Databricks-DE-Pro Developing Code (Python/SQL) Practice Question
A Data Engineer is using Databricks Asset Bundles (DABs) to manage a project. Where should the engineer define the job settings, such as clusters, schedules, and task dependencies?
⚠ Common exam trap
Candidates often confuse DABs configuration with traditional Spark submit scripts or workspace notebooks, incorrectly choosing standalone python or notebook files instead of the root-level YAML configuration required for bundle deployments.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
In a databricks.yml file within the project root.
Databricks Asset Bundles use YAML files, specifically those located in the bundles configuration folder, to manage infrastructure as code. Defining job settings in the `databricks.yml` file allows for version control and consistent deployments across different environments (dev, staging, prod). This approach ensures that the entire project state is replicable and managed through a standardized CI/CD pipeline, reducing manual configuration errors and increasing the speed of environment deployments.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
In a hardcoded JSON file located in the /tmp/ directory.
Why it's wrong here
The /tmp/ directory is ephemeral and not suitable for storing project configurations. Any files stored there are at risk of being deleted by the system. Furthermore, hardcoded configurations lack the modularity and variable support provided by the official DABs YAML configuration structure, making them difficult to manage in a collaborative, automated development environment.
- ✓
In a databricks.yml file within the project root.
Why this is correct
The databricks.yml file is the central configuration file for Databricks Asset Bundles. It contains the schema definitions for resources, including jobs, pipelines, and delta tables. This file serves as the single source of truth for the deployment, ensuring that all job definitions are versioned, reproducible, and easily deployable via the CLI.
- ✗
Inside the Python code using the dbutils.job() library.
Why it's wrong here
The dbutils library is for runtime operations within a notebook, not for defining infrastructure or scheduling jobs. Job definitions are infrastructure-as-code and must be managed outside of the business logic code to maintain separation of concerns. Defining infrastructure within the application code makes it impossible to manage resources independently of the notebook execution.
- ✗
By manually clicking through the Databricks UI after deployment.
Why it's wrong here
Manual UI configuration defeats the purpose of Asset Bundles and violates the principles of Infrastructure-as-Code (IaC). Any changes made manually in the UI will not be reflected in the project repository, leading to configuration drift and making it impossible to audit, recreate, or roll back the environment state to a known good configuration.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
This Databricks-DE-Pro practice question is part of Courseiva's free Databricks certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Databricks-DE-Pro exam.