AI-102 Practice Test 28 — 15 Questions

Question 1

Which TWO Azure AI services can be used to build a multilingual question-answering bot that retrieves answers from a knowledge base of documents?

Accepted Answer

Azure OpenAI Service with a RAG pattern. The Azure OpenAI Service combined with a Retrieval-Augmented Generation (RAG) pattern allows you to ingest documents into a vector store, retrieve relevant chunks based on the user's query, and then generate a natural language answer using a large language model. This architecture inherently supports multilingual input and output because the underlying model (e.g., GPT-4) can process and generate text in many languages, making it ideal for a multilingual question-answering bot.

Answer

Azure AI Language Understanding (LUIS)

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Azure AI Translator

Answer

Azure AI Document Intelligence

Question 2

You are building a multilingual support chatbot using Azure AI Language. The chatbot must understand user queries in English, Spanish, and French, and respond in the same language. The solution should minimize latency and cost. What is the recommended approach?

Accepted Answer

Use a single Azure AI Language project with multilingual support enabled.. Azure AI Language supports multilingual projects natively, allowing a single model to process queries in English, Spanish, and French without additional translation steps. This minimizes latency (no round-trip translation) and cost (no Translator API consumption), while maintaining response accuracy in the user's original language.

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Use Azure AI Translator to translate all queries to English, process with an English-only project, then translate responses back.

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Create separate Azure AI Language projects for each language and route queries based on detected language.

Answer

Use a single English-only project and rely on Azure AI Translator for all non-English queries.

Question 3

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.

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Key Phrase Extraction

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Extractive Summarization

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Conversational Language Understanding

Question 4

Which TWO features are available in Azure AI Language's extractive summarization?

Accepted Answer

Confidence scores for each extracted sentence.. Azure AI Language's extractive summarization returns a confidence score for each extracted sentence, indicating the model's certainty that the sentence is important. Option D is correct because the feature outputs a ranked list of sentences extracted from the source document, ordered by their relevance scores.

Answer

Identified named entities from the document.

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Sentiment scores for each extracted sentence.

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Generated abstractive summary.

Question 5

A company wants to build a solution that can identify and redact personally identifiable information (PII) from customer support transcripts. The solution must handle multiple languages. Which Azure AI service should be used?

Accepted Answer

Azure AI Language - PII Detection. Azure AI Language's PII Detection feature is specifically designed to identify and redact personally identifiable information in text across multiple languages. It supports over 30 languages and can detect entities such as names, addresses, phone numbers, and credit card numbers, making it the correct choice for this multilingual PII redaction requirement.

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Azure AI Content Safety

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Azure AI Document Intelligence

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Azure AI Translator

Question 6

A company uses Azure AI Language Service for custom text classification. The model is trained to classify support tickets into categories. After deployment, the model performs well on the test set but poorly on new incoming tickets. Which action should be taken to improve generalization?

Accepted Answer

Add more labeled data from actual production tickets. Adding more labeled data from actual production tickets helps the model learn the true distribution of real-world inputs, reducing overfitting to the test set. The model's poor performance on new tickets indicates it memorized patterns specific to the training data rather than generalizing. Incorporating production data directly addresses the distribution shift between the test set and live traffic.

Answer

Switch to a prebuilt text classification model

Answer

Increase the number of training epochs

Answer

Reduce the confidence threshold for classification

Question 7

Your company is developing a knowledge mining solution for a legal firm that needs to extract information from scanned legal documents. The documents contain handwritten notes in addition to printed text. You need to extract both printed and handwritten text. You are using Azure AI Document Intelligence with the Read OCR model. The solution must be integrated into Azure AI Search. During testing, the printed text is extracted correctly, but handwritten text is often missing or incorrect. What should you do to improve the extraction of handwritten text?

Accepted Answer

Ensure the Document Intelligence skill is configured with the Read OCR model and handwriting recognition enabled.. The Document Intelligence Read OCR model supports handwriting recognition, but it must be explicitly enabled in the skill configuration. By ensuring handwriting recognition is enabled, the solution will extract handwritten text. Option C is incorrect because training a custom model is unnecessary; the Read OCR model already handles handwriting when enabled. Option A is incorrect because increasing resolution may improve overall quality but does not specifically enable handwriting recognition. Option B is incorrect because the built-in OCR skill in Azure AI Search does not support handwriting.

Answer

Preprocess the documents to increase image resolution before sending to Document Intelligence.

Answer

Add an OCR skill from Azure AI Search's built-in skills to the skillset.

Answer

Train a custom Document Intelligence model on handwritten samples.

Question 8

Refer to the exhibit. You have this Azure AI Search indexer configuration. The indexer is failing after processing 6 documents that contain errors. What should you do to ensure the indexer continues processing even if some documents fail?

Accepted Answer

Increase maxFailedItems to a higher value, such as 100. The indexer is failing after processing 6 documents because the `maxFailedItems` threshold has been reached. By increasing `maxFailedItems` to a higher value like 100, the indexer will continue processing even if more documents fail, as long as the total number of failed items stays below the new threshold.

Answer

Decrease the batch size to 5

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Increase batch size to 20

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Remove the schedule to run the indexer on demand

Question 9

You need to use Azure AI Language to analyze customer feedback. Which THREE analysis types are available in the Text Analytics API?

Accepted Answer

Sentiment analysis. Sentiment analysis is a core feature of the Azure AI Language Text Analytics API, allowing you to determine the overall positive, negative, or neutral sentiment of customer feedback. This capability is essential for analyzing customer opinions at scale, and it is explicitly listed as one of the three main analysis types (along with entity recognition and key phrase extraction) in the service documentation.

Answer

Image captioning

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Speech-to-text

Question 10

Refer to the exhibit. You have a Custom Question Answering project configured with the JSON shown. When you test the project in Azure AI Language Studio, the query 'How many vacation days do I get?' returns no answer. What is the most likely cause?

Accepted Answer

The query is not phrased as an exact match to the trained question.. Custom Question Answering can be configured to require exact matching between the user query and the trained questions. In the exhibit, the JSON likely defines a QnA pair with a specific question, but the test query 'How many vacation days do I get?' does not exactly match the trained question (e.g., it might be slightly different wording). Since the project is set to exact match, the service returns no answer because the query is not an exact match. Option A correctly identifies this cause.

Answer

The language is set to English but the query uses informal language.

Answer

The answer field is empty in the JSON.

Answer

The confidence score threshold is set too high.

Question 11

You are debugging a CLU application. The JSON above shows a request to the Azure AI Language runtime API. The response returns an intent of "BookFlight" with a confidence of 0.95, but no entities are extracted. The training data includes entities like "Location" and "DateTime". What is the most likely cause?

Accepted Answer

The request is missing the 'verbose' parameter set to true.. The CLU runtime API, by default, returns only the top intent and does not include extracted entities unless the 'verbose' query parameter is set to true. Without this parameter, the response omits the entities array even if the model was trained to extract them, which explains why the intent is returned with high confidence but no entities appear.

Answer

The deployment name is incorrect.

Answer

The model was not trained on entity extraction.

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The utterance does not contain any entities.

Question 12

A developer is creating a custom text classification model using Azure AI Language. The dataset has 10,000 documents across 50 categories. Which method is most suitable for labeling the data efficiently?

Accepted Answer

Use active learning in the custom text classification project. Active learning in custom text classification projects automatically selects the most informative unlabeled documents for manual review, reducing labeling effort while maximizing model accuracy. With 10,000 documents across 50 categories, active learning prioritizes ambiguous or high-uncertainty samples, making it the most efficient approach for iterative labeling.

Answer

Use a prebuilt model from the Azure AI Language service

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Manually label all documents in the Language Studio

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Use Azure Machine Learning designer to auto-label

Question 13

A company is building a bot using Microsoft Copilot Studio (formerly Power Virtual Agents). They want to use Azure AI Language to understand user intents. Which TWO components are required?

Accepted Answer

A Conversational Language Understanding (CLU) app. Conversational Language Understanding (CLU) is the Azure AI Language service that enables intent recognition and entity extraction from user utterances. When building a bot with Microsoft Copilot Studio, you can integrate a CLU app to handle complex natural language understanding, allowing the bot to route user intents to appropriate topics or actions.

Answer

A QnA Maker knowledge base

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An Azure AI Translator resource

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An Azure AI Bot Service bot

Question 14

You are building a solution to analyze customer feedback from multiple sources: emails, chat logs, and survey responses. You need to detect the overall sentiment trend over time and identify the most frequently mentioned topics. The solution must also allow the business analyst to ask natural language questions about the data (e.g., 'Show me complaints about shipping in the last month'). You have all data in Azure Blob Storage. You need to implement a solution with minimal custom code. Which combination of Azure services should you use?

Accepted Answer

Use Azure AI Language to extract sentiment and key phrases, then index the data in Azure Cognitive Search with semantic search enabled; use the search's built-in features for trend analysis and natural language queries.. Azure AI Language provides built-in sentiment analysis and key phrase extraction, and Azure Cognitive Search with semantic search enables indexing the extracted data for trend analysis and natural language queries without custom code. This combination directly meets the requirements of detecting sentiment trends, identifying topics, and allowing natural language questions, all with minimal custom development.

Answer

Use Azure OpenAI Service to analyze sentiment and generate summaries, and store results in a Cosmos DB for querying.

Answer

Use Azure AI Language to perform sentiment analysis and key phrase extraction, then load the results into Power BI for trend analysis and natural language Q&A.

Answer

Use Azure AI Language's custom question answering to create a knowledge base from the feedback and allow natural language queries.

Question 15

You need to monitor an agent deployed in Microsoft Foundry. Which Azure service should you use to collect and analyze logs and metrics from the agent?

Accepted Answer

Azure Monitor. Azure Monitor is the correct choice because it is the comprehensive monitoring service for Azure resources, including agents deployed in Microsoft Foundry. It collects and analyzes logs and metrics from the agent, providing a unified view of performance and health. Application Insights is a subset of Azure Monitor focused on application performance monitoring (APM), but for general agent monitoring, Azure Monitor is the primary service.

Answer

Application Insights

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Azure Log Analytics

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Microsoft Sentinel