The correct choice is to deploy an Azure AI services multi-service resource. This resource type provides a single endpoint and key that can be shared across multiple AI services like Computer Vision and Language, which directly addresses the goal of reducing costs by consolidating billing and simplifying credential management. On the AI-102 exam, this scenario tests your understanding of resource provisioning options, specifically contrasting the multi-service resource with single-service instances—a common trap is assuming you must deploy separate resources for each API. Remember, when you need to share a key and endpoint across services, think "multi-service" as your single point of access. A handy memory tip: "One key to rule them all" for multi-service, versus "many keys for many tasks" for single-service deployments.
AI-102 Plan and manage an Azure AI solution Practice Question
This AI-102 practice question tests your understanding of plan and manage an azure ai solution. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Your Azure AI solution uses multiple AI services including Computer Vision and Language. To reduce costs, you want to share a single key and endpoint across services. Which Azure resource type should you deploy?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Azure AI services multi-service resource
D is correct because Azure AI services multi-service resource provides a single endpoint and key that can be used across multiple AI services (e.g., Computer Vision, Language, Face, etc.), reducing management overhead and cost by consolidating billing. This resource type is designed specifically for scenarios where you want to share credentials across services without deploying separate single-service instances.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Separate single-service resources for each AI service
Why it's wrong here
Separate resources require individual keys and endpoints, increasing cost.
✗
Azure Key Vault for storing keys
Why it's wrong here
Key Vault does not consolidate endpoints.
✗
Azure API Management gateway
Why it's wrong here
API Management can manage APIs but is not a cost-saving resource for AI services.
✓
Azure AI services multi-service resource
Why this is correct
A multi-service resource provides a single key and endpoint for multiple AI services.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse Azure API Management (Option C) as a way to share endpoints, but it is a gateway for API management, not a shared AI resource; the correct answer is the multi-service resource that natively provides a single key and endpoint for multiple AI services.
Detailed technical explanation
How to think about this question
Under the hood, the Azure AI services multi-service resource (formerly known as Cognitive Services multi-service account) uses a single regional endpoint (e.g., https://<your-resource-name>.cognitiveservices.azure.com/) and a single key that authenticates requests to any supported AI service within that resource. This is achieved via a unified authentication mechanism where the key is validated against the resource's subscription, and the service type is determined by the request path (e.g., /vision/v3.2/analyze for Computer Vision or /text/analytics/v3.1/sentiment for Language). In real-world scenarios, this simplifies client-side configuration and reduces the number of keys to rotate, but note that not all AI services are supported (e.g., Custom Vision, QnA Maker require separate resources).
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Plan and manage an Azure AI solution — This question tests Plan and manage an Azure AI solution — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Azure AI services multi-service resource — D is correct because Azure AI services multi-service resource provides a single endpoint and key that can be used across multiple AI services (e.g., Computer Vision, Language, Face, etc.), reducing management overhead and cost by consolidating billing. This resource type is designed specifically for scenarios where you want to share credentials across services without deploying separate single-service instances.
What should I do if I get this AI-102 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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