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

Section	Weight	Objectives
Topic 1: Data Governance & Quality	11%	- Data quality and reliability 1. Schema enforcement and evolution 2. Data validation and quality checks - Unity Catalog implementation 1. Permissions and access control 2. Data governance model
Topic 2: Data Processing & Transformations	31%	- Query optimization and performance 1. Optimization strategies 2. Understanding query plans - Data transformation techniques 1. Aggregations, joins, and window functions 2. Complex data processing logic 3. DataFrame operations and transformations
Topic 3: Productionizing Data Pipelines	18%	- Deployment and CI/CD 1. Version control integration 2. Databricks Asset Bundles - Monitoring and troubleshooting 1. Pipeline reliability and recovery 2. Logging and error handling - Workflow orchestration 1. Lakeflow Jobs creation and management 2. Scheduling, triggers, and dependencies
Topic 4: Development and Ingestion	30%	- Data ingestion patterns and methods 1. Delta Lake basics and usage 2. Connecting to external data sources 3. Batch and streaming ingestion - Notebook development fundamentals 1. Data exploration and validation 2. Using PySpark and Spark SQL
Topic 5: Databricks Intelligence Platform	10%	- Platform architecture and core concepts 1. Workspace navigation and management 2. Data layout and optimization: partitioning, file sizing, caching 3. Compute options: clusters, SQL warehouses, serverless

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Databricks Certified Data Engineer Associate Exam Databricks-Certified-Data-Engineer-Associate Prüfungsfragen mit Lösungen (Q38-Q43):

38. Frage
A data provider must share a live Delta table with an external partner who uses an open-source Spark environment outside Databricks, without copying the data.
Which feature should be used?

A. A deep clone written to a partner-owned S3 bucket nightly
B. A materialized view exported as Parquet and delivered by SFTP
C. Delta Sharing with a recipient and share
D. A Unity Catalog external location with a storage credential granted to the partner

Antwort: C

Begründung:
Delta Sharing is an open protocol that lets a provider share live data from Unity Catalog with recipients on any platform that supports the protocol, with no data replication. Access is governed by shares and recipients, and the recipient always reads the current version of the data.

39. Frage
The Delta transaction log for the 'students' tables is shown using the 'DESCRIBE HISTORY students' command. A Data Engineer needs to query the table as it existed before the UPDATE operation listed in the log.

Which command should the Data Engineer use to achieve this? (Choose two.)

A. SELECT * FROM students FROM HISTORY VERSION AS OF 3
B. SELECT * FROM students@v4
C. SELECT * FROM students TIMESTAMP AS OF '2024-04-22T 14:32:47.000+00:00'
D. SELECT * FROM students TIMESTAMP AS OF '2024-04-22T 14:32:58.000+00:00'
E. SELECT * FROM students VERSION AS OF 5

Antwort: B,C

40. Frage
A data engineer needs to apply custom logic to string column city in table stores for a specific use case. In order to apply this custom logic at scale, the data engineer wants to create a SQL user-defined function (UDF).
Which of the following code blocks creates this SQL UDF?

Antwort: B

Begründung:
https://www.databricks.com/blog/2021/10/20/introducing-sql-user-defined-functions.html

41. Frage
A data engineer has multiple Unity Catalog-managed Delta tables that require regular maintenance. Currently, the engineer manually schedules OPTIMIZE and VACUUM jobs for each table, adjusting their frequency according to how often each table is queried and updated.
The engineer needs to eliminate this manual maintenance overhead and allow Databricks to determine automatically when and how to run these operations.
Which action should the engineer take?

A. Enable predictive optimization at the account, catalog, or schema level so Databricks automatically identifies and runs maintenance operations on managed tables.
B. Enable liquid clustering with CLUSTER BY on each table so it automatically compacts files and removes stale files on a schedule.
C. Set delta.autoOptimize.autoCompact and delta.autoOptimize.optimizeWrite table properties to trigger automatic VACUUM and file compaction.
D. Configure Auto Loader with schema evolution enabled so it automatically triggers OPTIMIZE and VACUUM after each ingestion batch.

Antwort: A

42. Frage
A Databricks workflow fails at the last stage due to an error in a notebook. This workflow runs daily. The data engineer fixes the mistake and wants to rerun the pipeline. This workflow is very costly and time- intensive to run.
Which action should the data engineer do in order to minimise downtime and cost?

A. Switch to another cluster
B. Re-run the entire workflow
C. Repair run
D. Restart the cluster

Antwort: C

43. Frage
......

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