A team is building a Databricks Asset Bundle for a data processing project. They have a `src/` directory with Python modules, a `tests/` directory, and a `databricks.yml` file. They want to ensure that the bundle includes the Python code when deployed to a job. What is the correct way to structure the bundle?

Use `resources` to define the Python modules as a library.

Place all Python code in the `databricks.yml` file as inline code.

Copy the `src/` directory to DBFS during deployment using a shell script.

Reference the `src/` directory in the job's `python_file` path, and use `artifacts` to package the code as a wheel.

A data engineering team is migrating their ETL code to Databricks Asset Bundles. They have a monorepo with multiple projects, each with its own `databricks.yml` file. They want to use a single CI/CD pipeline to deploy all projects to the same staging workspace, but they need to ensure that each project's deployment is isolated and can be promoted independently. What is the best approach?

Use environment variables in the CI pipeline to switch between projects.

Create a single bundle that includes all projects and deploy them together.

Use separate bundle configurations for each project and run `databricks bundle deploy` for each in the CI pipeline.

Use a single `databricks.yml` file at the root and reference all projects as subdirectories.

What is a common cause of a 'Dependency conflict' error when installing libraries in Databricks?

Two packages require different versions of the same dependency

The workspace is in a different region

The cluster has insufficient memory

In a Databricks notebook, which command installs a local wheel file?

%sh install /path/to/my_package.whl

%conda install /path/to/my_package.whl

%pip install /path/to/my_package.whl

DatabricksCertified Data Engineer Professional

Domain 1Objective 1

Using Python and Tools for Development DATA-ENGINEER-PROFESSIONAL Practice Questions (Page 3)

Part of the Developing Code for Data Processing using Python and SQL domain, which accounts for 22% of the DATA-ENGINEER-PROFESSIONAL exam. Databricks does not publish an official question count, but from its 120-minute exam (~50–80 total, ~11–18 in this domain), expect 6–9 from this objective — we provide 23 practice questions to prepare you well beyond it. (estimate)

23questions here

5free pages

7concepts

22%of the exam

Questions 11–15

11application · medium
A team is building a Databricks Asset Bundle for a data processing project. They have a `src/` directory with Python modules, a `tests/` directory, and a `databricks.yml` file. They want to ensure that the bundle includes the Python code when deployed to a job. What is the correct way to structure the bundle?
Select an answer first
12expert · hard
A data engineering team is migrating their ETL code to Databricks Asset Bundles. They have a monorepo with multiple projects, each with its own `databricks.yml` file. They want to use a single CI/CD pipeline to deploy all projects to the same staging workspace, but they need to ensure that each project's deployment is isolated and can be promoted independently. What is the best approach?
Select an answer first
13foundation · easy
Which command can help you diagnose dependency conflicts by listing installed packages and their versions?
Select an answer first
14foundation · easy
What is a common cause of a 'Dependency conflict' error when installing libraries in Databricks?
Select an answer first
15foundation · easy
In a Databricks notebook, which command installs a local wheel file?
Select an answer first

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Using Python and Tools for Development DATA-ENGINEER-PROFESSIONAL Practice Questions (Page 3)

Questions 11–15

Which command can help you diagnose dependency conflicts by listing installed packages and their versions?

What is a common cause of a 'Dependency conflict' error when installing libraries in Databricks?

In a Databricks notebook, which command installs a local wheel file?

Keep studying — Developing Code for Data Processing using Python and SQL