Databricks-GenAI-Assoc Assembling and Deploying Apps Practice Question
Which Databricks feature is primary for managing the lifecycle, versioning, and deployment readiness of custom Generative AI models?
⚠ Common exam trap
Examinees often guess general MLflow tracking features instead of identifying the MLflow Model Registry as the dedicated tool for lifecycle and deployment readiness.
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
✓
MLflow Model Registry
MLflow Model Registry is the central feature for managing the lifecycle of models, including versioning and stage transitions. Understanding how to promote a model from 'Staging' to 'Production' is fundamental for ensuring that only tested and verified models reach the end-users. This workflow is critical for maintaining quality and stability in generative AI applications, as it provides a structured process for model evolution and deployment management.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Unity Catalog Volumes
Why it's wrong here
Unity Catalog Volumes are designed for storing and managing unstructured data files, not for model versioning or deployment lifecycle management. While volumes are useful for organizing training data, they do not provide the registry features required to track model metadata, transitions, or deployment readiness statuses for AI models.
- ✓
MLflow Model Registry
Why this is correct
The MLflow Model Registry provides a centralized store for managing the full lifecycle of a model. It allows teams to version models, transition them between lifecycle stages like 'Staging' and 'Production', and maintain a comprehensive history of model development, which is essential for reliable and reproducible deployments in production environments.
- ✗
Databricks SQL Warehouse
Why it's wrong here
Databricks SQL Warehouses are optimized for running SQL queries and BI workloads, not for model management or deployment. They lack the necessary API and registry features required to manage model versions, transitions, or inference endpoint orchestration, making them unsuitable for the lifecycle management of generative AI models in production.
- ✗
Delta Live Tables
Why it's wrong here
Delta Live Tables is a framework for building reliable and maintainable data pipelines. It is not intended for the management, versioning, or deployment of machine learning models. While it handles data transformation effectively, it lacks the specific tools for tracking model artifacts and their associated deployment states in production.
About these practice questions
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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.