Databricks Databricks-Certified-Data-Engineer-Associate the latest exam practice questions and answers

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

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

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Databricks-Certified-Data-Engineer-Associate Latest Practice Questions, Test Databricks-Certified-Data-Engineer-Associate Testking

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q222-Q227):

NEW QUESTION # 222
A data architect has determined that a table of the following format is necessary:

Which of the following code blocks uses SQL DDL commands to create an empty Delta table in the above format regardless of whether a table already exists with this name?

A. Option D
B. Option C
C. Option B
D. Option A
E. Option E

Answer: E

Explanation:
Create a table using SQL | Databricks on AWS, Create a table using SQL - Azure Databricks, Delta Lake Quickstart - Azure Databricks

NEW QUESTION # 223
A data engineer is designing a Databricks Job that includes a Notebook task for data cleansing and a SQL Query task for generating a summary report. The summary report must only execute after the cleansing task completes successfully. How should the dependency be configured in the Lakeflow Jobs task graph?

A. Select the cleansing task as a 'Run if' dependency in the summary report task configuration.
B. Set both tasks to run on the same schedule with a five-minute start delay for the report.
C. Place both tasks in the same notebook and use a standard Python function call.
D. Configure the cleansing task to use an 'On Failure' trigger to start the summary report.

Answer: A

Explanation:
Configure the summary report task to depend on the cleansing task and use the default Run if: All succeeded condition. This ensures the SQL task runs only after the notebook task completes successfully.

NEW QUESTION # 224
A data engineer needs to apply custom logic to string column city in table stores for a specific use case. In order to apply this custom logic at scale, the data engineer wants to create a SQL user-defined function (UDF).
Which of the following code blocks creates this SQL UDF?

Answer: E

Explanation:
https://www.databricks.com/blog/2021/10/20/introducing-sql-user-defined-functions.html

NEW QUESTION # 225
A data engineer is attempting to drop a Spark SQL table my_table and runs the following command:
DROP TABLE IF EXISTS my_table;
After running this command, the engineer notices that the data files and metadata files have been deleted from the file system.
Which of the following describes why all of these files were deleted?

A. The table's data was smaller than 10 GB
B. The table was external
C. The table did not have a location
D. The table's data was larger than 10 GB
E. The table was managed

Answer: E

Explanation:
Explanation
managed tables files and metadata are managed by metastore and will be deleted when the table is dropped .
while external tables the metadata is stored in a external location. hence when a external table is dropped you clear off only the metadata and the files (data) remain.

NEW QUESTION # 226
A data engineer is writing a script that is meant to ingest new data from cloud storage. In the event of the Schema change, the ingestion should fail. It should fail until the changes downstream source can be found and verified as intended changes.
Which command will meet the requirements?

A. addNewColumns
B. failOnNewColumns
C. rescue
D. none

Answer: B

NEW QUESTION # 227
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