AI-102 Plan and manage an Azure AI solution Practice Question
A company is building a custom question-answering solution using Azure AI Language. They need to ensure that the model can provide answers from a set of internal documents, but only to authenticated users from the company's Azure Active Directory tenant. The solution should minimize latency and cost. Which deployment option should the team choose?
⚠ Common exam trap
Watch out — candidates often assume Azure AD token-based authentication alone is sufficient for security, overlooking that a public endpoint still exposes the service to internet-based attacks and latency, while private endpoints are required for true network isolation.
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
✓
Deploy a custom question-answering model using a dedicated Azure AI Language resource with a private endpoint and managed identity.
Deploying a custom question-answering model with a dedicated Azure AI Language resource, a private endpoint, and managed identity ensures that only authenticated users from the company's Azure AD tenant can access the solution via private network connectivity, minimizing latency by avoiding public internet routing and reducing cost by using a dedicated (not serverless) resource that can be right-sized.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Deploy a custom question-answering model using a dedicated Azure AI Language resource with a private endpoint and managed identity.
Why this is correct
This provides secure access via private endpoint and managed identity, and dedicated resources ensure low latency and predictable cost.
- ✗
Use the public endpoint of Azure AI Language with Azure AD token-based authentication.
Why it's wrong here
Public endpoint does not inherently restrict access to the company's tenant; it requires additional network controls.
- ✗
Deploy a serverless endpoint with Azure Functions and Azure Cognitive Search.
Why it's wrong here
Serverless may have cold-start latency and is not ideal for frequent queries; also introduces more complexity.
- ✗
Use the prebuilt question-answering model from Azure AI Language with a custom answer list.
Why it's wrong here
Prebuilt models cannot be customized with internal documents; they only answer from a predefined FAQ.
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 |
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This AI-102 question is part of Courseiva's 945-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 by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI-102 practice question is part of Courseiva's free Microsoft 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 AI-102 exam.