Databricks-GenAI-Assoc Assembling and Deploying Apps Practice Question
A data team is developing a Mosaic AI Agent application. Which TWO of the following are mandatory for deploying this application using the Databricks Model Serving infrastructure?
⚠ Common exam trap
Candidates often select manual deployment scripts or external orchestration tools, missing the core mandatory package requirements like signatures and environment files.
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
✓
A logged model signature defining input and output schemas
Deploying Mosaic AI Agents requires defining a signature for the model input/output and ensuring the agent code is packaged with its dependencies. These steps are crucial because the Serving environment needs to understand how to route requests and instantiate the environment. Properly defining the signature ensures type safety during inference, while dependency management prevents runtime errors, ensuring the agent operates as expected within the serverless environment.
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 logged model signature defining input and output schemas
Why this is correct
A model signature is mandatory for Model Serving to validate incoming JSON payloads. It allows the serving endpoint to automatically enforce data types and structure, preventing malformed requests from reaching the model code. This ensures consistency and simplifies debugging during the inference request-response cycle.
- ✗
A manual provisioning of a dedicated GPU worker pool
Why it's wrong here
Databricks Model Serving is a serverless offering, meaning users do not need to manually provision or manage infrastructure like worker pools. The platform handles autoscaling, capacity management, and infrastructure allocation automatically based on the traffic demands, simplifying the deployment process for the developer.
- ✓
A requirements.txt or conda environment file
Why this is correct
Including environment specifications is critical for the serving endpoint to install necessary libraries before the model can execute. Without these dependencies, the agent code would fail to run. MLflow automatically captures these during the log_model process, ensuring the remote environment matches the development environment.
- ✗
A hardcoded API key for external vector store access
Why it's wrong here
Hardcoding credentials is a significant security risk and is discouraged. Instead, Databricks provides secret scopes or Unity Catalog connections to handle authentication securely. Storing secrets within the code exposes the application to potential compromise, violating security best practices for production enterprise application deployments.
- ✗
A pre-configured Kubernetes manifest file
Why it's wrong here
Databricks Model Serving is a fully managed service that abstracts away Kubernetes complexity. Developers do not interact with K8s manifests or cluster configuration files. The platform handles containerization and orchestration internally, allowing developers to focus strictly on the agent logic rather than the underlying infrastructure.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
About these practice questions
Courseiva writes every Databricks-GenAI-Assoc question from scratch — 330 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or 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.