AI-102 Plan and manage an Azure AI solution Practice Question
You are designing an Azure AI solution that uses multiple Azure AI services, including Azure AI Vision and Azure AI Language. You need to ensure that the solution can be deployed in a way that minimizes latency between services and provides a single endpoint for management. What should you use?
⚠ Common exam trap
The trap here is assuming that using API Management or private endpoints alone can achieve a single endpoint and minimal latency, when actually a multi-service resource is the native solution for co-located services with unified management.
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 single Azure AI Services multi-service resource that includes both Vision and Language capabilities.
A multi-service Azure AI Services resource bundles multiple AI capabilities into a single Azure resource with one endpoint and key. Deploying it in one region ensures all services are co-located, reducing latency. It also simplifies management by consolidating billing and access control, meeting both requirements.
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 single Azure AI Services resource for each service and use private endpoints to connect them.
Why it's wrong here
Private endpoints provide secure connectivity but do not co-locate services or provide a single endpoint for management. Each service remains a separate resource with its own endpoint and key. This approach also adds complexity and does not inherently minimize latency unless the services are in the same region, which is not specified.
- ✗
Deploy each service as a separate resource in the same region and use Azure API Management to aggregate them.
Why it's wrong here
While deploying in the same region reduces latency, using Azure API Management adds an extra hop and does not eliminate the need to manage multiple resources. It also does not provide a single endpoint for the services natively; API Management would be an additional layer. This increases complexity and may introduce latency.
- ✓
Deploy a single Azure AI Services multi-service resource that includes both Vision and Language capabilities.
Why this is correct
An Azure AI Services multi-service resource allows you to access multiple AI services, such as Vision and Language, through a single endpoint and key. Deploying this resource in one region ensures that all services are co-located, minimizing network latency between them. It also simplifies management by providing a single resource for billing and access control.
- ✗
Deploy each service as a separate resource in different regions and use Azure Front Door to route requests.
Why it's wrong here
Deploying services in different regions increases latency between them because inter-service communication must traverse the public internet or Azure backbone across regions. Azure Front Door can route external requests but does not reduce internal latency between services. This approach also does not provide a single management endpoint for the services themselves.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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