You are writing unit tests for a PySpark transformation function that cleans a DataFrame. You want to run the tests quickly without spinning up a full Databricks cluster. Which approach should you use?

Run the tests on a shared interactive cluster in Databricks.

Run the tests as part of an end-to-end pipeline test.

Use `unittest` with mock objects that simulate DataFrame operations.

Use `pytest` with a local Spark session (`pyspark.sql.SparkSession.builder.master("local[2]")`).

Two engineers are collaborating on a Databricks notebook. Engineer A changed the first cell, and Engineer B changed the second cell. When merging, Git reports a conflict because the notebook's JSON metadata (such as cell IDs) has overlapping changes. What is the most efficient way to resolve this conflict?

Use the `git merge` tool with a notebook-specific merge driver that understands Databricks notebook format.

Have one engineer reapply their changes on top of the other's branch manually.

Manually edit the notebook JSON to merge the cell changes and resolve the metadata conflict.

Use the Databricks CLI's `databricks labs` command to automatically merge notebook conflicts.

Your team is building a medallion architecture pipeline in Azure Databricks. You need to validate that the bronze-to-silver transformation correctly handles null values and data type conversions. Which testing tier should you implement to catch these issues early in the development cycle?

User acceptance tests that verify the final reports meet business requirements.

Integration tests that check the pipeline's ability to read from the source system.

Unit tests that run on the transformation functions with sample DataFrames.

End-to-end tests that run the entire pipeline on production data.

Your team is developing a complex pipeline with multiple transformations. You have limited CI/CD budget and need to catch logic errors as early as possible while also ensuring that the pipeline components work together. Which testing strategy should you prioritize?

Run only end-to-end tests on the full pipeline in staging.

Run integration tests only, covering all component interactions.

Run unit tests on every transformation function and a few integration tests for critical component interactions.

Run only user acceptance tests with business stakeholders.

Microsoft Certified:Azure Databricks Data Engineer Associate

Domain 4Objective 3

Implement Development Lifecycle Processes in Azure Databricks DP-750 Practice Questions (Page 3)

Part of the Deploy and maintain data pipelines and workloads domain, which accounts for 30–35% of the DP-750 exam. Microsoft does not publish an official question count, but from its 120-minute exam (~50–80 total, ~15–28 in this domain), expect 4–7 from this objective — we provide 36 practice questions to prepare you well beyond it. (estimate)

36questions here

8free pages

12concepts

30–35%of the exam

Questions 11–15

11foundation · easy
Which Python testing framework is commonly used to write and run unit tests in Azure Databricks notebooks?
Select an answer first
12application · medium
You are writing unit tests for a PySpark transformation function that cleans a DataFrame. You want to run the tests quickly without spinning up a full Databricks cluster. Which approach should you use?
Select an answer first
13expert · hard
Two engineers are collaborating on a Databricks notebook. Engineer A changed the first cell, and Engineer B changed the second cell. When merging, Git reports a conflict because the notebook's JSON metadata (such as cell IDs) has overlapping changes. What is the most efficient way to resolve this conflict?
Select an answer first
14application · medium
Your team is building a medallion architecture pipeline in Azure Databricks. You need to validate that the bronze-to-silver transformation correctly handles null values and data type conversions. Which testing tier should you implement to catch these issues early in the development cycle?
Select an answer first
15expert · hard
Your team is developing a complex pipeline with multiple transformations. You have limited CI/CD budget and need to catch logic errors as early as possible while also ensuring that the pipeline components work together. Which testing strategy should you prioritize?
Select an answer first

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Implement Development Lifecycle Processes in Azure Databricks DP-750 Practice Questions (Page 3)

Questions 11–15

Which Python testing framework is commonly used to write and run unit tests in Azure Databricks notebooks?

You are writing unit tests for a PySpark transformation function that cleans a DataFrame. You want to run the tests quickly without spinning up a full Databricks cluster. Which approach should you use?

Your team is building a medallion architecture pipeline in Azure Databricks. You need to validate that the bronze-to-silver transformation correctly handles null values and data type conversions. Which testing tier should you implement to catch these issues early in the development cycle?

Your team is developing a complex pipeline with multiple transformations. You have limited CI/CD budget and need to catch logic errors as early as possible while also ensuring that the pipeline components work together. Which testing strategy should you prioritize?

Keep studying — Deploy and maintain data pipelines and workloads