100% Pass Quiz 2026 The Best Databricks Databricks-Certified-Data-Engineer-Associate Valid Exam Syllabus

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

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

>> Databricks-Certified-Data-Engineer-Associate Valid Exam Syllabus <<

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q40-Q45):

NEW QUESTION # 40
Which Databricks feature can be used to check the data sources and tables used in a workspace?

A. Use the lineage feature to visualize a graph that shows all dependencies, including where the table is used in notebooks, other tables, and reports.
B. Use the lineage feature to visualize a graph that highlights where the table is used only in notebooks.
C. Use the audit logs feature to review user activities, such as who queried or modified a table, instead of visualizing dependencies between data objects.
D. Use the lineage feature to visualize a graph that highlights where the table is used only in reports.

Answer: A

Explanation:
The lineage feature provides a visual graph of end-to-end data dependencies, showing data sources, tables, notebooks, and downstream consumers such as reports, enabling visibility into how data is produced and used across the workspace.

NEW QUESTION # 41
A data engineer has a Python notebook in Databricks, but they need to use SQL to accomplish a specific task within a cell. They still want all of the other cells to use Python without making any changes to those cells.
Which of the following describes how the data engineer can use SQL within a cell of their Python notebook?

A. They can attach the cell to a SQL endpoint rather than a Databricks cluster
B. They can simply write SQL syntax in the cell
C. They can change the default language of the notebook to SQL
D. It is not possible to use SQL in a Python notebook
E. They can add %sql to the first line of the cell

Answer: E

Explanation:
In Databricks, you can use different languages within the same notebook by using magic commands. Magic commands are special commands that start with a percentage sign (%) and allow you to change the behavior of the cell. To use SQL within a cell of a Python notebook, you can add %sql to the first line of the cell. This will tell Databricks to interpret the rest of the cell as SQL code and execute it against the default database. You can also specify a different database by using the USE statement. The result of the SQL query will be displayed as a table or a chart, depending on the output mode. You can also assign the result to a Python variable by using the -o option. For example, %sql -o df SELECT * FROM my_table will run the SQL query and store the result as a pandas DataFrame in the Python variable df. Option A is incorrect, as it is possible to use SQL in a Python notebook using magic commands. Option B is incorrect, as attaching the cell to a SQL endpoint is not necessary and will not change the language of the cell. Option C is incorrect, as simply writing SQL syntax in the cell will result in a syntax error, as the cell will still be interpreted as Python code. Option E is incorrect, as changing the default language of the notebook to SQL will affect all the cells, not just one. Reference: Use SQL in Notebooks - Knowledge Base - Noteable, [SQL magic commands - Databricks], [Databricks SQL Guide - Databricks]

NEW QUESTION # 42
What is the functionality of AutoLoader in Databricks?

A. Auto Loader automatically ingests and processes new files from cloud storage, handling batch data with support for schema evolution.
B. Auto Loader automatically ingests and processes new files from cloud storage, handling only streaming data with no support for schema evolution.
C. Auto Loader automatically ingests and processes new files from cloud storage, handling both batch and streaming data with support for schema evolution.
D. Auto Loader automatically ingests and processes new files from cloud storage, handling batch and streaming data with no support for schema evolution.

Answer: C

Explanation:
Auto Loader in Databricks automatically ingests new files from cloud storage, supports both batch and streaming ingestion, and handles schema evolution, making it ideal for scalable pipelines.

NEW QUESTION # 43
A data engineer needs to support a Unity Catalog Delta table catalog.schema.sales that is currently registered as an external table. New requirements state:
- Automatic file cleanup after a drop is handled by the platform.
- Minimal downtime during migration while keeping the same table name,
permissions, and history.
- Enforced name-based access through Unity Catalog (no path-based
access).
Which action should the data engineer take to meet the requirements?

A. Run DROP TABLE catalog.schema.sales PURGE to delete files and meet goals
B. Run ALTER TABLE catalog.schema.sales SET MANAGED to convert and meet the goals
C. Create a new managed table with CTAS and migrate workloads to that table today
D. Run ALTER TABLE catalog.schema.sales SET LOCATION to switch external paths now

Answer: B

Explanation:
ALTER TABLE catalog.schema.sales SET MANAGED converts the external Delta table in place while retaining its name, permissions, configuration, views, and history, with minimal downtime.
As a managed table, Unity Catalog controls its storage lifecycle, including cleanup after deletion, and workloads should access it by its three-part table name.

NEW QUESTION # 44
A data engineer at a company that uses Databricks with Unity Catalog needs to share a collection of tables with an external partner who also uses a Databricks workspace enabled for Unity Catalog. The data engineer decides to use Delta Sharing to accomplish this.
What is the first piece of information the data engineer should request from the external partner to set up Delta Sharing?

A. Their Databricks account password
B. The sharing identifier of their Unity Catalog metastore
C. The name of their Databricks cluster
D. The IP address of their Databricks workspace

Answer: B

NEW QUESTION # 45
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