Databricks-GenAI-Assoc Assembling and Deploying Apps Practice Question
An engineer has finished building a RAG chatbot and wants to expose it as a Databricks App so business users can reach it through a browser. The app needs a Python web server and a command that starts it. Which artifact in the app's project layout defines the runtime command and dependencies used when the app is deployed?
⚠ Common exam trap
The trap here is conflating deployment orchestration files with the app's own runtime configuration, when only the app configuration declares the start command.
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
✓
The app.yaml file, which declares the command to run and the environment or dependencies for the app.
A Databricks App is launched according to its app.yaml configuration, which specifies the command that starts the web server along with source and dependency information. The bundle file orchestrates deployment, requirements files list packages, and model signatures describe schemas, but only the app configuration tells the platform how to run the application.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
The app.yaml file, which declares the command to run and the environment or dependencies for the app.
Why this is correct
Databricks Apps use an app.yaml file to define how the app starts and what it needs. It carries the run command, the source path, and environment or dependency references, so the platform knows how to launch the web server on deploy. This is the correct artifact for specifying startup behavior and dependencies in the app project layout.
- ✗
The MLflow model signature stored with the logged model, which encodes the serving command.
Why it's wrong here
An MLflow model signature describes input and output schemas for a model, not how to start an application server. It has no bearing on the process command or dependency installation for a Databricks App. Relying on it would leave the platform without a launch instruction, so the app would not start as intended.
- ✗
The requirements.txt file alone, which both lists dependencies and specifies the server start command.
Why it's wrong here
requirements.txt lists Python package dependencies but has no field for a start command. It cannot tell the platform which process to launch or on which port to serve, so on its own it is insufficient to run the app. It is typically referenced by the app configuration rather than serving as the launch definition itself.
- ✗
The databricks.yml bundle file, which contains the Python entrypoint and pip requirements for the app.
Why it's wrong here
databricks.yml describes bundle resources and targets for deployment orchestration, not the app's runtime entrypoint or dependency list. While a bundle can reference an app resource, the startup command and dependencies belong in the app's own configuration file. Putting the entrypoint here would not be read as the app's launch definition and would not start the server.
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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.