Databricks-DE-Assoc Implementing CI/CD Practice Question
A data platform team is migrating their deployment process to Databricks Asset Bundles (DABs). They already have a Python wheel task defined in a Databricks Job and a set of notebooks in a Git repository. They want the bundle deployment to be repeatable across development, staging, and production targets with different cluster sizes. Which approach should they take to parameterize the target-specific cluster configuration?
⚠ Common exam trap
The trap here is assuming environment differences require separate job definitions or branches rather than target-level variable overrides within a single bundle.
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
✓
Use the bundle's databricks.yml targets block with variables and override cluster node types per target.
Databricks Asset Bundles provide a targets block in databricks.yml specifically so one bundle can deploy to multiple workspaces with environment-specific overrides. Using variables for cluster properties and overriding them per target preserves a single job definition while still allowing dev, staging, and prod to use different cluster sizes. This matches the recommended DABs workflow for environment parity and repeatable CI/CD 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.
- ✗
Store cluster configuration in a Delta table and have the job read it at runtime using spark.conf.
Why it's wrong here
Reading cluster configuration at runtime cannot influence cluster provisioning because the cluster is created before the job's code executes. Cluster-level settings such as node type and autoscaling must be declared in the job or cluster definition. This approach also adds an unnecessary dependency on a table and does not integrate with DABs target overrides.
- ✗
Define cluster settings directly in the job YAML and use separate branches for each environment.
Why it's wrong here
Separate branches per environment force code duplication and merge overhead, which DABs were designed to eliminate. Cluster settings hardcoded in the job YAML cannot vary by target without editing files between deployments. This approach breaks the single-source-of-truth model and makes promotions error-prone because the job definition itself changes between environments.
- ✗
Create a separate Databricks Job for each environment and deploy them with the same bundle.
Why it's wrong here
Duplicating jobs per environment multiplies maintenance and risks drift between definitions. DABs are designed so a single job resource can be deployed to multiple targets with overrides, not duplicated per environment. Separate jobs also complicate permissions and monitoring because job IDs and names diverge across workspaces.
- ✓
Use the bundle's databricks.yml targets block with variables and override cluster node types per target.
Why this is correct
DABs support a targets section in databricks.yml where each target can override variables, workspace host, and resource properties. By defining variables for cluster node types and cluster sizes, the team can keep one job definition and supply target-specific values. This is the documented pattern for environment parity and repeatable deployments across dev, staging, and prod.
Quick reference
RAID Level Comparison
| RAID Level | Min Disks | Fault Tolerance | Read | Write | Usable Capacity |
|---|---|---|---|---|---|
| RAID 0 | 2 | None | Excellent | Excellent | 100% |
| RAID 1 | 2 | 1 disk | Good | Moderate | 50% |
| RAID 5 | 3 | 1 disk | Good | Moderate | 67–94% |
| RAID 6 | 4 | 2 disks | Good | Lower | 50–88% |
| RAID 10 | 4 | 1 disk per mirror | Excellent | Good | 50% |
RAID is not a backup strategy — it protects against disk failure but not against accidental deletion, ransomware, or site-level events.
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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.