AI-102 Practice Test 15 — 15 Questions

Question 1

You are using Azure AI Search to index customer support tickets. You want to automatically extract the customer's sentiment and key phrases from each ticket. Which Azure AI service should you integrate as a skillset?

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

Accepted Answer

Azure AI Language. Azure AI Language provides pre-built capabilities for sentiment analysis and key phrase extraction, which are exactly the skills needed to process customer support ticket text. When integrated as a skillset in Azure AI Search, it enriches the indexing pipeline by automatically extracting these insights from each document. The other services focus on different modalities (vision, translation, document structure) and do not offer native sentiment or key phrase extraction.

Answer

Azure AI Document Intelligence

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Azure AI Computer Vision

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Azure AI Translator

Question 2

A company uses Azure AI Language to analyze customer reviews. They need to detect sentiment, extract key phrases, and identify named entities. Which THREE capabilities should they combine?

Accepted Answer

Named entity recognition (NER). Sentiment analysis (E) is correct because it returns sentiment labels and confidence scores (positive, negative, neutral, mixed) for the review text, directly satisfying the requirement to detect sentiment. Key phrase extraction (D) is correct because it identifies the main talking points in unstructured text, which fulfills the requirement to extract key phrases. Named entity recognition (A) is correct because it detects and categorizes entities such as people, places, organizations, and dates, satisfying the requirement to identify named entities. Abstractive summarization (B) is not needed because it generates new condensed text rather than performing the three requested analyses, and language detection (C) is not required because the scenario does not ask to identify the review language.

Answer

Abstractive summarization

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Language detection

Question 3

You are implementing a knowledge mining solution for a legal firm. The solution must ingest large volumes of legal documents (PDFs and Word files) stored in Azure Blob Storage. You need to extract text, recognize named entities (e.g., parties, judges, case numbers), and index the content for full-text search. The solution should also support redaction of sensitive information before indexing. Which combination of Azure AI services should you use?

Accepted Answer

Azure AI Document Intelligence, Azure AI Language, custom skill for redaction, and Azure AI Search. It combines Azure AI Document Intelligence for OCR and text extraction from PDFs and Word files, Azure AI Language for named entity recognition (e.g., parties, judges, case numbers), a custom skill for redaction (to remove sensitive information before indexing), and Azure AI Search to index the cleaned content for full-text search. This stack directly addresses all requirements: ingestion, entity extraction, redaction, and search indexing.

Answer

Azure AI Document Intelligence, Azure AI Translator, and Azure AI Search

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Azure AI Document Intelligence, Azure AI Video Indexer, and Azure AI Search

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Azure AI Document Intelligence, Azure AI Content Safety, and Azure AI Search

Question 4

You are developing an Azure AI solution that uses pre-built models from Azure AI Vision to analyze images. The solution must be able to detect objects and read printed text. Which TWO capabilities should you use?

Accepted Answer

Object detection. Object detection (C) is correct because it is the Azure AI Vision pre-built capability that locates and classifies multiple objects within an image, returning bounding boxes and labels, which directly satisfies the requirement to detect objects. Read (OCR) (E) is correct because it is the modern Azure AI Vision OCR engine that extracts printed and handwritten text from images and documents, satisfying the requirement to read printed text. The legacy OCR (A) option is an older, deprecated recognition model with weaker accuracy and limited language support, so it is not the recommended choice for new solutions. Facial detection (B) only returns face locations and attributes and does not detect general objects or read text, and image tagging (D) produces descriptive labels for the overall image rather than object locations or extracted text, so neither meets the stated requirements.

Answer

OCR (legacy)

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Facial detection

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Image tagging

Question 5

You are a data engineer at a university. The university wants to digitize its historical student records (paper forms) to make them searchable. The records are scanned as images (JPEG) and stored in Azure Blob Storage. Each form contains handwritten fields: student name, ID number, date of birth, and degree. You need to extract these fields and index them in Azure AI Search. The solution must use Azure AI Services and minimize manual labeling effort. Which approach should you take?

Accepted Answer

Use Azure AI Document Intelligence to train a custom extraction model with a few labeled samples, then deploy as a custom skill in Azure AI Search.. Azure AI Document Intelligence (formerly Form Recognizer) is specifically designed to extract structured fields from forms with handwritten text. By training a custom extraction model with a few labeled samples, you minimize manual labeling effort while achieving high accuracy for fields like student name, ID, date of birth, and degree. The model can then be deployed as a custom skill in Azure AI Search to index the extracted data.

Answer

Use Azure AI Custom Vision to train a model to detect handwriting regions, then use Azure AI Vision OCR to read text.

Answer

Use Azure AI Search with a blob indexer and a skillset that includes OCR skill and Entity Recognition skill.

Answer

Use Azure AI Vision OCR to extract text from images, then use Azure AI Language to extract entities like name, date, and degree.

Question 6

You are designing an agentic solution using Azure AI Agent Service. The agent needs to perform actions on behalf of users, such as sending emails and updating databases. The solution must use managed identities for authentication to Azure resources. Which TWO configurations are required?

Accepted Answer

Use DefaultAzureCredential in the agent's code to authenticate to Azure services. Option E is correct because a system-assigned managed identity must first be enabled on the Azure resource hosting the agent (for example, the Azure AI Foundry/Azure AI Services resource or the compute running the agent code) so that Microsoft Entra ID can issue tokens for that resource without storing secrets. Option C is correct because DefaultAzureCredential is the recommended credential chain in the Azure Identity SDK; it automatically picks up the managed identity (via ManagedIdentityCredential/EnvironmentCredential) when running in Azure, allowing the agent code to authenticate to Azure services such as Azure SQL, Storage, or Microsoft Graph without embedding credentials. Option A is not required and contradicts the managed-identity requirement, since connection strings/secrets in Key Vault are a secret-based pattern rather than identity-based authentication. Option B is not required because managed identities are service principals managed by the platform; you do not manually create a service principal in Microsoft Entra ID, and RBAC role assignments are made to the managed identity's principal, not to a separately created app registration. Option D is incorrect because API keys are static secrets and the scenario explicitly mandates managed identities for authentication to Azure resources.

Answer

Store connection strings in Azure Key Vault and reference them in the agent's configuration

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Create a service principal in Microsoft Entra ID and assign RBAC roles to the agent's resource

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Configure the agent to use an API key for each external service

Question 7

You are a content moderator for a social media platform that uses Azure Content Moderator. The platform has a custom blocklist of URLs (e.g., 'example.com/spam') and a custom term list for hate speech. Recently, users have been posting comments that contain a new form of hate speech not yet in the term list. The comments are being allowed through moderation. You need to update the solution to catch these new phrases as quickly as possible. What should you do?

Accepted Answer

Add the new phrases to the existing custom term list using the List Management API. Azure Content Moderator's custom term lists allow you to dynamically add new offensive terms or phrases via the List Management API, which immediately updates the moderation screening without retraining or redeploying any model. This provides the fastest way to catch new hate speech patterns as they emerge, as the term list is checked in real-time during content review.

Answer

Create a new custom model using Custom Vision to detect the new phrases in text

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Retrain the image classification model using the new phrases as training data

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Delete the existing term list and recreate it with the new phrases included

Question 8

Refer to the exhibit. You have created a Text Analytics resource and retrieved its keys. You want to use the key1 to call the Sentiment Analysis API from a Python application. Which endpoint URL should you use?

Accepted Answer

https://mytextanalytics.cognitiveservices.azure.com/text/analytics/v3.1/sentiment. The Sentiment Analysis API for Azure Cognitive Services Text Analytics uses the endpoint pattern `https://<resource-name>.cognitiveservices.azure.com/text/analytics/v3.1/sentiment`. This is the standard REST API endpoint for sentiment analysis in version 3.1, which requires the `/text/analytics/v3.1/sentiment` path appended to the custom resource domain.

Answer

https://mytextanalytics.cognitiveservices.azure.com/sentiment/v3.1

Answer

https://mytextanalytics.api.cognitive.microsoft.com/text/analytics/v3.1/sentiment

Answer

https://mytextanalytics.cognitiveservices.azure.com/analyze

Question 9

Refer to the exhibit. You are reviewing the configuration of an Azure OpenAI Service resource. The resource is configured with customer-managed keys for encryption. What is the primary benefit of this configuration?

Accepted Answer

Enhanced control over data encryption keys. Customer-managed keys (CMK) allow you to control and manage the encryption keys used to protect your data at rest in Azure OpenAI Service. This provides enhanced control over who can access the keys, when they are rotated, and how they are stored, which is critical for meeting compliance and security requirements. The primary benefit is not performance, cost, or deployment simplicity, but rather the ability to enforce your own key lifecycle and access policies.

Answer

Simplified deployment process

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Improved model performance

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Reduced operational costs

Question 10

A company is implementing a question-answering system using Azure AI Language Service. They have a set of FAQ documents in PDF format. Which feature should they use to automatically generate question-answer pairs?

Accepted Answer

Custom Question Answering. Custom Question Answering (C) is the correct feature because it is specifically designed to ingest semi-structured content like FAQ PDFs and automatically generate question-answer pairs. It uses a built-in extraction pipeline that parses the document structure (e.g., headings, bullet points) to identify likely questions and their corresponding answers, which can then be reviewed and refined in the Azure Language Studio portal.

Answer

Key Phrase Extraction

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Extractive Summarization

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Conversational Language Understanding

Question 11

You are deploying an Azure AI Language custom text classification model. You need to ensure the model meets performance requirements before promoting it to production. Which two actions should you take? (Choose two.)

Accepted Answer

Evaluate the model on a held-out test set that was not used during training.. Option A is correct because evaluating the custom text classification model on a held-out test set that was never used during training provides an unbiased estimate of generalization performance, which is essential before promoting the model to production. Option B is correct because reviewing the confusion matrix reveals per-class precision/recall patterns and shows exactly which classes are frequently misclassified, enabling targeted data or label improvements. Option C is incorrect because Azure AI Language does not require a fixed 95% accuracy threshold on a cross-validation split; performance targets are scenario-specific and cross-validation is not the standard evaluation workflow for this service. Option D is incorrect because computing accuracy on the training set measures memorization rather than generalization and will be optimistically biased. Option E is incorrect because comparing to a majority-class baseline is a useful sanity check but is not one of the required actions for validating a custom text classification model before production.

Answer

Ensure the model achieves at least 95% accuracy on a cross-validation split.

Answer

Use the training set to compute accuracy and ensure it is above 90%.

Answer

Compare the model's performance to a baseline model that always predicts the most common class.

Question 12

You are planning a solution that uses Azure AI Language to analyze customer feedback from social media posts. The solution must: - Detect sentiment (positive, negative, neutral) for each post. - Extract key phrases. - Support English and Spanish languages. - Run asynchronously for a batch of 10,000 posts. - Use the least expensive option that meets requirements. What should you do?

Accepted Answer

Use the Azure AI Language service with the built-in sentiment analysis and key phrase extraction capabilities. Process posts in batches using the async API.. Azure AI Language's built-in sentiment analysis and key phrase extraction natively support both English and Spanish, and the async batch API is designed for high-volume processing (e.g., 10,000 posts) at a lower cost than per-document calls. This approach meets all requirements without custom models or translation overhead.

Answer

Build a custom text classification model in Azure AI Language to detect sentiment and extract key phrases.

Answer

Use the Azure AI Language service with the single-document API for each post.

Answer

Use Azure AI Translator to translate all posts to English, then use Azure AI Language for analysis.

Question 13

You are using Microsoft Copilot Studio to create an agent that helps users reset their passwords. The agent should first verify the user's identity using multi-factor authentication (MFA) before proceeding. Which feature should you configure?

Accepted Answer

Configure Authentication settings to require Microsoft Entra ID authentication with MFA policy. Microsoft Copilot Studio allows you to configure Authentication settings directly on the agent, and by selecting 'Microsoft Entra ID' as the authentication provider, you can enforce an MFA policy that is already configured in your Entra ID tenant. This ensures that before the agent processes any password reset logic, the user must complete MFA, satisfying the identity verification requirement without custom code or flows.

Answer

Add a variable to store the user's identity status

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Add a Power Automate flow that calls Microsoft Entra ID MFA

Answer

Use a 'Sign in' topic trigger from the customer channel

Question 14

You are building a chatbot using Azure AI Language and need to handle user intents that are not covered by the predefined intents. What should you implement?

Accepted Answer

A 'None' intent in a conversational language understanding project. In a Conversational Language Understanding (CLU) project, the 'None' intent is specifically designed to capture utterances that do not match any of the defined intents. This intent acts as a catch-all for unrecognized user inputs, ensuring the chatbot can gracefully handle out-of-scope or ambiguous queries without misclassifying them into a predefined intent.

Answer

Custom entities to capture unknown phrases

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A fallback intent in the QnA Maker knowledge base

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A prebuilt intent from the LUIS catalog

Question 15

A developer is deploying a custom text classification model in Azure AI Language. The model must be accessible via a REST API with low latency. Which TWO actions should the developer take?

Accepted Answer

Obtain the endpoint URL and primary key from Language Studio. Option D is correct because deploying the custom text classification model to a real-time endpoint in Azure AI Language exposes a synchronous REST API that returns predictions immediately, satisfying the low-latency requirement. Option C is correct because, to call that REST API, the developer must obtain the endpoint URL and the primary key (or a secondary key) from the project's deployment details in Language Studio, which are used in the Ocp-Apim-Subscription-Key header for authentication. Option A is incorrect because the batch processing API is designed for asynchronous, high-volume jobs and does not provide the low-latency synchronous responses required here. Option B is incorrect because exporting the model as a Docker container is for on-premises or disconnected container deployment, not for exposing the model through the managed Azure AI Language REST endpoint. Option E is incorrect because a test endpoint in the Azure portal is intended for validation and does not provide the production-grade, low-latency real-time REST API needed for the application.

Answer

Use the batch processing API

Answer

Export the model as a Docker container

Answer

Deploy to a test endpoint in the Azure portal