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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. Data governance model 2. Permissions and access control
Topic 2: Productionizing Data Pipelines	18%	- Deployment and CI/CD 1. Databricks Asset Bundles 2. Version control integration - Workflow orchestration 1. Scheduling, triggers, and dependencies 2. Lakeflow Jobs creation and management - Monitoring and troubleshooting 1. Logging and error handling 2. Pipeline reliability and recovery
Topic 3: Databricks Intelligence Platform	10%	- Platform architecture and core concepts 1. Data layout and optimization: partitioning, file sizing, caching 2. Workspace navigation and management 3. Compute options: clusters, SQL warehouses, serverless
Topic 4: 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. DataFrame operations and transformations 3. Complex data processing logic
Topic 5: Development and Ingestion	30%	- Data ingestion patterns and methods 1. Connecting to external data sources 2. Delta Lake basics and usage 3. Batch and streaming ingestion - Notebook development fundamentals 1. Using PySpark and Spark SQL 2. Data exploration and validation

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q299-Q304):

NEW QUESTION # 299
Which of the following tools is used by Auto Loader process data incrementally?

A. Checkpointing
B. Spark Structured Streaming
C. Data Explorer
D. Databricks SQL
E. Unity Catalog

Answer: B

Explanation:
Auto Loader provides a Structured Streaming source called cloudFiles that can process new data files as they arrive in cloud storage without any additional setup. Auto Loader uses a scalable key-value store to track ingestion progress and ensure exactly-once semantics. Auto Loader can ingest various file formats and load them into Delta Lake tables. Auto Loader is recommended for incremental data ingestion with Delta Live Tables, which extends the functionality of Structured Streaming and allows you to write declarative Python or SQL code to deploy a production-quality data pipeline. References: What is Auto Loader?, What is Auto Loader? | Databricks on AWS, Solved: How does Auto Loader ingest data? - Databricks - 5629

NEW QUESTION # 300
A data engineer wants to delegate day-to-day permission management for the schema main.marketing to the mkt-admins group, without making them workspace admins. They should be able to grant and revoke privileges for other users on objects within that schema.
Which approach aligns with Unity Catalog's ownership and privilege model?

A. Grant USE SCHEMA on main.marketing, and MODIFY on all tables to mkt-admins, which enables the management of grants within the schema.
B. Grant MANAGE permissions on the metastore to mkt-admins, which allows managing privileges for all schemas and tables globally.
C. Make mkt-admins a workspace-level admins group, then assign SELECT on main.marketing to allow privilege delegation.
D. Transfer ownership of the schema main.marketing to mkt-admins; owners can manage privileges on the schema and its contained objects.

Answer: D

Explanation:
In Unity Catalog, ownership is the primary mechanism for delegating full administrative control of a securable object. The owner of a schema can grant and revoke privileges on that schema and on all objects contained within it (such as tables and views), without needing to be a workspace admin or metastore admin. Transferring ownership of main.marketing to the mkt-admins group therefore aligns precisely with the requirement to delegate day-to-day permission management at the schema scope. Granting MANAGE at the metastore level (option B) would be overly permissive, enabling global administration across all schemas and objects, which violates the principle of least privilege. Simply granting USE SCHEMA and MODIFY (option C) does not confer the ability to manage grants for other users. Making the group workspace admins (option D) unnecessarily elevates privileges beyond Unity Catalog's data governance model. Unity Catalog documentation emphasizes using ownership transfer to delegate administrative responsibilities at the appropriate scope while maintaining centralized governance in Databricks.
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NEW QUESTION # 301
Which of the following Structured Streaming queries is performing a hop from a Silver table to a Gold table?

Answer: D

NEW QUESTION # 302
A data engineer is designing a Gold-layer table for a BI team that needs to query complex aggregations. The data should be precomputed to provide low latency for dashboard users, while the underlying source data changes only once every few hours.
Which object type should be implemented in Unity Catalog?

A. External table
B. Streaming table
C. Materialized view
D. View

Answer: C

NEW QUESTION # 303
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 TIMESTAMP AS OF '2024-04-22T 14:32:58.000+00:00'
C. SELECT * FROM students VERSION AS OF 5
D. SELECT * FROM students TIMESTAMP AS OF '2024-04-22T 14:32:47.000+00:00'
E. SELECT * FROM students@v4

Answer: D,E

NEW QUESTION # 304
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