Databricks-GenAI-Assoc Application Development Practice Question
Which TWO of the following are mandatory requirements for developing an AI application using the Databricks Mosaic AI Model Serving environment?
⚠ Common exam trap
Candidates often focus on model training parameters or API authentication methods, missing the foundational requirements of Unity Catalog registration and defined compute resources for serving.
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 model must be registered as a model version within a Unity Catalog schema.
Mosaic AI Model Serving requires specific configurations to ensure secure and performant access. Firstly, the model must be registered in Unity Catalog to maintain governance and lineage. Secondly, the serving endpoint requires defined compute resources, typically GPU-accelerated for LLMs, to handle inference requests. These requirements are essential for productionizing models, as they ensure that models are discoverable, governed, and have the necessary hardware to meet low-latency performance targets in real-time scenarios.
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 model must be stored in a legacy Databricks Workspace folder.
Why it's wrong here
Legacy workspace paths are not compatible with the modern Unity Catalog serving architecture. All models must be registered within Unity Catalog to leverage the unified governance model, lineage tracking, and permission structures that Mosaic AI services require to function effectively within a secure enterprise data environment.
- ✓
The model must be registered as a model version within a Unity Catalog schema.
Why this is correct
Unity Catalog acts as the central repository for model artifacts and versions in Databricks. Registering the model here provides the necessary metadata, lineage, and access control required by the serving infrastructure to deploy the model securely and ensure it remains reachable by authorized internal or external applications.
- ✗
The model must be served using a shared-access mode interactive cluster.
Why it's wrong here
Model serving endpoints use dedicated, managed inference infrastructure, not interactive clusters. Interactive clusters are designed for development and exploratory analysis, whereas inference services require specific, optimized runtime environments that provide scalability, high availability, and auto-scaling capabilities tailored specifically for model serving workloads rather than general-purpose data science tasks.
- ✓
The serving endpoint must be configured with a defined compute resource.
Why this is correct
An inference endpoint needs specific compute resources (like GPU instances) to execute model logic. Defining this configuration is a mandatory step in the deployment process, allowing the platform to provision the necessary infrastructure to meet the required throughput and latency demands of the model being deployed for users.
- ✗
The application code must perform manual model sharding across nodes.
Why it's wrong here
Databricks Mosaic AI handles model sharding and distribution automatically within the managed serving environment. Developers do not need to manually manage hardware-level parallelism or model sharding, as the infrastructure layer abstracts these complexities to focus on ease of deployment, scalability, and simplified management of inference workloads.
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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.