobs
Security, Governance, Monitoring, and Optimization- Cost optimization and performance tuning
- Implement Unity Catalog governance and access control
- Monitor and optimize Spark workloads

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Databricks Certified Professional Data Engineer Exam 認定 Databricks-Certified-Professional-Data-Engineer 試験問題 (Q94-Q99):

質問 # 94
Which of the following techniques structured streaming uses to create an end-to-end fault toler-ance?

正解：D

解説：
Explanation
The answer is Checkpointing and idempotent sinks
How does structured streaming achieves end to end fault tolerance:
*First, Structured Streaming uses checkpointing and write-ahead logs to record the offset range of data being processed during each trigger interval.
*Next, the streaming sinks are designed to be _idempotent_-that is, multiple writes of the same data (as identified by the offset) do not result in duplicates being written to the sink.
Taken together, replayable data sources and idempotent sinks allow Structured Streaming to en-sure end-to-end, exactly-once semantics under any failure condition.


質問 # 95
The data governance team is reviewing user for deleting records for compliance with GDPR. The following logic has been implemented to propagate deleted requests from the user_lookup table to the user aggregate table.

Assuming that user_id is a unique identifying key and that all users have requested deletion have been removed from the user_lookup table, which statement describes whether successfully executing the above logic guarantees that the records to be deleted from the user_aggregates table are no longer accessible and why?

正解：C

解説：
The DELETE operation in Delta Lake is ACID compliant, which means that once the operation is successful, the records are logically removed from the table. However, the underlying files that contained these records may still exist and be accessible via time travel to older versions of the table. To ensure that these records are physically removed and compliance with GDPR is maintained, a VACUUM command should be used to clean up these data files after a certain retention period. The VACUUM command will remove the files from the storage layer, and after this, the records will no longer be accessible.


質問 # 96
A Databricks job has been configured with 3 tasks, each of which is a Databricks notebook. Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a serial dependency on Task A.
If task A fails during a scheduled run, which statement describes the results of this run?

正解：B

解説：
Explanation
When a Databricks job runs multiple tasks with dependencies, the tasks are executed in a dependency graph. If a task fails, the downstream tasks that depend on it are skipped and marked as Upstream failed. However, the failed task may have already committed some changes to the Lakehouse before the failure occurred, and those changes are not rolled back automatically. Therefore, the job run may result in a partial update of the Lakehouse. To avoid this, you can use the transactional writes feature of Delta Lake to ensure that the changes are only committed when the entire job run succeeds. Alternatively, you can use the Run if condition to configure tasks to run even when some or all of their dependencies have failed, allowing your job to recover from failures and continue running. References:
transactional writes: https://docs.databricks.com/delta/delta-intro.html#transactional-writes Run if: https://docs.databricks.com/en/workflows/jobs/conditional-tasks.html


質問 # 97
Newly joined data analyst requested read-only access to tables, assuming you are owner/admin which section of Databricks platform is going to facilitate granting select access to the user

正解：E

解説：
Explanation
Anser is Data Explorer
https://docs.databricks.com/sql/user/data/index.html
Data explorer lets you easily explore and manage permissions on databases and tables. Users can view schema details, preview sample data, and see table details and properties. Administrators can view and change owners, and admins and data object owners can grant and revoke permissions.
To open data explorer, click Data in the sidebar.


質問 # 98
Although the Databricks Utilities Secrets module provides tools to store sensitive credentials and avoid accidentally displaying them in plain text users should still be careful with which credentials are stored here and which users have access to using these secrets.
Which statement describes a limitation of Databricks Secrets?

正解：A

解説：
This is the correct answer because it describes a limitation of Databricks Secrets. Databricks Secrets is a module that provides tools to store sensitive credentials and avoid accidentally displaying them in plain text.
Databricks Secrets allows creating secret scopes, which are collections of secrets that can be accessed by users or groups. Databricks Secrets also allows creating and managing secrets using the Databricks CLI or the Databricks REST API. However, a limitation of Databricks Secrets is that the Databricks REST API can be used to list secrets in plain text if the personal access token has proper credentials. Therefore, users should still be careful with which credentials are stored in Databricks Secrets and which users have access to using these secrets. Verified References: [Databricks Certified Data Engineer Professional], under "Databricks Workspace" section; Databricks Documentation, under "List secrets" section.


質問 # 99
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