AI-102 Practice Test 23 — 15 Questions

Question 1

You are developing a solution that uses Azure AI Translator to translate documents from English to French. You need to ensure that the translated text maintains the original formatting, such as HTML tags. Which feature should you use?

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

Accepted Answer

Set the content type to 'text/html' in the translation request.. The translator can preserve the original formatting when using the appropriate content type or using the 'textType' parameter set to 'html'. Option A is wrong because the glossary is for custom translations. Option B is wrong because the parallel corpus is for training custom models. Option D is wrong because transliteration converts between scripts, not formats.

Answer

Use the Translator glossary to preserve formatting.

Answer

Use the Translator parallel corpus to train a custom model.

Answer

Use the Transliterate method to preserve formatting.

Question 2

Which THREE components are required to build a custom question answering solution using Azure AI Language?

Accepted Answer

A project in Azure AI Language. A custom question answering project requires a project in Azure AI Language, a knowledge base containing Q&A pairs, and an endpoint for inference. A LUIS app is for language understanding, not QnA. An Azure AI Bot Service is optional for deploying a bot.

Answer

A Language Understanding (LUIS) app

Answer

An Azure AI Bot Service resource

Question 3

Your team is developing a chatbot using Azure AI Bot Service with language understanding via Azure AI Language (CLU). You need to ensure that the chatbot can handle multiple intents in a single user utterance and return confidence scores for each. Which configuration should you use?

Accepted Answer

Use a Conversational Language Understanding (CLU) project with the 'Multiple intents' setting enabled.. Azure AI Language's Conversational Language Understanding (CLU) supports a 'Multiple intents' setting that allows the model to predict multiple intents from a single utterance, each with its own confidence score. This is the native way to handle multi-intent scenarios without requiring orchestration or custom logic.

Answer

Use an Orchestration workflow project that connects to multiple CLU projects.

Answer

Use a standard CLU project and manually combine intents.

Answer

Use Azure AI QnA Maker with custom logic to detect intents.

Question 4

Which TWO Azure AI services can be used together to build a solution that transcribes customer service calls and detects sentiment?

Accepted Answer

Azure AI Language (sentiment analysis). Azure AI Speech's speech-to-text capability transcribes the audio of customer service calls into text, while Azure AI Language's sentiment analysis evaluates that text to determine the overall sentiment (e.g., positive, negative, or neutral). Together, they form a complete pipeline for analyzing call recordings. Option A is correct because sentiment analysis is the specific feature within Azure AI Language that detects sentiment from text. Option B is correct because speech-to-text is the specific feature within Azure AI Speech that converts audio to text.

Answer

Azure AI Translator

Answer

Azure AI Speech (text-to-speech)

Answer

Azure AI Language (conversational language understanding)

Question 5

Your Azure AI Search index is experiencing high query latency. You have enabled semantic search and custom scoring profiles. You need to reduce latency without degrading search quality. Which action should you take?

Accepted Answer

Increase the number of replicas.. Increasing the number of replicas distributes query load across multiple copies of the index, allowing parallel processing of search requests. This directly reduces query latency without altering the search logic, scoring profiles, or semantic enrichment, thus preserving search quality.

Answer

Remove custom scoring profiles.

Answer

Reduce the number of partitions.

Answer

Disable semantic search.

Question 6

You are designing a knowledge mining solution to extract information from scanned invoices stored as multi-page TIFF images. Which two Azure AI services should you combine to extract text and structure the data?

Accepted Answer

Azure AI Document Intelligence and Azure AI Vision. Azure AI Document Intelligence (formerly Form Recognizer) can extract structured data like invoice fields, while Azure AI Vision provides OCR to extract text from multi-page TIFF images. Option A is incorrect because Azure AI Language does not process images directly. Option C is incorrect because Azure AI Search indexes data but does not extract text from images. Option D is incorrect because Azure AI Translator translates text but does not extract or structure data.

Answer

Azure AI Language and Azure AI Search

Answer

Azure AI Search and Azure AI Vision

Answer

Azure AI Translator and Azure AI Document Intelligence

Question 7

You are designing a knowledge mining solution for customer support emails. The solution must extract the customer's name, issue category, and sentiment from each email. Which two Azure AI services should you combine?

Accepted Answer

Azure AI Language and Azure AI Search. Azure AI Language provides entity extraction and sentiment analysis. Azure AI Search indexes the extracted data for search. The other services are not directly needed for extraction and indexing.

Answer

Azure AI Bot Service and Azure AI Language

Answer

Azure AI Document Intelligence and Azure AI Search

Answer

Azure AI Translator and Azure AI Language

Question 8

You need to enrich documents with key phrases and sentiment before indexing into Azure AI Search. Which type of skill should you use?

Accepted Answer

Cognitive Services skill. Key phrase extraction and sentiment analysis are cognitive skills available in the Azure AI Language service. The Cognitive Services skill references a Cognitive Services resource that provides these capabilities.

Answer

Entity Recognition skill

Answer

Document Extraction skill

Answer

Custom Web API skill

Question 9

Which TWO built-in cognitive skills in Azure AI Search can be used to extract entities from text?

Accepted Answer

PII Detection. The two built-in cognitive skills in Azure AI Search that extract entities from text are Entity Recognition and PII Detection. Entity Recognition extracts named entities such as people, organizations, and locations. PII Detection extracts personally identifiable information like phone numbers, email addresses, and social security numbers. Custom Entity Lookup is a custom skill, not built-in. Sentiment Analysis determines sentiment, and Language Detection identifies language; neither extracts entities.

Answer

Custom Entity Lookup

Answer

Sentiment Analysis

Answer

Language Detection

Question 10

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?

Accepted Answer

Key Phrase Extraction. The Key Phrase Extraction skill extracts key phrases from text, making it the correct choice for automatically extracting key phrases from customer support tickets. Entity Recognition identifies named entities like people, places, and organizations, not key phrases. Sentiment Analysis determines the sentiment (positive, negative, neutral) of text. OCR extracts text from images, not from text-based tickets.

Answer

Entity Recognition

Answer

Sentiment Analysis

Answer

OCR

Question 11

Which TWO actions should you take to ensure compliance with data privacy regulations when using Azure AI Language to process customer support transcripts that contain personally identifiable information (PII)?

Accepted Answer

Enable PII detection and redaction in the Azure AI Language service.. Azure AI Language's PII detection and redaction feature is specifically designed to identify and mask personally identifiable information (PII) in text, such as names, addresses, and social security numbers, directly within the service. This built-in capability ensures that sensitive data is removed or obfuscated before storage or further processing, directly supporting compliance with data privacy regulations like GDPR or HIPAA.

Answer

Disable diagnostic logging for the resource.

Answer

Use customer-managed keys (CMK) for encryption.

Answer

Apply Azure AI Content Safety filters to anonymize personal data.

Question 12

You are designing a solution to extract customer names and addresses from scanned handwritten forms. The forms are stored as images in Azure Blob Storage. The extraction must achieve high accuracy with minimal manual review. Which combination of Azure AI services should you use?

Accepted Answer

Azure AI Document Intelligence with a custom model trained on handwritten forms. Azure AI Document Intelligence's custom model capability allows you to train a model specifically on handwritten forms, enabling it to learn the unique handwriting patterns and layout structures present in your scanned documents. This tailored approach achieves high accuracy with minimal manual review, as the model is optimized for your specific form type rather than generic invoice or receipt templates.

Answer

Azure AI Document Intelligence with prebuilt invoice and receipt models

Answer

Azure AI Language Service with custom Named Entity Recognition (NER)

Answer

Azure AI Computer Vision with OCR and Azure AI Search

Question 13

Which TWO services can be used to enrich an Azure AI Search index with knowledge mining skills? (Choose two.)

Accepted Answer

Azure AI Computer Vision. Azure AI Search indexes can be enriched using built-in AI skills that call Azure AI services. Azure AI Computer Vision provides image analysis skills (e.g., OCR, description, tag extraction) that can be attached to an indexer pipeline. Azure AI Language Service provides text analytics skills (e.g., entity recognition, key phrase extraction, sentiment analysis) that enrich document fields during indexing.

Answer

Azure AI Video Indexer

Answer

Azure AI Speech Service

Answer

Azure AI Translator

Question 14

You have configured an Azure AI Search indexer with a Cosmos DB data source as shown in the exhibit. The indexer runs successfully, but you notice that the index is missing some documents that were recently added to Cosmos DB. What is the most likely cause?

Accepted Answer

The high water mark is not being updated correctly, causing some documents to be skipped.. The most likely cause is that the high water mark (the _ts value) is not being updated correctly, causing the indexer to skip documents that were recently added. Azure AI Search uses a high water mark strategy to track changes from Cosmos DB, and if the _ts value is not properly recorded or the indexer's change tracking logic fails, new documents may be missed even though the indexer runs successfully.

Answer

The indexer is not configured to track changes using _ts.

Answer

The container name is misspelled.

Answer

The query does not select all fields required by the index.

Question 15

You are using Azure OpenAI Service to generate code snippets. The output must be safe and free of security vulnerabilities. Which TWO practices should you follow? (Select TWO.)

Accepted Answer

Use Azure AI Content Safety to filter outputs. Azure AI Content Safety is a dedicated service that provides an additional layer of filtering for harmful or inappropriate content, including security vulnerabilities, beyond what the model itself offers. It allows you to define custom severity thresholds and blocklists, ensuring that generated code snippets are safe before they reach the user. This is a recommended practice for production deployments to mitigate risks like injection attacks or exposure of sensitive patterns.

Answer

Increase the temperature parameter to encourage diversity

Answer

Rely on the model's built-in safety features

Answer

Fine-tune the model with a dataset of secure code examples