AI-102 Practice Test 31 — 15 Questions

Question 1

Refer to the exhibit. You are configuring a custom conversational language understanding project in Azure AI Language. The project currently has English training phrases. You need to add support for Spanish. The exhibit shows the current settings. What should you do?

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

Accepted Answer

Set enableMultiLanguage to true and add Spanish training phrases. In Azure AI Language, a custom conversational language understanding (CLU) project can support multiple languages by setting the `enableMultiLanguage` property to `true` and then adding training phrases in the target language (Spanish) to the same project. This allows the model to learn intents and entities across languages without creating separate projects, leveraging the multilingual capabilities of the underlying LUIS-based engine.

Answer

Create a new project for Spanish

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Set enableMultiLanguage to true and deploy

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Use Azure AI Translator to translate English phrases to Spanish

Question 2

You are deploying a generative AI solution using Azure OpenAI Service. The solution must comply with data residency requirements that mandate all data processing and storage remain within a specific geographic region. You need to configure the service to meet these requirements. What should you do?

Accepted Answer

Deploy the Azure OpenAI Service resource in the required region. Deploying the Azure OpenAI Service resource in the required region ensures that all data processing and storage occur within that geographic boundary, as Azure OpenAI Service is a regional resource that processes and stores data only in the region where it is provisioned. This directly satisfies data residency requirements because the service's infrastructure, including model inference and training data, remains within the selected Azure region. No additional routing or filtering is needed because the resource itself is bound to the region.

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Use a custom endpoint that routes traffic to the required region

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Select a model that is only available in the required region

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Configure content filtering to block data leaving the region

Question 3

Refer to the exhibit. You are reviewing a content safety policy for an Azure AI Foundry deployment. The policy rate limits to 20 requests per minute. A user submits 50 requests in one minute. How many requests are allowed?

Accepted Answer

20. The content safety policy enforces a rate limit of 20 requests per minute. When a user submits 50 requests in one minute, the rate limiter allows only the first 20 requests and blocks the remaining 30. This is a standard token-bucket or sliding-window rate-limiting behavior in Azure AI Foundry, where exceeding the limit results in HTTP 429 (Too Many Requests) for excess requests.

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50

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100

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None, all are blocked.

Question 4

You are building a mobile app that allows users to take a photo of a product and get detailed information. The app uses Azure AI Custom Vision to classify products. You need to ensure low latency for inference. What should you do?

Accepted Answer

Export the Custom Vision model as a TensorFlow model and run on-device. Exporting the Custom Vision model as a TensorFlow model and running it on-device eliminates network latency entirely. Inference happens locally on the mobile device, which provides the lowest possible latency for real-time classification, especially when network connectivity is poor or inconsistent.

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Increase the number of training iterations

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Use the Azure AI Vision API directly

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Use Azure Front Door to cache results

Question 5

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?

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.

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Azure AI Document Intelligence

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Azure AI Computer Vision

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Azure AI Translator

Question 6

A company is building a chatbot that must handle user queries in multiple languages. The chatbot uses Azure AI Language Service. Which feature should be used to detect the language of incoming messages before routing them to the appropriate language model?

Accepted Answer

Language Detection. Language Detection is the correct feature because it is specifically designed to identify the language of text input, returning a language name and a confidence score. In a multi-language chatbot, this detection step is essential to route the query to the appropriate language-specific model or handler. Azure AI Language Service provides a dedicated pre-built capability for language detection, which can be called via the REST API or SDK.

Answer

Sentiment Analysis

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Key Phrase Extraction

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Entity Recognition

Question 7

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.

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

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

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

Question 8

You are designing a solution that uses Azure AI Translator to translate documents in real-time. The solution must handle up to 10,000 characters per request and must be cost-effective. Which pricing tier should you select?

Accepted Answer

S1 (Standard). The S1 (Standard) tier is correct because it supports up to 10,000 characters per request for real-time document translation, which meets the requirement, and it is the most cost-effective paid tier for this workload. The Free tier (F0) has a limit of 5,000 characters per request and is not suitable for production use, while higher tiers like S2 and D3 are unnecessary and more expensive for this character limit.

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F0 (Free)

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D3 (Volume)

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S2 (Standard)

Question 9

Your Azure AI Search indexer is failing to index documents from an Azure Blob Storage container. The error message shows 'AccessDenied'. What is the most likely cause?

Accepted Answer

The search service's managed identity lacks 'Storage Blob Data Reader' role.. The 'AccessDenied' error indicates that the Azure AI Search service lacks the necessary permissions to read data from the Azure Blob Storage container. The most likely fix is to assign the 'Storage Blob Data Reader' role to the search service's system-assigned managed identity, which grants read access to blob containers and their contents.

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The blob container name is misspelled in the datasource.

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The indexer execution interval is set too high.

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The index schema does not match the blob metadata.

Question 10

You are designing a responsible AI solution for a healthcare application that uses Azure AI Vision to analyze medical images. The solution must minimize bias across demographic groups and provide explainability for predictions. Which combination of services should you use?

Accepted Answer

Azure AI Content Safety for content moderation and Fairlearn with Azure Machine Learning interpretability.. Azure AI Content Safety helps filter harmful content and reduce bias in model outputs, while Fairlearn with Azure Machine Learning interpretability provides tools to assess fairness across demographic groups and explain model predictions. This combination directly addresses the requirements of minimizing bias and providing explainability for medical image analysis.

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Azure AI Document Intelligence for extracting metadata and Azure AI Language for sentiment analysis.

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Azure AI Translator for multilingual support and Azure AI Metrics Advisor for monitoring.

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Azure AI Search for indexing results and Azure AI Anomaly Detector for outliers.

Question 11

You are developing a solution that uses Azure Document Intelligence to extract data from invoices and then uses Azure OpenAI to summarize the extracted data. The solution occasionally produces summaries that omit key fields like the invoice total. What should you do to improve accuracy?

Accepted Answer

Define a structured prompt that explicitly requests each field and provide examples. The issue is that the summarization prompt lacks explicit instructions for which fields to include. By defining a structured prompt that explicitly requests each key field (e.g., invoice total, date, vendor) and providing examples, you guide the Azure OpenAI model to consistently extract and include those fields in the summary, reducing omission errors. This approach leverages prompt engineering to improve output reliability without changing model parameters or size.

Answer

Set temperature to 0 to make the output more deterministic

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Use a larger model like GPT-4 instead of GPT-3.5

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Increase the max_tokens parameter

Question 12

A developer is building a custom agent using the Microsoft Bot Framework SDK. The agent must be able to handle multiple turns and maintain context across the conversation. The agent uses dialogs to guide the user through a multi-step process. Which component is responsible for managing the dialog stack and persisting state between turns?

Accepted Answer

The DialogSet object that contains dialogs and manages the stack.. The DialogSet object is the correct component because it is specifically designed to manage the dialog stack and persist state between turns in the Bot Framework SDK. It maintains the stack of active dialogs, handles dialog lifecycle events, and integrates with state management to ensure context is preserved across multiple turns. The other options either handle activity routing, define bot structure, or provide middleware for state, but do not directly manage the dialog stack.

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The ActivityHandler class that processes incoming activities.

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The IBot interface implementation.

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The StateMiddleware component.

Question 13

Which TWO Azure services can be used to detect and redact PII from images containing text? (Choose two.)

Accepted Answer

Azure AI Content Safety.. Azure AI Content Safety can detect and redact PII from images containing text by analyzing the extracted text for sensitive information such as personal identifiers and applying redaction. Azure Computer Vision (OCR) provides the underlying text extraction capability, and when combined with Content Safety's PII detection, enables end-to-end redaction of PII from images.

Answer

Azure Form Recognizer.

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Azure Video Analyzer for Media.

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Azure Cognitive Search.

Question 14

An organization uses Microsoft Copilot Studio to create an agent for IT support. The agent should be able to reset passwords, unlock accounts, and look up user information by connecting to on-premises Active Directory via Microsoft Entra ID. Which type of authentication should be used for the agent to access these actions?

Accepted Answer

OAuth 2.0 authentication with Microsoft Entra ID using on-behalf-of flow.. The agent needs to securely access on-premises Active Directory through Microsoft Entra ID. OAuth 2.0 with the on-behalf-of (OBO) flow allows Copilot Studio to obtain a token for the agent's identity, then exchange it for a token to call downstream APIs (like Microsoft Graph or a custom API) that interact with AD. This provides delegated access and supports modern authentication without exposing static credentials.

Answer

Basic authentication with username and password.

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API key authentication to the on-premises API.

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Certificate-based authentication for the agent's identity.

Question 15

An agent uses Azure AI Language to perform sentiment analysis on customer feedback. The team notices that the sentiment scores are sometimes inaccurate for negative feedback. Which TWO improvements should the team consider?

Accepted Answer

Pre-process the text to handle negations and sarcasm.. Azure AI Language's pre-built sentiment analysis models can struggle with linguistic nuances like negations (e.g., 'not good') and sarcasm (e.g., 'Great, another delay'). Pre-processing the text to explicitly handle these patterns—such as by expanding contractions or using negation detection—can improve the accuracy of the sentiment scores before they are passed to the model.

Answer

Switch to a different language model without fine-tuning.

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Increase the number of decimal places in the sentiment score.

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Increase the confidence threshold for positive sentiment.