AI-103 Practice Test 8 — 10 Questions

Question 1

You need to ensure that your agent is compliant with regional data residency requirements. Where should you check this?

Accepted Answer

In the Azure AI Foundry resource's region settings.. Data residency for Azure AI Foundry is governed by the region of the Azure AI Foundry resource (and its dependent resources such as the model deployment and storage). To verify compliance with regional data residency requirements, you check the resource's region settings, because that determines where data is stored and processed. Content safety policies, prompt flow runtime, and model configuration do not control geographic placement.

Answer

In the Prompt Flow runtime.

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In the Content Safety policy.

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In the model configuration settings.

Question 2

You want to extract gender and age attributes from human faces in photos. While Azure AI Face service handles facial analysis, which base Azure AI Vision 4.0 feature detects the presence and bounding boxes of people?

Accepted Answer

features=people. In Azure AI Vision 4.0 image analysis, the 'people' feature detects human presence and returns bounding boxes for each person in the image, which is the base capability you need before applying Face service for gender/age attributes. The 'objects' feature detects common objects (not specifically people), and 'tags'/'read' serve different purposes. So 'features=people' is the correct query parameter.

Answer

features=objects

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features=tags

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features=read

Question 3

You are training a custom text classification model in Language Studio. After reviewing your trained model's performance metrics, you decide to improve recall for a specific underperforming category. What action should you take?

Accepted Answer

Add more varied training examples for that category to the dataset and retrain the model.. To improve recall for an underperforming category in custom text classification, you should add more varied training examples for that category and retrain the model. Recall measures the model's ability to find all relevant instances, so more diverse examples help the model generalize better. This is a standard approach in machine learning.

Answer

Increase the confidence threshold parameter in your client application code.

Answer

Delete all training documents for that category.

Answer

Switch the classification type from multi-label to single-label.

Question 4

Your application uploads an image to Azure AI Vision for analysis. You need to generate thumbnail images with specific aspect ratios while maintaining the primary region of interest. Which API endpoint or feature should you use?

Accepted Answer

Call the Get Thumbnail API passing width, height, and smartCropping parameters.. The Get Thumbnail API in Azure AI Vision is specifically designed to generate thumbnails with a specified width and height, and it supports a smartCropping parameter. When smartCropping is set to true, the service analyzes the image to identify the primary region of interest (e.g., faces, salient objects) and crops the image to that region while preserving the requested aspect ratio. This directly fulfills the requirement without needing custom models or local processing.

Answer

Use Custom Vision Training API to train a cropping model.

Answer

Call the Analyze Image API with features=objects and crop the image locally.

Answer

Use Azure AI Document Intelligence layout analysis.

Question 5

You are analyzing performance metrics of a Custom Vision object detection iteration. You observe a Mean Average Precision (mAP) of 88%, but when testing with real-world user photos, the model fails to detect objects that are partially occluded. What should you do to improve occlusion handling?

Accepted Answer

Add training images containing partially occluded objects and ensure they are correctly tagged.. Occlusion handling is a data problem: if the training set lacks examples of partially occluded objects, the model never learns to recognize them. Adding training images that contain partially occluded instances of the target class and tagging them with accurate bounding boxes teaches the model to detect objects even when parts are hidden, improving real-world performance.

Answer

Switch from General domain to Food domain.

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Lower the confidence threshold to 0.01.

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Increase image compression artifacts.

Question 6

You are evaluating a Custom Vision classification model. You want to programmatically test the model against a validation dataset without using the portal UI. Which endpoint should your script call?

Accepted Answer

POST /customvision/v3.3/Prediction/{projectId}/classify/iterations/{publishedName}/image. To programmatically test a published Custom Vision classification model, you call the prediction endpoint POST /customvision/v3.3/Prediction/{projectId}/classify/iterations/{publishedName}/image. This endpoint accepts an image and returns the predicted tags with confidence scores for the specified published iteration. It is the correct REST path for classification predictions outside the portal.

Answer

POST /computervision/imageanalysis:analyze

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POST /customvision/v3.3/Training/{projectId}/images

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GET /vision/v3.2/models

Question 7

You are managing a multi-tenant SaaS application that uses Azure AI Vision. Each tenant has their own isolated data, and you want to track costs per tenant. How should you provision Azure AI services to achieve granular cost attribution?

Accepted Answer

Deploy a dedicated Azure AI Services resource per tenant and use Azure Cost Management for billing attribution.. Deploying a dedicated Azure AI Services resource per tenant is the only approach that provides true isolation and granular cost attribution, because Azure Cost Management tracks costs at the resource level. Each tenant's resource appears as a separate line item in cost analysis, enabling per-tenant billing. This also satisfies the data-isolation requirement mentioned in the scenario.

Answer

Use a single global Azure AI Services resource and parse tenant IDs from request user-agent headers for billing.

Answer

Store tenant IDs in Custom Vision project tags.

Answer

Use Azure Blob Storage cost allocation tags to cover AI API costs.

Question 8

You are implementing a grounded RAG solution in Azure AI Foundry. Users report that the model occasionally hallucinates answers even when relevant documents are retrieved by Azure AI Search. What is the most effective prompt engineering strategy to mitigate this grounding failure?

Accepted Answer

Add strict prompt instructions stating that the model must rely solely on the retrieved context chunks and explicitly output a refusal statement if the answer is not present.. The most effective prompt engineering mitigation for grounding failures is to explicitly instruct the model to answer only from the retrieved context and to refuse when the answer is absent. This constrains the model's generation to the provided evidence and reduces hallucination by giving it a clear escape hatch rather than forcing an answer.

Answer

Remove the system message entirely so the model can freely blend internal training weights with search results.

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Increase the temperature parameter to 1.5 to encourage creative synthesis of the retrieved data.

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Disable chunking in Azure AI Search so the entire document is passed as a single massive token block.

Question 9

You are conducting a security review of an Azure AI Search service and Azure OpenAI integration. You discover potential security misconfigurations. Which THREE remediation actions should you take to align with Azure security benchmarks? Each correct answer presents part of the solution.

Accepted Answer

Enable diagnostic logging and configure alerts for unauthorized access attempts and HTTP 401/403 errors.. Option B is correct because enabling diagnostic logging and configuring alerts for unauthorized access attempts and HTTP 401/403 errors provides the audit trail and detection capability required by Azure security benchmarks to identify and respond to authentication and authorization failures against Azure AI Search and Azure OpenAI. Option C is correct because disabling public network access once private endpoints are fully implemented enforces network isolation, ensuring traffic reaches the services only through the private endpoint (private link) rather than the public internet, which is a core Azure security benchmark recommendation. Option E is correct because rotating static API keys regularly limits the blast radius of leaked keys, and transitioning clients to Microsoft Entra ID managed identities eliminates long-lived shared secrets entirely by using token-based, identity-driven authentication. Option A does not belong because granting Contributor to anonymous external users violates least privilege and exposes the services to unauthorized modification. Option D does not belong because embedding admin API keys in client-side single-page applications exposes full administrative credentials to anyone inspecting the browser, which is a critical secret-management anti-pattern.

Answer

Grant Contributor permissions to anonymous external users.

Answer

Expose admin API keys in client-side single-page applications for direct access.

Question 10

When establishing a governance strategy for Azure AI resources across multiple cloud teams, which THREE administrative tasks should an administrator perform? (Choose three.)

Accepted Answer

Apply Role-Based Access Control (RBAC) following the principle of least privilege.. Option A is correct because applying Role-Based Access Control (RBAC) with least privilege ensures each cloud team member receives only the permissions needed for their role, such as Reader, Contributor, or specific custom roles, reducing the blast radius of misconfiguration or compromise across Azure AI resources. Option B is correct because Azure Policy can enforce organizational standards such as naming conventions and allowed regions by evaluating resource properties during creation and updates, and it can deny noncompliant deployments or flag them for remediation, which is essential for consistent multi-team governance. Option C is correct because organizing subscriptions under Azure Management Groups creates a hierarchy where policies and RBAC assignments inherit down to child subscriptions, giving administrators a single governance control plane across multiple cloud teams without configuring each subscription individually. Option D is incorrect because disabling Azure Activity Logs would remove the administrative audit trail needed for compliance, security monitoring, and incident investigation, directly undermining governance. Option E is incorrect because assigning the Owner role to all developers violates least privilege and grants excessive control over access and billing, increasing risk rather than establishing sound governance.

Answer

Disable Azure Activity Logs to prevent administrative audit trails.

Answer

Assign the Owner role to all developers to streamline resource creation.