Databricks-GenAI-Assoc Assembling and Deploying Apps Practice Question
An AI engineer is developing a custom Databricks App using Mosaic AI Agent Framework and needs to deploy the application workspace securely. Which deployment artifact and configuration mechanism should the engineer use to define the app dependencies and entry point?
⚠ Common exam trap
Candidates often confuse Databricks App deployment with standard notebook deployment, forgetting that an 'app.yaml' file is strictly required to define the entry point and runtime environment.
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
✓
An app.yaml configuration file defining the command, environment variables, and source path alongside the application source code.
Deploying Databricks Apps requires an app.yaml configuration file and source code bundled together. The app.yaml file specifies the runtime, environment variables, and command-line entry points required to execute the application properly inside the Databricks environment. Defining these parameters correctly ensures the Mosaic AI Agent dependencies are initialized and served without manual intervention.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A setup.py file placed in the root directory configured to automatically trigger a cluster restart upon deployment.
Why it's wrong here
While setup.py handles Python package metadata, it does not manage application lifecycle states or entry points for Databricks Apps. Databricks Apps rely on specific infrastructure configuration manifests rather than traditional Python setup modules to control runtime execution.
- ✓
An app.yaml configuration file defining the command, environment variables, and source path alongside the application source code.
Why this is correct
The app.yaml file serves as the core manifest for Databricks Apps, enabling developers to specify entry-point commands, required Python versions, and runtime parameters. This file allows the Databricks platform to provision compute and host the application securely within the workspace.
- ✗
A Databricks Asset Bundles deployment target pointing to an external Kubernetes cluster hosting the agent code.
Why it's wrong here
Databricks Apps are natively hosted and managed directly inside the Databricks workspace infrastructure rather than external Kubernetes clusters. Asset Bundles are used for orchestration, but runtime hosting relies on the built-in Databricks Apps compute layer.
- ✗
A standard requirements.txt file executed through a Jupyter notebook scheduled via a workflow job.
Why it's wrong here
A notebook run by a workflow job executes ad hoc code rather than packaging a Databricks App; the Agent Framework requires a defined app artifact with its dependency and entry-point configuration. It is tempting because notebooks and jobs are the standard way to run Python on Databricks, but they do not produce a deployable application endpoint.
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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.