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

Section	Objectives
Topic 1: Data Governance and Quality	- Data access control and governance - Unity Catalog basics - Data quality concepts and management
Topic 2: Data Processing and Transformations	- User-defined functions (UDFs) - Delta Lake fundamentals (tables, transactions, optimization) - PySpark DataFrame transformations - Apache Spark SQL operations (joins, aggregations, filtering)
Topic 3: Productionizing Data Pipelines	- Databricks Workflows / Jobs orchestration - Scheduling and monitoring jobs - Pipeline deployment and operationalization
Topic 4: Data Ingestion and ELT Development	- Data ingestion using Spark SQL and PySpark - ETL patterns and transformations - Handling structured and semi-structured data
Topic 5: Databricks Lakehouse Platform Fundamentals	- Clusters, notebooks, and basic Databricks environment usage - Workspace, architecture, and core platform concepts

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q108-Q113):

NEW QUESTION # 108
A data engineer has written a function in a Databricks Notebook to calculate the population of bacteria in a given medium.

Analysts use this function in the notebook and sometimes provide input arguments of the wrong data type, which can cause errors during execution.
Which Databricks feature will help the data engineer quickly identify if an incorrect data type has been provided as input?

A. The Databricks debugger enables the use of a variable explorer to see at a glance the value of the variables.
B. The Databricks debugger enables breakpoints that will raise an error if the wrong data type is submitted
C. The Spark User interface has a debug tab that contains the variables that are used in this session.
D. The Data Engineer should add print statements to find out what the variable is.

Answer: A

NEW QUESTION # 109
An organization has data stored across multiple external systems, including MySQL, Amazon Redshift, and Google BigQuery. The data engineer wants to perform analytics without ingesting directly into Databricks, ensuring unified governance and minimizing data duplication. Which feature of Databricks enables querying these external data sources while maintaining centralized governance?

A. Delta Lake
B. Databricks Connect
C. MLflow
D. Lakehouse Federation

Answer: D

Explanation:
Lakehouse Federation allows Databricks to query external data sources like MySQL, Redshift, and BigQuery without moving or duplicating data, while maintaining centralized governance through Unity Catalog.

NEW QUESTION # 110
A data engineer has realized that the data files associated with a Delta table are incredibly small.
They want to compact the small files to form larger files to improve performance.
Which of the following keywords can be used to compact the small files?

A. REDUCE
B. COMPACTION
C. OPTIMIZE
D. VACUUM
E. REPARTITION

Answer: C

NEW QUESTION # 111
Which of the following is hosted completely in the control plane of the classic Databricks architecture?

A. JDBC data source
B. Databricks web application
C. Driver node
D. Databricks Filesystem
E. Worker node

Answer: B

Explanation:
The Databricks web application is the user interface that allows you to create and manage workspaces, clusters, notebooks, jobs, and other resources. It is hosted completely in the control plane of the classic Databricks architecture, which includes the backend services that Databricks manages in your Databricks account. The other options are part of the compute plane, which is where your data is processed by compute resources such as clusters. The compute plane is in your own cloud account and network. Reference: Databricks architecture overview, Security and Trust Center

NEW QUESTION # 112
A data engineer is using the following code block as part of a batch ingestion pipeline to read from a composable table:

Which of the following changes needs to be made so this code block will work when the transactions table is a stream source?

A. Replace spark.read with spark.readStream
B. Replace format("delta") with format("stream")
C. Replace predict with a stream-friendly prediction function
D. Replace schema(schema) with option ("maxFilesPerTrigger", 1)
E. Replace "transactions" with the path to the location of the Delta table

Answer: A

Explanation:
To read from a stream source, the data engineer needs to use the spark.readStream method instead of the spark.
read method. The spark.readStream method returns a DataStreamReader object that can be used to specify the details of the input source, such as the format, the schema, the path, and the options. The spark.read method is only suitable for batch processing, not streaming processing. The other changes are not necessary or correct for reading from a stream source. References: Structured Streaming Programming Guide, Read a stream, Databricks Data Sources

NEW QUESTION # 113
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