AI-102 Practice Test 27 — 15 Questions

Question 1

A company uses Azure AI Search to index customer support tickets. They need to automatically extract key phrases from each ticket to improve search relevance. Which built-in skill should they add to the skillset?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Key Phrase Extraction. The Key Phrase Extraction skill in Azure AI Search is a built-in cognitive skill that uses natural language processing to extract key phrases from text. It is designed exactly for scenarios like extracting important terms from support tickets to improve search relevance and indexing.

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Entity Recognition

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Sentiment Analysis

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OCR

Question 2

You work for a manufacturing company that uses Azure AI services to automate quality inspection on a production line. You have a Custom Vision object detection model that identifies defects on metal parts. The model was trained on images captured under ideal lighting conditions. However, when deployed in the factory, the model's accuracy drops significantly due to inconsistent lighting and glare. You need to improve the model's robustness without collecting new images from the factory floor. What should you do?

Accepted Answer

Apply data augmentation techniques such as brightness, contrast, and blur adjustments to the existing training images.. Using data augmentation techniques like brightness and contrast adjustments, rotation, and noise injection can simulate various lighting conditions and improve robustness. Option A is wrong because increasing training iterations may overfit to the existing data. Option C is wrong because higher resolution does not address lighting variation. Option D is wrong because changing the model type does not address the data issue.

Answer

Increase the number of training iterations to force the model to learn more features.

Answer

Use higher resolution images for training.

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Change the model type from object detection to classification.

Question 3

A developer uses the Azure OpenAI API to generate code. They want to ensure that the generated code is in Python. Which parameter should they set?

Accepted Answer

system message. The system message is used to set the behavior and context of the AI model, including specifying the desired output format or language. By setting the system message to 'You are a helpful assistant that always writes code in Python', the developer can instruct the model to generate Python code consistently. This parameter is part of the chat completions API and directly influences the model's persona and constraints.

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temperature

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top_p

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max_tokens

Question 4

Refer to the exhibit. You submit this request to Azure AI Language's conversational language understanding (CLU) for the 'FlightBooking' project. The model correctly identifies the intent as 'BookFlight' and extracts entities: 'Seattle' as FromCity, 'New York' as ToCity, and 'June 15th' as Date. What is the next step for the application?

Accepted Answer

Call a separate booking API with the extracted entities to complete the reservation.. After CLU extracts the intent and entities, the application must use those entities to call a separate booking API to complete the reservation. CLU itself does not perform bookings; it only provides language understanding. Option B is incorrect because the CLU response cannot directly book a flight. Option C is incorrect because the intent is already clearly identified as 'BookFlight' and entities are extracted, so no rephrasing is needed. Option D is incorrect because sending another request to CLU would not confirm booking; confirmation is handled by the booking API.

Answer

Use the CLU response to directly book the flight via the Azure AI Language service.

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Prompt the user to rephrase the request because the intent is ambiguous.

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Send another request to CLU to confirm the booking details.

Question 5

You are planning to use Azure AI Content Safety to moderate user-generated content in a social media application. The solution must detect hate speech and self-harm content. Which Content Safety features should you enable?

Accepted Answer

Hate and self-harm content filters. Azure AI Content Safety provides pre-built filters for specific harm categories, including hate speech and self-harm. Enabling the 'Hate and self-harm content filters' directly activates the detection models for these categories, meeting the requirement without needing custom categories or additional features.

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Severity levels for all categories

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Custom categories for hate speech and self-harm

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Image moderation

Question 6

You are a machine learning engineer at a large retail company. The company has thousands of product descriptions that need to be updated regularly. They currently use a manual process. You propose using Azure OpenAI to generate new descriptions based on product attributes. You have a dataset of existing product descriptions and attributes stored in an Azure SQL Database. The solution must be cost-effective, scalable, and must not require retraining the model. You need to design the solution. You have the following options:

Option A: Use Azure OpenAI with few-shot learning by including examples in the prompt for each product. Deploy the model on an Azure Kubernetes Service (AKS) cluster for high throughput.

Option B: Use Azure OpenAI with prompt templates that include product attributes and call the API for each product. Use Azure Logic Apps to orchestrate the workflow and store results back to Azure SQL Database.

Option C: Fine-tune a custom model on the existing product descriptions and deploy it as a managed endpoint. Use Azure Data Factory to batch process all products.

Option D: Use Azure OpenAI with the batch API to generate descriptions for all products at once, using a single prompt that lists all products and attributes. Store the batch output in Azure Blob Storage and then import into Azure SQL Database.

Which option should you choose?

Accepted Answer

Option B. (Azure Logic Apps) is the correct choice. It uses Azure OpenAI with prompt templates that insert product attributes, making individual API calls per product. This approach is scalable because Azure Logic Apps can handle high volumes with built-in retry and concurrency, and it is cost-effective as you only pay per API call and execution. It does not require model retraining. In contrast, Option A (AKS) introduces unnecessary infrastructure complexity; Option C (fine-tuning) requires retraining; and Option D (batch API) risks exceeding prompt size limits and is less suitable for incremental updates.

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Option C

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Option D

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Option A

Question 7

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 8

You are a security engineer for a financial services company. The company uses Azure AI Language to analyze customer communications for compliance. The solution processes sensitive personal data. You need to ensure that all data transmitted to the Azure AI Language service is encrypted in transit and that the service endpoint is not accessible from the public internet. Additionally, you must use Microsoft Entra ID for authentication. The current implementation uses API keys and the public endpoint. You need to reconfigure the solution. What should you do?

Accepted Answer

Disable the public network access, configure a private endpoint, enable managed identity, and enforce HTTPS. It addresses all three requirements: disabling public network access removes internet exposure, configuring a private endpoint ensures traffic stays within the Azure backbone and your virtual network, enabling managed identity allows Microsoft Entra ID authentication without API keys, and enforcing HTTPS guarantees encryption in transit via TLS. This combination fully secures the Azure AI Language service for sensitive personal data.

Answer

Configure a private endpoint and continue using the public endpoint for redundancy

Answer

Disable the public network access without configuring a private endpoint

Answer

Enable Microsoft Entra ID authentication but keep the public endpoint and API keys

Question 9

You are building a custom text classification solution in Azure AI Language. You have a dataset with 10 categories and 1000 labeled documents. You need to choose the best project type. What should you use?

Accepted Answer

Custom text classification (single or multi-label). Custom text classification (single or multi-label) is the correct project type because you have a labeled dataset with 10 categories and need to train a model to classify text into those specific categories. Azure AI Language provides a custom text classification feature that allows you to train a model using your own labeled data, supporting both single-label and multi-label classification scenarios. This is the only option that enables you to build a bespoke classifier tailored to your 10-category dataset.

Answer

Conversational Language Understanding (CLU)

Answer

Key Phrase Extraction

Answer

Prebuilt Text Classification API

Question 10

You are deploying a generative AI model using Azure AI Foundry. The model must be accessible only from a specific virtual network. Additionally, you need to monitor all API calls for auditing. Which two configurations are required? (Choose two.)

Accepted Answer

Enable diagnostic settings to send logs to a Log Analytics workspace.. Enabling diagnostic settings to send logs to a Log Analytics workspace captures all API call details (e.g., request URI, response status, caller IP) for auditing and monitoring. This is the standard Azure method for collecting resource-level logs, and it works with Azure AI Foundry deployments to meet compliance and security requirements.

Answer

Assign a managed identity to the model deployment.

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Enable public network access from selected IP addresses.

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Configure CORS to allow only the VNet's domain.

Question 11

A company uses Microsoft Copilot Studio to create an agent that helps employees schedule meetings. The agent must access the user's calendar to find free time slots and book meetings. The agent should only work for users who have granted consent. Which authentication and authorization approach should be used?

Accepted Answer

Configure OAuth 2.0 authentication with Microsoft Entra ID and request delegated permissions for Microsoft Graph.. The agent needs to act on behalf of a signed-in user (delegated identity) to access their calendar. OAuth 2.0 with Microsoft Entra ID and delegated permissions for Microsoft Graph allows the agent to request only the scopes (e.g., Calendars.ReadWrite) that the user has consented to, ensuring the agent operates within the user's granted permissions.

Answer

Use certificate-based authentication for the agent.

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Use API key authentication to call Microsoft Graph.

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Use OAuth 2.0 client credentials flow with application permissions.

Question 12

Your team is using Azure AI Search to index a large collection of technical manuals. Users report that searches for 'disk failure' do not return relevant results because the manuals use terms like 'hard drive crash'. Which feature should you implement to improve recall?

Accepted Answer

Add a synonym map to the index. A synonym map in Azure AI Search allows you to define equivalent terms (e.g., 'disk failure' = 'hard drive crash') so that queries automatically expand to include synonyms. This directly addresses the vocabulary mismatch between user queries and indexed content, improving recall without requiring changes to the documents or queries.

Answer

Apply a filter

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Configure a scoring profile

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Enable semantic search

Question 13

A company wants to moderate user-generated images for adult content. Which Azure AI Vision feature should they use?

Accepted Answer

Analyze Image API with moderation categories. The Analyze Image API in Azure AI Vision includes built-in moderation categories for detecting adult, racy, and gory content in images. This feature is specifically designed for content moderation without requiring custom training, making it the correct choice for moderating user-generated images for adult content.

Answer

Custom Vision with a custom adult classifier

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Face API

Answer

OCR

Question 14

A company is using Azure AI Vision to analyze images from a manufacturing line. The solution must detect defects in real-time. The team discovers that the model's accuracy drops significantly when images are captured under different lighting conditions. What is the best approach to improve the model's robustness?

Accepted Answer

Retrain the model using images captured under various lighting conditions, using data augmentation.. The accuracy drop is caused by a domain shift: the model was trained on images with limited lighting variation but is deployed under diverse lighting. Retraining with images captured under various lighting conditions and applying data augmentation (brightness, contrast, exposure jitter) teaches the model to be invariant to those variations, directly improving robustness. This addresses the root cause rather than masking it at inference time.

Answer

Apply image pre-processing to normalize lighting before sending to the model.

Answer

Increase the number of training images without varying lighting conditions.

Answer

Use a pre-built model from Azure AI Vision instead of a custom model.

Question 15

Drag and drop the steps to set up Azure AI Content Safety for content moderation into the correct order.

Accepted Answer

1. Create Content Safety resource, 2. Get endpoint and key, 3. Call Content Safety API, 4. Analyze response, 5. Act on results. The correct order for setting up Azure AI Content Safety is: first create the Content Safety resource, then obtain its endpoint and key, call the Content Safety API with the content, analyze the response for moderation results, and finally act on the analysis (e.g., block or flag content). This sequence ensures proper authentication and logical flow from setup to action.

Answer

1. Get endpoint and key, 2. Create Content Safety resource, 3. Call Content Safety API, 4. Analyze response, 5. Act on results

Answer

1. Create Content Safety resource, 2. Get endpoint and key, 3. Call Content Safety API, 4. Act on results, 5. Analyze response

Answer

1. Call Content Safety API, 2. Analyze response, 3. Create Content Safety resource, 4. Get endpoint and key, 5. Act on results