You are using Databricks Asset Bundles to deploy a job that runs a notebook. The job must run in different environments with different cluster sizes and different parameter values. You want to avoid duplicating the notebook code and job definition. What is the best way to manage environment-specific settings?

Use environment variables in the notebook and set them manually before each deployment.

Use Git branches to hold environment-specific configuration files and switch branches for each deployment.

Use bundle targets and variable substitution to define environment-specific cluster sizes and parameters in the bundle configuration.

Create a separate bundle for each environment with duplicated notebook code.

You are setting up a Databricks Asset Bundle for a project that uses multiple notebooks and a job definition. You want to ensure that the bundle can be deployed to different environments (dev, staging, prod) with environment-specific parameters. What should you configure in the bundle?

Create a single `databricks.yml` with hardcoded values for the production environment.

Store environment-specific parameters in a separate Git branch and switch branches for each deployment.

Use bundle configuration files with target-specific overrides, such as `databricks.yml` plus `databricks.dev.yml` and `databricks.prod.yml`.

Use environment variables in the notebook code and set them manually before each deployment.

Which of the following is an example of an integration test in Azure Databricks?

Testing a function that adds two numbers

Testing the user interface of the Databricks workspace

Testing the speed of a query on a large dataset

Testing that a notebook reads data from a Delta table and writes to another table correctly

What is the primary purpose of a pull request in a Git-based Azure Databricks workflow?

To automatically deploy code to production

To delete a branch after it has been merged

To clone a remote repository to the local machine

To propose changes from a feature branch and request review before merging

What is the primary goal of end-to-end testing in an Azure Databricks data pipeline?

To confirm that the system meets business requirements

To validate the entire pipeline from data ingestion to final output

To test the interaction between two specific components

To test individual functions in isolation

Microsoft Certified:Azure Databricks Data Engineer Associate

Domain 4Objective 3

Implement Development Lifecycle Processes in Azure Databricks DP-750 Practice Questions (Page 7)

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 31–35

31expert · hard
You are using Databricks Asset Bundles to deploy a job that runs a notebook. The job must run in different environments with different cluster sizes and different parameter values. You want to avoid duplicating the notebook code and job definition. What is the best way to manage environment-specific settings?
Select an answer first
32application · medium
You are setting up a Databricks Asset Bundle for a project that uses multiple notebooks and a job definition. You want to ensure that the bundle can be deployed to different environments (dev, staging, prod) with environment-specific parameters. What should you configure in the bundle?
Select an answer first
33foundation · easy
Which of the following is an example of an integration test in Azure Databricks?
Select an answer first
34foundation · easy
What is the primary purpose of a pull request in a Git-based Azure Databricks workflow?
Select an answer first
35foundation · easy
What is the primary goal of end-to-end testing in an Azure Databricks data pipeline?
Select an answer first

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