A data engineering team is adopting Databricks Asset Bundles to manage their ETL pipelines. They have a repository with a `src/` directory containing Python modules, a `tests/` directory, and a `databricks.yml` file at the root. During CI, they run unit tests and then deploy the bundle to a staging workspace. The CI pipeline needs to reference the correct deployment target and ensure the Python code is packaged with the bundle. Which configuration approach should they use?

Place `databricks.yml` inside the `src/` directory so that the bundle automatically includes all Python code and dependencies.

Use a single `targets` block with `mode: production` to deploy to staging, and rely on the CI pipeline to copy Python files to the cluster via `dbutils.fs.cp`.

Define a `targets` block in `databricks.yml` for staging, and use `artifacts` to package the `src/` directory as a wheel file that is installed on the cluster.

Define a `bundle` block in `databricks.yml` with `name` and `targets` for staging, and use `resources` to reference the Python modules.

A data engineer is setting up a new cluster for a team that needs to use the `requests` library (version 2.31.0) and `pandas` (version 2.0.3) for a data processing job. The cluster uses Databricks Runtime 13.3 LTS, which already includes a specific version of `pandas`. The engineer wants to ensure the job runs consistently across environments. What is the best approach?

Use `%pip install requests==2.31.0 pandas==2.0.3` in a notebook cell before running the job.

Install `requests` and `pandas` from PyPI using the cluster library UI, selecting the latest versions available.

Create a `requirements.txt` file with `requests==2.31.0` and `pandas==2.0.3`, and install it as a PyPI library on the cluster.

Install only `requests` from PyPI and rely on the runtime's pre-installed `pandas` version.

In the 'split-apply-combine' pattern used by grouped Pandas UDFs, what does the 'apply' step do?

Applies a pandas function to each group independently

Combines the results into a single DataFrame

Splits the data into groups based on a key

DatabricksCertified Data Engineer Professional

Domain 1Objective 1

Using Python and Tools for Development DATA-ENGINEER-PROFESSIONAL Practice Questions (Page 4)

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 16–20

16application · medium
A data engineering team is adopting Databricks Asset Bundles to manage their ETL pipelines. They have a repository with a `src/` directory containing Python modules, a `tests/` directory, and a `databricks.yml` file at the root. During CI, they run unit tests and then deploy the bundle to a staging workspace. The CI pipeline needs to reference the correct deployment target and ensure the Python code is packaged with the bundle. Which configuration approach should they use?
Select an answer first
17foundation · easy
When installing a PyPI package in a Databricks cluster, which library source type should you select in the Libraries UI?
Select an answer first
18application · medium
A data engineer is setting up a new cluster for a team that needs to use the `requests` library (version 2.31.0) and `pandas` (version 2.0.3) for a data processing job. The cluster uses Databricks Runtime 13.3 LTS, which already includes a specific version of `pandas`. The engineer wants to ensure the job runs consistently across environments. What is the best approach?
Select an answer first
19foundation · easy
In the 'split-apply-combine' pattern used by grouped Pandas UDFs, what does the 'apply' step do?
Select an answer first
20foundation · easy
Which Databricks CLI command is used to deploy a Databricks Asset Bundle to a target environment?
Select an answer first

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Using Python and Tools for Development DATA-ENGINEER-PROFESSIONAL Practice Questions (Page 4)

Questions 16–20

When installing a PyPI package in a Databricks cluster, which library source type should you select in the Libraries UI?

In the 'split-apply-combine' pattern used by grouped Pandas UDFs, what does the 'apply' step do?

Which Databricks CLI command is used to deploy a Databricks Asset Bundle to a target environment?

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