Databricks-DE-Pro Debugging and Deploying Practice Question
A data engineer is deploying a Databricks job using Databricks Asset Bundles. They want to ensure that the job uses a specific cluster configuration that is defined once and reused across multiple tasks. Which bundle feature should they use?
⚠ Common exam trap
Candidates often confuse job-level cluster definitions with task-level `new_cluster` or external cluster references, which do not provide the same reusability.
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
✓
Define a `job_cluster` in the job's `job_clusters` section and reference it by key in each task.
The `job_clusters` section in a Databricks job definition allows specifying cluster configurations that can be referenced by multiple tasks via `job_cluster_key`. This is the correct way to define a cluster once and reuse it across tasks within the same job, ensuring consistency and simplifying maintenance. Other options either duplicate configuration or rely on external resources.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a `new_cluster` definition for each task, ensuring they all have identical configurations.
Why it's wrong here
While duplicating `new_cluster` definitions across tasks would achieve identical configurations, it is not a reusable feature. It leads to maintenance overhead and potential drift. The question asks for a feature that defines once and reuses, which `new_cluster` does not provide at the job level.
- ✓
Define a `job_cluster` in the job's `job_clusters` section and reference it by key in each task.
Why this is correct
The `job_clusters` section allows defining reusable cluster configurations at the job level. Each task can then reference a cluster by its key using the `job_cluster_key` field. This promotes consistency and reduces duplication. It is the intended way to share cluster settings across tasks within a single job.
- ✗
Define a cluster in the `resources/clusters` directory and reference it in the job.
Why it's wrong here
Databricks Asset Bundles do not support defining standalone clusters as resources that can be referenced by jobs. Clusters are defined inline within jobs or tasks. There is no `resources/clusters` directory in the bundle schema. This approach would not work and is not supported.
- ✗
Use an `existing_cluster_id` for all tasks, pointing to a pre-created cluster.
Why it's wrong here
Using an existing cluster ID requires the cluster to be created outside the bundle, which defeats the purpose of infrastructure as code. It also does not define the cluster configuration within the bundle, making it less portable and reproducible. The job would depend on an external resource, which is not ideal for deployment.
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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.