Databricks-GenAI-Assoc Assembling and Deploying Apps Practice Question
A machine learning engineer needs to deploy a real-time Model Serving endpoint. Which Databricks construct is required to manage the model's environment, dependencies, and artifacts while ensuring version control for the deployment?
⚠ Common exam trap
Candidates frequently choose raw MLflow runs or workspace artifact paths, failing to realize that production serving mandates version-controlled MLflow Models stored within the Unity Catalog.
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 Models within Unity Catalog
Databricks Model Serving relies on the Unity Catalog Model Registry to manage model versions and artifacts. By using Unity Catalog, organizations ensure governance, lineage, and consistent environment reproduction. This is critical for production deployments as it decouples the model development lifecycle from the serving infrastructure, ensuring that the exact artifact deployed in staging is the one running in production, thereby maintaining model integrity and reproducibility across environments.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Delta Live Tables pipelines
Why it's wrong here
Delta Live Tables is primarily designed for data engineering and ETL workloads, focusing on data quality and pipeline management. It lacks the features required to serve machine learning models, such as model inference, endpoint scaling, or artifact versioning necessary for real-time model serving infrastructure.
- ✓
MLflow Models within Unity Catalog
Why this is correct
MLflow Models registered within Unity Catalog provide a centralized repository for model artifacts and metadata. This integration enables Model Serving to access validated model versions, manage environment configurations, and track the full lineage of the model, which is essential for secure and reliable production deployments.
- ✗
Databricks Job clusters
Why it's wrong here
Job clusters are optimized for scheduled data processing and batch workloads. While they can run training code, they are not architected for low-latency inference or managing the HTTP endpoints required for real-time model serving. They lack the specialized scaling and endpoint management features required here.
- ✗
Workspace-local Model Registry
Why it's wrong here
The legacy workspace-local registry does not support the governance and cross-workspace sharing capabilities of Unity Catalog. Databricks recommends using Unity Catalog for all new deployments to ensure compliance, centralized access control, and seamless integration with the modern Databricks Model Serving platform architecture.
About these practice questions
This Databricks-GenAI-Assoc question is part of Courseiva's 330-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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.