AI-102 Plan and manage an Azure AI solution Practice Test 13 — 15 Questions

Question 1

You deploy a custom vision model using Azure AI Custom Vision. After deployment, you notice the model has high accuracy on training data but low accuracy on new images. What is the most likely cause?

Accepted Answer

The model is overfitted to the training data. High accuracy on training data but low accuracy on new images is the classic symptom of overfitting, where the model has memorized the training examples (including noise and irrelevant patterns) rather than learning generalizable features. In Azure AI Custom Vision, this typically occurs when the training dataset is too small, too homogeneous, or lacks sufficient variation, causing the model to fail on unseen data.

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The training time was too short

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The training dataset has too few images

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The wrong domain was selected during training

Question 2

Refer to the exhibit. You are configuring an agent in Azure AI Foundry. The agent fails to start because the specified model is not available in the current Azure OpenAI resource. What should you do to resolve the issue?

Accepted Answer

Deploy the gpt-4-0613 model in the Azure OpenAI resource. The agent fails to start because the specified model (likely gpt-4-0613) is not deployed in the Azure OpenAI resource. In Azure AI Foundry, agents require an existing model deployment to invoke; you cannot use a model that hasn't been deployed. Option B correctly resolves this by deploying the required model in the Azure OpenAI resource.

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Modify the system_prompt to include the model version

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Change the connection_type to 'Weak'

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Change the provider to 'AzureAI'

Question 3

You are designing an Azure AI solution that uses Azure AI Language to analyze customer support transcripts. The solution must identify key phrases, detect sentiment, and extract custom entities specific to your product catalog. Which two Azure AI Language features should you enable?

Accepted Answer

Key Phrase Extraction. Key Phrase Extraction (Option B) is correct because it identifies the main points and important terms in customer support transcripts, such as 'refund request' or 'account issue,' which directly supports analyzing the content. Custom Entity Extraction (Option D) is correct because it allows you to define and extract domain-specific entities from your product catalog, such as product names or model numbers, using a trained custom entity extraction model. Together, these two features enable both general insight extraction and tailored, product-specific data extraction from the transcripts.

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PII Detection

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Summarization

Question 4

Which TWO of the following are best practices for securing Azure AI services?

Accepted Answer

Enable diagnostic settings to audit usage and detect anomalies.. Option C is correct because enabling diagnostic settings on Azure AI services streams resource logs and metrics to destinations such as Log Analytics, Azure Storage, or Event Hubs, which supports auditing usage, monitoring for anomalies, and meeting compliance requirements. Option E is correct because managed identities let applications authenticate to Azure AI services via Microsoft Entra ID tokens, eliminating the need to store or rotate API keys in code or configuration. Option A is incorrect since publicly exposing endpoints increases the attack surface; access should be restricted with private endpoints, network ACLs, or Azure Private Link. Option B is incorrect because disabling diagnostic logging removes the audit trail needed to detect misuse and investigate incidents. Option D is incorrect because sharing API keys across applications prevents per-app revocation and least-privilege scoping, so keys should be unique, stored in Key Vault, and rotated regularly.

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Expose endpoints publicly to simplify client access.

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Disable diagnostic logging to reduce data exposure.

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Share API keys among multiple applications for simplicity.

Question 5

Which THREE are required when planning to use Azure OpenAI Service for a generative AI application that must comply with responsible AI principles?

Accepted Answer

Implement content filters to block harmful outputs.. Option B is correct because Azure OpenAI Service provides configurable content filters (categories such as hate, violence, sexual, and self-harm, with severity thresholds) that are a core responsible AI control for blocking harmful model outputs. Option C is correct because human-in-the-loop review is a responsible AI requirement for high-impact or critical decisions, ensuring a human validates or overrides model output before it affects users. Option E is correct because data governance policies for training data address privacy, consent, provenance, and bias concerns, which are foundational to responsible AI compliance. Option A is not required: a 1000-token cap is an arbitrary cost/latency constraint, not a responsible AI principle, and token limits are set per model/deployment as needed. Option D is not required: rate limiting is an availability and abuse-mitigation control, not a responsible AI requirement, and it does not by itself ensure fair, safe, or accountable AI behavior.

Answer

Restrict the model to a maximum of 1000 tokens.

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Enable rate limiting to prevent abuse.

Question 6

Refer to the exhibit. You are reviewing a Bicep template for deploying an Azure AI Language resource. After deployment, you need to ensure that the resource uses a private endpoint to block public access. Which additional resource should you include in the template?

Accepted Answer

A Private Endpoint resource linked to the Cognitive Services account. A Private Endpoint resource, when linked to the Cognitive Services account via the `privateLinkServiceId` property, assigns a private IP address from a virtual network to the Azure AI Language resource. This blocks all public access by default when the resource's `publicNetworkAccess` property is set to 'Disabled', ensuring traffic only flows over the Microsoft backbone network through Azure Private Link.

Answer

A service endpoint for Microsoft.CognitiveServices

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A virtual network peering connection

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A virtual network gateway

Question 7

You are planning to use Azure AI Vision to analyze images for a retail inventory management application. The solution must detect products on shelves and read expiration dates. Which two Azure AI Vision capabilities should you use?

Accepted Answer

Object Detection. Object Detection (B) is correct because it identifies and locates products on shelves by drawing bounding boxes around each detected item, which is essential for inventory tracking. Optical Character Recognition (OCR) (D) is correct because it extracts text from images, enabling the reading of expiration dates printed on product labels or packaging.

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Image Captioning

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Face Detection

Question 8

Your organization is migrating on-premises machine learning models to Azure. The models are used for real-time inference. You need to choose a service that provides managed endpoints with autoscaling and supports custom containers. Which service should you use?

Accepted Answer

Azure Machine Learning managed online endpoints. Azure Machine Learning managed online endpoints are the correct choice because they provide fully managed, autoscaling endpoints specifically designed for real-time inference. They support custom container images, allowing you to deploy any model packaged as a Docker container, and handle traffic splitting, health checks, and scaling automatically without managing underlying infrastructure.

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Azure Functions

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Azure Kubernetes Service (AKS) with manual scaling

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Azure AI Services custom vision

Question 9

You are developing a chatbot that uses Azure AI Language to understand user intents. The chatbot must handle multiple languages and direct users to the appropriate support team based on the detected intent. Which Azure AI Language feature should you use?

Accepted Answer

Conversational language understanding (CLU). Conversational language understanding (CLU) is the correct feature because it is specifically designed to extract intents and entities from user utterances in a multi-language conversational context. CLU enables the chatbot to detect the user's intent (e.g., 'billing', 'technical support') and route them to the appropriate support team, while also supporting multiple languages through its language-agnostic model training and per-language project configurations.

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Text Analytics

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QnA Maker

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Translator

Question 10

You are deploying an Azure AI Document Intelligence solution to process invoices. The solution must extract line-item details such as product code, quantity, and unit price. Which prebuilt model should you use?

Accepted Answer

prebuilt-invoice. The prebuilt-invoice model is specifically designed to extract line-item details such as product code, quantity, and unit price from invoices. It uses deep learning models trained on thousands of invoice samples to identify and extract structured data, including tables and line items, making it the correct choice for this requirement.

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prebuilt-receipt

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prebuilt-idDocument

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prebuilt-layout

Question 11

Your Azure AI Search solution uses a custom skill to call an external API. The skill runs locally but fails when deployed to the search service. What is the most likely cause?

Accepted Answer

The skill endpoint is not publicly accessible via HTTPS.. When a custom skill runs locally but fails after deployment to Azure AI Search, the most common cause is that the skill's endpoint is not publicly accessible via HTTPS. Azure AI Search indexers execute skills in the cloud and must be able to reach the external API over the internet using a secure HTTPS connection; localhost or HTTP endpoints will fail.

Answer

The skill's output field mappings are missing.

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The skill's input field mappings are incorrect.

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The indexer name is misspelled in the skillset.

Question 12

You are designing a chatbot using Azure AI Language. The chatbot must understand user intents and also extract entities like dates and locations. Which feature combination should you use?

Accepted Answer

Conversational Language Understanding (CLU) with entities. Conversational Language Understanding (CLU) is the correct Azure AI Language feature for building a chatbot that understands user intents and extracts entities like dates and locations. CLU is specifically designed for natural language understanding (NLU) tasks, providing prebuilt and custom entity extraction alongside intent recognition, which directly matches the requirement.

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Sentiment analysis and entity linking

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Custom text classification and key phrase extraction

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Orchestration Workflow and custom text classification

Question 13

You need to enforce that only users from your Microsoft Entra ID tenant can call your Azure AI Language API endpoint. Which security mechanism should you configure?

Accepted Answer

Microsoft Entra ID authentication. Microsoft Entra ID authentication (formerly Azure AD) allows you to enforce that only users and applications from your specific Entra ID tenant can call the Azure AI Language API. This is achieved by configuring a managed identity or service principal and assigning it the Cognitive Services User role, which ensures that tokens issued by your tenant are required for access. API keys and IP whitelists do not provide tenant-level identity enforcement, and Azure Firewall is a network-level control that does not authenticate individual users or applications.

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API key authentication

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IP whitelist

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Azure Firewall

Question 14

Your Azure AI Document Intelligence model is failing to extract tables from scanned PDFs. The PDFs are low-quality images. What should you do first?

Accepted Answer

Verify that the Read OCR step is extracting text correctly.. The Read OCR step is the foundational layer for table extraction in Azure AI Document Intelligence. If the OCR cannot accurately recognize text from low-quality images, subsequent table extraction models will fail regardless of training or image enhancement. Verifying OCR output first isolates whether the issue is at the text recognition stage or the table parsing stage, following a systematic troubleshooting approach.

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Use Azure AI Computer Vision to enhance the image.

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Retrain the model with more table examples.

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Use a higher resolution scanner for input PDFs.

Question 15

You plan to use Azure AI Content Safety to detect hate speech in user-generated content. Which type of content safety is most appropriate for this scenario?

Accepted Answer

Text moderation. Text moderation is the correct choice because Azure AI Content Safety's text moderation API is specifically designed to detect and filter hate speech, along with other harmful content categories like violence and self-harm, in user-generated text. It uses machine learning classifiers trained on a vast corpus to assign severity scores across predefined categories, making it the direct and most appropriate tool for this scenario.

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Custom categories

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Image moderation

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Prompt Shields