Databricks-GenAI-Assoc Assembling and Deploying Apps Practice Question
An engineer is packaging a GenAI agent application with Databricks Asset Bundles so that the same bundle deploys to a development and a production workspace. The agent's serving endpoint name must differ per target, and the production endpoint needs more concurrent capacity. Which mechanism in the bundle configuration should the engineer use?
⚠ Common exam trap
The trap here is treating secrets or post-deploy scripts as the way to vary resource attributes, when bundle variables with per-target overrides are the purpose-built mechanism.
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 target-specific variables in databricks.yml and reference them with ${var.endpoint_name} and ${var.min_replicas} in the resource definition.
Databricks Asset Bundles let you declare variables and override them per target, so a single resource definition can deploy the agent with different endpoint names and replica counts in development and production. Referencing variables with the ${var.} syntax keeps the bundle DRY while honoring environment differences. Hard-coded values, secret scopes, and post-deployment mutation scripts all fail to provide the clean, declarative per-target configuration the scenario requires.
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 post-deployment notebook that calls the serving endpoints API to rename the endpoint and adjust replicas after each bundle deployment.
Why it's wrong here
Renaming a serving endpoint after deployment is not a supported way to manage naming, and mutating resources outside the bundle causes drift between the declared and actual state. The bundle would not know about the changes, so subsequent deployments could fail or revert them. This is fragile and does not leverage the bundle's native parameterization.
- ✗
Store the endpoint name and replica count as secrets in a Databricks secret scope and read them at deployment time with the Databricks CLI.
Why it's wrong here
Secret scopes hold sensitive values and are not designed to parameterize resource definitions such as endpoint names or replica counts. Bundles cannot interpolate secret values directly into resource fields at deploy time, and replica counts are not secrets. This adds complexity without solving the environment-specific configuration requirement.
- ✓
Define target-specific variables in databricks.yml and reference them with ${var.endpoint_name} and ${var.min_replicas} in the resource definition.
Why this is correct
Databricks Asset Bundles support variables declared at the bundle level and overridden per target, which is the supported way to vary values such as endpoint names and replica counts across development and production. Referencing them with the ${var.} syntax keeps one resource definition while letting each target supply different values, matching the requirement precisely.
- ✗
Hard-code the production endpoint name and replica count, then maintain a separate copy of the bundle for development.
Why it's wrong here
Duplicating the bundle creates two divergent sources of truth that will drift and multiply maintenance work. Hard-coding values also prevents the development target from using different names or smaller capacity, and it bypasses the bundle's built-in target override mechanism. This approach defeats the purpose of using a single deployable bundle across workspaces.
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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.