AI-102 Practice Test 23 — 15 Questions

Question 1

A retail company uses Azure Computer Vision to analyze in-store camera feeds. They recently added a new product line and updated the object detection model. However, the model fails to detect the new products. What should the company do first?

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

Accepted Answer

Retrain the custom object detection model with images of the new products.. The model fails to detect new products because it was never trained on them. Retraining the custom object detection model with labeled images of the new products is the correct first step, as it updates the model's knowledge to recognize the new product line. Pre-built models or threshold adjustments cannot add new object classes.

Answer

Use the pre-built 'products' model from Computer Vision.

Answer

Increase the confidence threshold in the API call.

Answer

Recreate the Computer Vision resource in a different region.

Question 2

Your organization is using Azure AI Document Intelligence to process a mix of invoices and purchase orders. You need to ensure that documents are correctly classified before extraction. Which THREE steps should you take?

Accepted Answer

Create a custom classification model in Document Intelligence. Option B is correct because a custom classification model in Azure AI Document Intelligence is the required resource for identifying document type before extraction, since prebuilt models extract fields but do not perform custom document classification. Option C is correct because training a custom classification model requires a minimum of 5 labeled samples per document type, which provides the model with enough examples to distinguish invoices from purchase orders. Option D is correct because after classification, you must chain the classification model with the appropriate extraction models so each document is routed to the correct extraction model for field retrieval. Option A is incorrect because one sample per type is insufficient; the minimum is 5 labeled samples per document type. Option E is incorrect because prebuilt invoice and purchase order models are extraction models, not classification models, and they do not classify documents before extraction.

Answer

Train the classification model with one sample per type

Answer

Use the prebuilt invoice and purchase order models for classification

Question 3

You are planning to deploy an Azure AI Content Safety solution. What is the primary requirement for using the service?

Accepted Answer

An active Azure subscription. An active Azure subscription is the primary requirement because Azure AI Content Safety is a cloud-based service that requires a valid subscription for resource provisioning, API access, and billing. Without an Azure subscription, you cannot create the Content Safety resource or authenticate API calls, regardless of other configurations.

Answer

Data must be stored in the same region as the service

Answer

All users must have Microsoft Entra ID P2 licenses

Answer

The application must be written in C#

Question 4

Which THREE factors should be considered when choosing between Azure AI Language's pre-built sentiment analysis and custom sentiment analysis for a specialized domain?

Accepted Answer

Custom models require a large set of labeled training data.. Option A is correct because custom sentiment analysis in Azure AI Language is a fine-tuned model that requires you to provide a substantial set of labeled training data (typically hundreds of labeled utterances per class) so the model can learn domain-specific patterns. Option C is correct because the pre-built sentiment analysis model is trained on general-purpose text, so specialized jargon, acronyms, or industry-specific phrasing in a niche domain may be misclassified, which is a key reason to consider a custom model. Option E is correct because Azure AI Language's pre-built sentiment analysis supports multiple languages out-of-the-box, which is a significant advantage when your data spans several languages and you want to avoid building separate custom models per language. Option B is not correct because custom models are not inherently faster; latency depends on deployment, and custom models can add overhead compared to the pre-built service. Option D is not correct because pre-built Azure AI Language models can be deployed in containers (for example, via Docker with the Language container images) for on-premises or disconnected scenarios.

Answer

Custom models always have faster response times.

Answer

Pre-built models cannot be used in containers.

Question 5

Which TWO actions can you take to mitigate the risk of generating harmful content when using Azure OpenAI Service? (Choose two.)

Accepted Answer

Set a system message that instructs the model to avoid harmful outputs.. Option A is correct because a system message sets the model's behavioral guardrails at inference time, and explicitly instructing the model to refuse or avoid harmful outputs is a documented prompt-engineering mitigation for Azure OpenAI Service. Option D is correct because Azure AI Content Safety filters (the default and customizable content filters in Azure OpenAI) inspect both prompts and completions for categories such as hate, violence, sexual, and self-harm, and block or annotate harmful content before it reaches users. Option B is not correct because fine-tuning on safe examples can shape tone and style but is not a reliable content-safety control and does not replace the platform's content filtering. Option C is not correct because multi-region deployment addresses availability and latency, not the generation of harmful content. Option E is not correct because increasing maxTokens only allows longer responses and can actually increase exposure to harmful output rather than mitigate it.

Answer

Fine-tune the model on a dataset of safe examples.

Answer

Deploy the model in multiple regions.

Answer

Increase the maxTokens parameter to allow longer responses.

Question 6

You are developing a generative AI application that must comply with responsible AI principles. Which Azure AI service should you use to detect and filter harmful content in both input prompts and output responses?

Accepted Answer

Azure AI Content Safety. Azure AI Content Safety is the dedicated Azure service for detecting and filtering harmful content such as hate speech, violence, self-harm, and sexual content in both user prompts and AI-generated responses. It provides configurable severity levels and integrates directly with generative AI workflows to enforce responsible AI policies, making it the correct choice for this requirement.

Answer

Microsoft Purview

Answer

Azure OpenAI Service

Answer

Azure AI Language

Question 7

You are a developer at a global e-commerce company. You are building a multilingual chatbot using Azure AI Language that supports English, French, German, and Spanish. The chatbot must answer frequently asked questions about order status, returns, and shipping. You plan to use Custom Question Answering with a single project containing questions and answers in all four languages. However, during testing, you notice that queries in French and German often return incorrect answers or no answer, while English and Spanish work well. You need to ensure accurate answers across all four languages. What should you do?

Accepted Answer

Create a separate Custom Question Answering project for each language and route user queries to the appropriate project based on language detection.. Custom Question Answering (CQA) projects are language-specific; a single project cannot reliably handle multiple languages due to differences in tokenization, stemming, and stop-word handling. By creating a separate project per language and routing queries based on language detection (e.g., using Azure AI Language's language detection API), you ensure that each project's model is optimized for its respective language, improving answer accuracy for French and German.

Answer

Use Azure Cognitive Search with semantic ranking to index the QnA pairs.

Answer

Add synonyms in the project for French and German terms to improve matching.

Answer

Deploy the same project to multiple regions and use traffic manager.

Question 8

You need to generate a summary of a long article using Azure OpenAI. The article is 10,000 tokens long. What should you do to fit the article within the model's context window?

Accepted Answer

Split the article into smaller sections and summarize each section separately.. The article exceeds the model's context window (typically 4096 or 8192 tokens for GPT-3.5/4). Splitting the article into smaller sections and summarizing each separately allows you to process the entire content within the token limits, then combine the summaries for a final coherent output. This is a standard chunking strategy for long documents when using Azure OpenAI.

Answer

Increase the temperature parameter.

Answer

Use a model with a smaller context window.

Answer

Set max_tokens to a lower value.

Question 9

You run the Azure CLI command shown in the exhibit to create an online endpoint for a generative AI model. The deployment fails because the selected VM instance type is not available in the East US region. Which action should you take to resolve the issue?

Accepted Answer

Specify a different VM type or region that supports Standard_NC6s_v3. The deployment failed because the Standard_NC6s_v3 VM instance type is not available in the East US region. The correct action is to either choose a different VM type that is available in East US or deploy to a different region that supports Standard_NC6s_v3. This directly addresses the root cause of the failure, as Azure Machine Learning online endpoints require the selected VM SKU to be available in the target region.

Answer

Increase the instance count to 2

Answer

Use a batch endpoint instead of online endpoint

Answer

Change --compute-type to CPU

Question 10

You are developing a mobile app that allows users to take a photo of a product and get information about it. The app must identify the product from the image. Which Azure AI service should you use?

Accepted Answer

Azure AI Custom Vision with image classification. Azure AI Custom Vision with image classification is specifically designed to identify and categorize products or objects within an image based on trained labels. This service allows you to upload images of products, train a model to recognize them, and then use the model to classify new product photos, making it ideal for a product identification app.

Answer

Azure AI Vision OCR

Answer

Azure AI Face API

Answer

Azure AI Custom Vision with object detection

Question 11

A company wants to deploy an agent using Azure Bot Service that integrates with Microsoft Teams. Which THREE steps should the team take?

Accepted Answer

Use the Bot Framework SDK to build the bot with Teams-specific features.. Option A is correct because the Bot Framework SDK provides the Teams-specific activities, Adaptive Cards, and middleware needed to build an agent that works properly inside Microsoft Teams. Option B is correct because a Teams app manifest (with the bot's ID, scopes, and commands) must be packaged and uploaded/sideloaded so Teams knows how to surface the bot to users. Option C is correct because registering the bot in Azure Bot Service yields the Microsoft App ID and password that the bot uses to authenticate with the Bot Connector and Teams channel. Option D is not required: bot code can run on App Service, containers, or Functions, so deploying to Azure Functions is only one optional hosting choice. Option E is not required: the Bot Framework SDK supports multiple languages (C#, JavaScript, Python, Java), so writing in C# with the Azure SDK for .NET is not a mandatory step.

Answer

Deploy the bot code to an Azure Function.

Answer

Write the bot in C# using Azure SDK for .NET.

Question 12

Your application needs to determine whether two photos of the same person are of the same individual, even if they are from different angles. Which Azure AI service should you use?

Accepted Answer

Azure AI Face. Azure AI Face provides face verification APIs that compare two faces and return a confidence score indicating whether they belong to the same person. It uses deep learning models trained to handle variations in pose, lighting, and expression, making it ideal for matching photos of the same individual from different angles.

Answer

Azure AI Video Indexer

Answer

Azure AI Custom Vision

Answer

Azure AI Vision OCR

Question 13

You are building an agentic solution using Azure AI Agent Service. The agent needs to send an email via Microsoft Graph API. Which authentication method should you use for the action?

Accepted Answer

OAuth 2.0. Microsoft Graph API requires OAuth 2.0 for authentication because it uses delegated or application permissions to access user data securely. Azure AI Agent Service can use OAuth 2.0 with a managed identity or service principal to obtain an access token for the Graph API, ensuring proper authorization and compliance with Microsoft's security model.

Answer

Client Certificate

Answer

API Key

Answer

Basic Authentication

Question 14

You are building a solution to extract key phrases from customer reviews using Azure AI Language. Which feature should you use?

Accepted Answer

Key Phrase Extraction. Key Phrase Extraction is the correct feature because it is specifically designed to identify and return the main talking points or important terms from unstructured text, such as customer reviews. Azure AI Language's Key Phrase Extraction API analyzes the text structure and linguistic patterns to surface the most relevant phrases, which directly addresses the requirement to extract key phrases from reviews.

Answer

Sentiment Analysis

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Language Detection

Answer

Named Entity Recognition

Question 15

Which THREE factors should you consider when selecting a model for a generative AI solution on Azure?

Accepted Answer

Cost per token and deployment options.. Option A is correct because cost per token and deployment options (such as pay-as-you-go versus provisioned throughput) directly affect the total cost and scalability of a generative AI solution on Azure. Option B is correct because the model's capability and modality determine whether it can handle the required task, such as text generation, code completion, or image creation. Option C is correct because latency and throughput requirements dictate whether the chosen model and deployment type can meet the application's performance and concurrency needs. Option D is not a primary selection factor because the number of transformer layers is an internal architectural detail that influences capability but is not a decision criterion by itself. Option E is not a primary selection factor because training data source and licensing are legal and compliance considerations, not core factors for selecting a model for a generative AI solution on Azure.

Answer

Number of transformer layers in the model.

Answer

Training data source and licensing.