Databricks-GenAI-Assoc Assembling and Deploying Apps Practice Question
A Generative AI engineer is packaging a retrieval-augmented generation application for deployment with Databricks Asset Bundles. The bundle must provision a Databricks job that periodically refreshes a Delta table used as the vector index, and the job requires a specific cluster node type that differs between the development and production workspaces. Which approach correctly handles the node type difference while keeping a single bundle definition?
⚠ Common exam trap
The trap here is assuming a single bundle cannot express environment differences, which pushes candidates toward duplicating resource files instead of using target-scoped variable overrides.
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 the node type in a databricks.yml variable and override its value in a target-specific block for each environment.
The bundle should declare a variable for the node type and override it inside each target, because Databricks Asset Bundles resolve variables per target at deploy time. This keeps one authoritative job definition while letting development and production use different compute, and it avoids duplicated YAML that drifts over time.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Create a separate bundle directory for each environment with entirely duplicated resource files and deploy each one independently.
Why it's wrong here
Duplicating resource files across bundle directories creates drift: a change to the job logic must be applied in every copy, and reviewers must diff multiple trees. Databricks Asset Bundles exist precisely to avoid this duplication through targets and variables in a single definition.
- ✓
Define the node type in a databricks.yml variable and override its value in a target-specific block for each environment.
Why this is correct
Databricks Asset Bundles support variables declared at the top level and overridden per target, so the same job definition can use a development node type and a production node type without duplicating the resource YAML. This is the intended mechanism for environment-specific values.
- ✗
Use a notebook widget inside the job to read the node type from a table at runtime and restart the cluster with the new value.
Why it's wrong here
A notebook widget cannot change the cluster a job is already running on; the cluster specification is resolved before the notebook executes. Reading a table at runtime also introduces an unnecessary dependency and does not solve the bundle-time configuration problem.
- ✗
Hard-code the production node type in the job resource and rely on the development workspace to silently substitute a smaller node type.
Why it's wrong here
Databricks does not silently substitute node types; if the specified node type is unavailable in the development workspace, the job run fails with a cluster creation error. Hard-coding also removes the ability to test with a cheaper node type, defeating the purpose of environment separation.
Visual reference
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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-GenAI-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-GenAI-Assoc exam.