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

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

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q92-Q97):

NEW QUESTION # 92
A data engineer needs to create a table in Databricks using data from their organization's existing SQLite database.
They run the following command:

Which of the following lines of code fills in the above blank to successfully complete the task?

A. sqlite
B. autoloader
C. DELTA
D. org.apache.spark.sql.sqlite
E. org.apache.spark.sql.jdbc

Answer: E

NEW QUESTION # 93
An organization is building a data lakehouse and needs to ingest data from multiple sources into Unity Catalog-managed tables:
Source 1: Salesforce (50+ objects, frequent schema changes, OAuth
authentication required)
Source 2: On-premises SQL server database (100+ tables, CDC enabled,
requires network connectivity)
Source 3: Daily JSON files landing in Azure Data Lake Storage Gen2
The organization wants all ingested data to be governed by Unity Catalog, to minimize data engineering effort for schema changes, and to use serverless wherever possible to reduce operational overhead.
Which ingestion strategy meets these requirements?

A. Use Auto Loader for all three sources with custom Python code to handle Salesforce API calls and SQL server CDC extraction, then write to Unity Catalog tables.
B. Use partner connectors (Fivetran) for all three sources via Partner Connect, as they provide the most comprehensive integration with Unity Catalog and automated schema evolution.
C. Use Lakeflow Connect managed connectors for all three sources, as they provide consistent serverless execution and Unity Catalog integration across all data source types.
D. Use Lakeflow Connect managed connectors for Salesforce and SQL Server, and Auto Loader for ADLS files, all within Lakeflow Spark Declarative Pipelines.

Answer: D

Explanation:
Lakeflow Connect provides managed, serverless connectors for Salesforce and SQL Server CDC, including Unity Catalog governance and automated schema evolution. Auto Loader is the appropriate managed mechanism for incrementally ingesting JSON files from ADLS Gen2 and can run within Lakeflow Spark Declarative Pipelines.

NEW QUESTION # 94
A data engineer must apply a daily batch of customer records to a Delta table so that existing customers are updated, new customers are inserted, and no duplicates are created.
Which statement accomplishes this in a single atomic operation?

A. MERGE INTO customers t USING updates s ON t.customer_id = s.customer_id WHEN MATCHED THEN UPDATE SET * WHEN NOT MATCHED THEN INSERT *
B. INSERT INTO customers SELECT * FROM updates
C. CREATE OR REPLACE TABLE customers AS SELECT * FROM updates
D. INSERT OVERWRITE TABLE customers SELECT * FROM updates

Answer: A

Explanation:
MERGE INTO performs upserts in a single ACID transaction, updating matched rows and inserting unmatched rows. INSERT INTO would create duplicates, and both INSERT OVERWRITE and CREATE OR REPLACE would discard existing records not present in the daily batch.

NEW QUESTION # 95
In which of the following scenarios should a data engineer select a Task in the Depends On field of a new Databricks Job Task?

A. When another task needs to fail before the new task begins
B. When another task needs to successfully complete before the new task begins
C. When another task has the same dependency libraries as the new task
D. When another task needs to use as little compute resources as possible
E. When another task needs to be replaced by the new task

Answer: B

Explanation:
A data engineer can create a multi-task job in Databricks that consists of multiple tasks that run in a specific order. Each task can have one or more dependencies, which are other tasks that must run before the current task. The Depends On field of a new Databricks Job Task allows the data engineer to specify the dependencies of the task. The data engineer should select a task in the Depends On field when they want the new task to run only after the selected task has successfully completed. This can help the data engineer to create a logical sequence of tasks that depend on each other's outputs or results. For example, a data engineer can create a multi-task job that consists of the following tasks:
Task A: Ingest data from a source using Auto Loader
Task B: Transform the data using Spark SQL
Task C: Write the data to a Delta Lake table
Task D: Analyze the data using Spark ML
Task E: Visualize the data using Databricks SQL
In this case, the data engineer can set the dependencies of each task as follows:
Task A: No dependencies
Task B: Depends on Task A
Task C: Depends on Task B
Task D: Depends on Task C
Task E: Depends on Task D
This way, the data engineer can ensure that each task runs only after the previous task has successfully completed, and the data flows smoothly from ingestion to visualization.
The other options are incorrect because they do not describe valid scenarios for selecting a task in the Depends On field. The Depends On field does not affect the following aspects of a task:
Whether the task needs to be replaced by another task
Whether the task needs to fail before another task begins
Whether the task has the same dependency libraries as another task
Whether the task needs to use as little compute resources as possible Reference: Create a multi-task job, Run tasks conditionally in a Databricks job, Databricks Jobs.

NEW QUESTION # 96
A data engineer has configured a Structured Streaming job to read from a table, manipulate the data, and then perform a streaming write into a new table.

The code block used by the data engineer is below:
Which line of code should the data engineer use to fill in the blank if the data engineer only wants the query to execute a micro-batch to process data every 5 seconds?

A. trigger(continuous= " 5 seconds " )
B. trigger( " 5 seconds " )
C. trigger(once= " 5 seconds " )
D. trigger(processingTime= " 5 seconds " )

Answer: D

NEW QUESTION # 97
......

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