The answer is to deploy the Azure AI Vision resource in the desired Azure region. This is correct because Azure AI Vision resources are regional Azure services, meaning all data processing and storage—including image analysis and any derived metadata—are confined to the specific Azure region where the resource is provisioned. By selecting the appropriate region during deployment, you enforce data residency compliance by ensuring that no data leaves that geographic boundary. On the Microsoft Azure AI Engineer Associate AI-102 exam, this concept tests your understanding that regional deployment is the primary mechanism for data residency in Azure AI services, not network configurations or encryption settings. A common trap is assuming data can be routed to a different region for processing while still maintaining residency; in reality, the resource’s home region dictates where all computation occurs. Memory tip: think “Region = Residence” — the region you choose is the only place your data lives.
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. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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.
A company is deploying a solution using Azure AI Vision to analyze images of products on a retail website. They need to ensure that the image analysis is performed within a specific geographic boundary for data residency compliance. What should they configure?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Deploy the Azure AI Vision resource in the desired Azure region
Azure AI Vision resources are regional Azure resources, meaning all data processing and storage occur within the Azure region where the resource is deployed. By deploying the resource in the desired geographic region, you ensure that image analysis and any derived data remain within that boundary, satisfying data residency compliance requirements. This is the fundamental mechanism for controlling data location in Azure AI services.
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.
✓
Deploy the Azure AI Vision resource in the desired Azure region
Why this is correct
Data stays in the region where the resource is deployed.
Related concept
Read the scenario before looking for a memorised answer.
✗
Create a private endpoint for the Vision resource
Why it's wrong here
Private endpoint controls network access, not data residency.
✗
Enable multi-region replication on the Vision resource
Why it's wrong here
Vision does not support multi-region replication.
✗
Use the Free tier of Azure AI Vision
Why it's wrong here
Free tier may not provide regional control.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse network isolation (private endpoints) with data residency, or assume that replication or tier changes can alter where data is stored, when in fact the region of the resource itself is the sole determinant for compliance.
Detailed technical explanation
How to think about this question
Azure AI Vision uses the regional endpoint (e.g., https://westus.api.cognitive.microsoft.com) for all API calls, and the service processes images and stores any extracted metadata exclusively within that region's data centers. For compliance scenarios, you must also consider that some features (like Optical Character Recognition) may temporarily cache data in the same region for performance, but the regional deployment is the primary control. If you need to ensure data never leaves a specific geography, you should also disable any cross-region training or logging features that might use Azure's global infrastructure.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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: Deploy the Azure AI Vision resource in the desired Azure region — Azure AI Vision resources are regional Azure resources, meaning all data processing and storage occur within the Azure region where the resource is deployed. By deploying the resource in the desired geographic region, you ensure that image analysis and any derived data remain within that boundary, satisfying data residency compliance requirements. This is the fundamental mechanism for controlling data location in Azure AI services.
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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