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

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

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q32-Q37):

NEW QUESTION # 32
A data engineer has two DataFrames: orders_df (all orders) and customers_df (only active customers). The data engineer needs to filter out orders placed by inactive customers. Which operation should the data engineer use?

A. orders_df and customers_df inner join
B. customers_df left join with orders_df
C. orders_df and customers_df cross join
D. orders_df left join with customers_df

Answer: A

Explanation:
An inner join keeps only orders whose customer key matches a row in customers_df. Since customers_df contains only active customers, orders from inactive customers are excluded.

NEW QUESTION # 33
A data engineer needs to reduce the number of small files generated during streaming ingestion into Delta tables. Small files are causing performance issues during downstream queries. Which Delta Lake feature automatically compacts small files during writes?

A. autoCompact
B. OPTIMIZE
C. VACUUM
D. CACHE

Answer: A

NEW QUESTION # 34
A data engineer needs to create a table in Databricks using data from a CSV file at location /path/to/csv.
They run the following command:

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

A. USING CSV
B. USING DELTA
C. None of these lines of code are needed to successfully complete the task
D. FROM CSV
E. FROM "path/to/csv"

Answer: A

NEW QUESTION # 35
When using MERGE INTOin Delta Lake on Databricks, which clause allows you to atomically delete target rows that no longer match any source rows?

A. WHEN NOT MATCHED THEN DELETE
B. WHEN MATCHED BY SOURCE THEN UPDATE SET NULL
C. WHEN NOT MATCHED BY SOURCE THEN DELETE
D. WHEN MATCHED AND THEN DELETE

Answer: C

Explanation:
The clause that deletes target rows not present in the source is the one that triggers when a target row has no matching source row, enabling atomic removal of outdated records during the merge operation.

NEW QUESTION # 36
A data engineer and data analyst are working together on a data pipeline. The data engineer is working on the raw, bronze, and silver layers of the pipeline using Python, and the data analyst is working on the gold layer of the pipeline using SQL. The raw source of the pipeline is a streaming input. They now want to migrate their pipeline to use Delta Live Tables. Which change will need to be made to the pipeline when migrating to Delta Live Tables?

A. The pipeline will need to be written entirely in SQL.
B. The pipeline can have different notebook sources in SQL & Python.
C. The pipeline will need to be written entirely in Python.
D. The pipeline will need to use a batch source in place of a streaming source.

Answer: B

Explanation:
When migrating to Delta Live Tables (DLT), you can use both SQL and Python in the same pipeline. DLT supports multi-language notebooks, allowing data engineers to continue using Python for certain transformations and data analysts to use SQL for their specific requirements.

NEW QUESTION # 37
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