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

Section	Weight	Objectives
Topic 1: Python for Data Engineering	10-15%	- Work with Spark APIs in Python - Use PySpark for data processing - Implement user-defined functions (UDFs)
Topic 2: Spark SQL and DataFrames	15-20%	- Handle null values and data quality - Write and execute Spark SQL queries - Join and union DataFrames - Aggregate and group data
Topic 3: Data Pipeline Architecture	15-20%	- Understand ELT vs ETL patterns - Design data pipelines for batch and streaming - Implement incremental data processing - Monitor and optimize pipeline performance
Topic 4: Apache Spark Data Processing Fundamentals	20-25%	- Work with structured data types (arrays, maps, structs) - Use Spark SQL for data processing - Create and use Spark DataFrames - Apply transformations and actions on DataFrames
Topic 5: Delta Lake Fundamentals	20-25%	- Write to and read from Delta tables - Understand ACID transactions and time travel - Explain Delta Lake features and benefits - Create and manage Delta tables
Topic 6: Lakehouse Platform Concepts	10-15%	- Explain data governance and security concepts - Understand the Lakehouse architecture and its benefits - Describe key Databricks Lakehouse platform components

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q278-Q283):

NEW QUESTION # 278
A data engineering team runs multiple daily batch jobs. The team needs to run the jobs on a fixed overnight schedule with consistent performance and controlled resource usage. The team wants to minimize operational overhead and reduce idle cluster costs.
Which compute option should the team use?

A. Serverless jobs compute
B. SQL warehouses
C. Classic jobs compute
D. Classic all-purpose compute

Answer: A

NEW QUESTION # 279
A Delta Live Table pipeline includes two datasets defined using STREAMING LIVE TABLE.
Three datasets are defined against Delta Lake table sources using LIVE TABLE.
The table is configured to run in Development mode using the Continuous Pipeline Mode.
Assuming previously unprocessed data exists and all definitions are valid, what is the expected outcome after clicking Start to update the pipeline?

A. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing.
B. All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated.
C. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist until the pipeline is shut down.
D. All datasets will be updated once and the pipeline will persist without any processing. The compute resources will persist but go unused.
E. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing.

Answer: E

NEW QUESTION # 280
Which of the following Structured Streaming queries is performing a hop from a Silver table to a Gold table?

Answer: E

Explanation:
The best practice is to use "Complete" as output mode instead of "append" when working with aggregated tables. Since gold layer is work final aggregated tables, the only option with output mode as complete is option E.

NEW QUESTION # 281
A data engineer wants to create a new table containing the names of customers that live in France.
They have written the following command:

A senior data engineer mentions that it is organization policy to include a table property indicating that the new table includes personally identifiable information (PII).
Which of the following lines of code fills in the above blank to successfully complete the task?

A. There is no way to indicate whether a table contains PII.
B. PII
C. COMMENT "Contains PII"
D. TBLPROPERTIES PII
E. "COMMENT PII"

Answer: C

Explanation:
In Databricks, when creating a table, you can add a comment to columns or the entire table to provide more information about the data it contains. In this case, since it's organization policy to indicate that the new table includes personally identifiable information (PII), option D is correct. The line of code would be added after defining the table structure and before closing with a semicolon. References: Data Engineer Associate Exam Guide, CREATE TABLE USING (Databricks SQL)

NEW QUESTION # 282
A company uses Delta Sharing to collaborate with partners across different cloud providers and geographic regions. What will result in additional costs due to cross-region or egress fees?

A. Accessing Delta Sharing data using a VPN within the same data center
B. Utilizing Delta Sharing for internal data analytics within a single cloud environment
C. Sharing data within the same cloud provider and region
D. Transferring data via Delta Sharing across clouds and across different geographic regions

Answer: D

Explanation:
Databricks documents that Delta Sharing does not require data replication , but cloud providers can still charge data egress fees when data is shared across clouds or across geographic regions . Databricks specifically states that sharing within the same region incurs no egress cost , while cross-cloud or cross- region transfers can create additional charges from the underlying cloud provider. That means option A is correct. Options B and C describe same-cloud or same-region use cases that do not trigger the cross-region
/cross-cloud egress pattern Databricks calls out. Option D is unrelated to the main documented billing driver for Delta Sharing costs. The important principle is that Delta Sharing itself is designed to avoid replication overhead, but the physical movement of data between cloud boundaries or regions can still result in vendor networking charges. Therefore, when sharing data with external partners in other regions or clouds, engineers should plan for possible egress costs and monitor them accordingly.
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NEW QUESTION # 283
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