Databricks-DE-Assoc Working with Lakeflow Jobs Practice Question
A data engineer is designing a Databricks Job workflow. Which TWO of the following are valid ways to trigger a Databricks Job?
⚠ Common exam trap
Candidates often select manual UI actions only, or mistakenly include unsupported methods, missing that REST APIs and manual triggers are both valid.
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 'Run now' button in the UI.
Databricks Jobs offer multiple entry points for execution. Understanding these triggers is essential for integrating pipelines into broader CI/CD cycles or event-driven architectures. By mastering these methods, engineers can effectively decouple their data processing logic from the scheduling mechanism, allowing for both manual verification during development and robust automated execution in production environments through APIs or service principals.
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 'Run now' button in the UI.
Why this is correct
The 'Run now' feature is the primary way to manually execute a job immediately for testing or ad-hoc data processing requirements. It bypasses the schedule and triggers an instance of the job using the existing configuration, making it indispensable for troubleshooting or re-running failed tasks.
- ✗
Directly editing the Python source code file.
Why it's wrong here
Editing source code changes the logic of the transformation but does not act as an execution trigger. A job must be explicitly invoked via the Jobs API, a scheduler, or a UI action to initiate the instantiation of a compute cluster and the execution of the defined task.
- ✓
Using the Databricks Jobs REST API.
Why this is correct
The Jobs API allows for programmatic triggers, enabling integration with external orchestrators like Airflow or CI/CD pipelines. By sending a POST request to the /api/2.1/jobs/run-now endpoint, engineers can reliably initiate workflows based on external events, ensuring data freshness and consistency across distributed enterprise systems.
- ✗
Changing the cluster node type.
Why it's wrong here
Modifying cluster node types alters the compute performance and cost profile of the environment, but it does not serve as an execution signal. Changing compute configuration is a passive administrative action that only takes effect when the job is actually started by a valid trigger.
- ✗
Modifying the job owner permissions.
Why it's wrong here
Permissions settings control access and authorization for viewing or modifying jobs, but they do not initiate execution. Security configuration is distinct from scheduling and triggers, as authorization ensures that only authorized personnel can manage the job, not that the job will automatically start upon save.
About these practice questions
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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-Assoc 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-Assoc exam.