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

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

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q242-Q247):

NEW QUESTION # 242
A data engineer has a bundle with a job resource keyed as etl_job in databricks.yml. After running 'databricks bundle deploy', the job appears in the workspace but has not executed. Which command triggers execution of the deployed job?

A. databricks bundle deploy --run
B. databricks bundle execute etl_job
C. databricks jobs submit --json <config>
D. databricks bundle run etl_job

Answer: D

Explanation:
databricks bundle deploy creates or updates the workspace resource but does not execute it.
databricks bundle run etl_job triggers the deployed job using its bundle resource key.

NEW QUESTION # 243
A data engineering team wants to validate a new ingestion pipeline locally while ensuring large aggregations run in serverless compute in their Databricks workspace. They plan to use Databricks Connect and have the option to attach to either a shared cluster or serverless.
Which workspace requirement should be confirmed first to avoid connection failures?

A. Verify that the workspace has Unity Catalog enabled and that the Databricks Connect version supports serverless for the target Runtime release.
B. Verify that the workspace has Unity Catalog disabled and the Databricks Connect version is less than the serverless Runtime version.
C. Verify that only assigned access mode clusters are used because serverless is not supported by Databricks Connect.
D. Verify that the local Spark version equals the serverless Spark version to satisfy Spark Connect parity.

Answer: A

Explanation:
Databricks Connect requires Unity Catalog to be enabled when connecting to serverless compute, and the Databricks Connect version must explicitly support serverless for the target Databricks Runtime to ensure compatibility and prevent connection failures.

NEW QUESTION # 244
Which of the following approaches should be used to send the Databricks Job owner an email in the case that the Job fails?

A. There is no way to notify the Job owner in the case of Job failure
B. Setting up an Alert in the Job page
C. MLflow Model Registry Webhooks
D. Manually programming in an alert system in each cell of the Notebook
E. Setting up an Alert in the Notebook

Answer: B

NEW QUESTION # 245
A data engineer is working with two tables. Each of these tables is displayed below in its entirety.
The data engineer runs the following query to join these tables together:
Which of the following will be returned by the above query?

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

Answer: D

Explanation:
Option A is the correct answer because it shows the result of an INNER JOIN between the two tables. An INNER JOIN returns only the rows that have matching values in both tables based on the join condition. In this case, the join condition is ON a.customer_id = c.customer_id, which means that only the rows that have the same customer ID in both tables will be included in the output. The output will have four columns: customer_id, name, account_id, and overdraft_amt. The output will have four rows, corresponding to the four customers who have accounts in the account table.

NEW QUESTION # 246
Which single Databricks CLI command deploys local bundle assets to the target workspace specified in the bundle configuration file?

A. databricks bundle init && databricks bundle deployment
B. databricks bundle deploy
C. databricks bundle validate
D. databricks bundle plan && databricks bundle run

Answer: B

NEW QUESTION # 247
......

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