Databricks-DE-Pro Developing Code (Python/SQL) Practice Question
Which TWO of the following are valid ways to trigger a job in Databricks?
⚠ Common exam trap
Candidates often select UI-only options while ignoring the REST API. In a professional data engineering context, automation via API is just as valid and common as manual UI triggering.
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
✓
Using the 'Jobs' UI to trigger a run.
Databricks provides multiple interfaces for job orchestration. The 'Jobs' UI in the workspace allows for interactive configuration and scheduling. Alternatively, the Databricks REST API provides a programmatic way to trigger jobs, which is crucial for CI/CD pipelines and integrating Databricks with external orchestrators like Airflow or Azure Data Factory. Using these methods ensures that pipelines can be automated, monitored, and integrated into broader enterprise data workflows.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Using the 'Jobs' UI to trigger a run.
Why this is correct
The UI provides an easy-to-use interface for manually running a job, viewing historical execution results, and configuring schedules. This is ideal for development, testing, and troubleshooting, as it allows engineers to quickly run and monitor jobs without writing code or interacting with APIs.
- ✗
Executing the 'run_job' command inside a notebook cell.
Why it's wrong here
There is no built-in `run_job` command within the notebook environment to trigger an external job definition. Notebooks are execution environments, and while they can be used to run jobs, they do not have a native, top-level command to trigger another job directly by name.
- ✓
Calling the Databricks REST API (Jobs API).
Why this is correct
The Jobs API allows for programmatic control over job execution, including running, canceling, and updating jobs. This is the standard mechanism for automated deployment pipelines and external orchestrators, enabling the integration of Databricks into larger data platform architectures.
- ✗
Running the 'dbutils.jobs.run()' method.
Why it's wrong here
The `dbutils` library does not have a `jobs` module that exposes a `run()` method for triggering jobs. While `dbutils` is a powerful tool for file system and secret management, it is not the interface for orchestrating job execution within the workspace.
- ✗
Creating a file named 'trigger.job' in DBFS.
Why it's wrong here
Creating files in DBFS does not trigger any background jobs in Databricks. Databricks job execution is managed through the platform's orchestration service, which operates independently of file system contents, making this an ineffective way to automate or manage job runs.
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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
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