AI-102 Practice Exam 51 — 20 Questions

Question 1

A healthcare organization needs to mine clinical notes to find mentions of diseases, medications, and treatment procedures. The data is stored in Azure SQL Database. Which Azure AI service should they integrate with Azure AI Search to extract these entities?

Accepted Answer

Azure AI Health Insights. Option B is correct because Azure AI Health Insights (formerly Text Analytics for Health) is specifically designed to extract medical entities from clinical text. Option A is incorrect because Azure AI Language provides general entity extraction but not healthcare-specific. Option C is incorrect because Azure AI Document Intelligence is for document extraction, not clinical notes. Option D is incorrect because Azure AI Search is the indexing service, not the extraction service.

Answer

Azure AI Document Intelligence

Answer

Azure AI Search

Answer

Azure AI Language

Question 2

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

Accepted Answer

PII Detection. Options A and C are correct. Entity Recognition extracts named entities (Person, Organization, Location). PII Detection extracts personally identifiable information entities (e.g., phone numbers, emails). Option B is incorrect because Sentiment Analysis determines sentiment. Option D is incorrect because Language Detection detects language. Option E is incorrect because Custom Entity Lookup matches against a custom list.

Answer

Custom Entity Lookup

Answer

Sentiment Analysis

Answer

Language Detection

Question 3

You are designing an Azure Cognitive Search solution for a legal document repository. The solution must support search across multiple languages, including English and Spanish. You need to ensure that searches return documents regardless of the language of the query. Which approach should you use?

Accepted Answer

Use the Microsoft analyzer for English and Spanish in a multi-language field.. Option D is correct because using a multi-language analyzer that includes both English and Spanish microsoft analyzers allows the index to handle multiple languages in the same field. Option A is wrong because a single analyzer cannot handle multiple languages. Option B is wrong because a custom analyzer would not be prebuilt for multiple languages. Option C is wrong because language-specific fields would require the user to know the language of the query.

Answer

Create a custom analyzer that splits text by language.

Answer

Use a single language analyzer set to English, and assume Spanish documents are translated.

Answer

Create separate fields for each language and set the analyzer per field.

Question 4

You are designing an Azure Cognitive Search solution that indexes customer support tickets. The index must include a field for 'sentiment' that is populated from an AI enrichment pipeline. Which TWO actions are required to achieve this?

Accepted Answer

Add a built-in Sentiment skill to the skillset.. Options A and C are correct. The enrichment pipeline must include a built-in sentiment skill (A), and a field mapping must be added to map the sentiment output to the index field (C). Option B is wrong because the sentiment skill is already built-in. Option D is wrong because a custom skill is not required. Option E is wrong because the index definition defines the field but does not populate it.

Answer

Implement a custom skill to normalize sentiment values.

Answer

Create a custom sentiment analysis skill using Azure AI Language.

Answer

Define a 'sentiment' field in the index with type Collection(Edm.String).

Question 5

You are using Azure AI Document Intelligence to process a large batch of PDF forms. The forms have varying layouts and handwriting. You need to extract text and key-value pairs. Which custom model type should you train?

Accepted Answer

Custom neural model. Option B is correct because a template model works best with forms that have a fixed layout, but since layouts vary, a neural model is more robust. Option A is wrong because a template model assumes fixed layout. Option C is wrong because a composed model combines multiple models but is not the primary choice for varying layouts. Option D is wrong because a prebuilt model is not customized for your forms.

Answer

Custom template model

Answer

Prebuilt-layout model

Answer

Custom composed model

Question 6

You are designing a knowledge mining solution for a large enterprise that uses Azure AI Search to index millions of documents. The solution must support high-availability and automatic failover. Which THREE actions should you take to meet these requirements?

Accepted Answer

Use geo-redundant storage (GRS) for the index data.. Option A ensures redundancy in case of a region failure. Option C enables high availability within a region. Option D ensures data redundancy and failover. Option B is about query language, not availability. Option E is for performance, not failover.

Answer

Enable semantic search on the index.

Answer

Enable indexing of large documents using the text split skill.

Question 7

A healthcare organization is implementing a knowledge mining solution to extract information from medical records. They need to ensure that the solution can identify medical conditions, medications, and treatment procedures using a pre-built model. The solution must be deployed in Microsoft Foundry. Which THREE components should be included? (Choose three.)

Accepted Answer

Text Analytics for Health skill in an Azure AI Search skillset.. Option A is correct because the Text Analytics for Health model is a pre-built model for extracting healthcare entities. Option C is correct because Azure AI Search is used to index the extracted data. Option D is correct because the Text Analytics for Health skill in Azure AI Search applies the model. Options B and E are incorrect: Language Understanding is for conversational AI, and Form Recognizer is for form data.

Answer

Azure AI Document Intelligence (formerly Form Recognizer) custom model.

Answer

Language Understanding (LUIS) model.

Question 8

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. Option C is correct because the Key Phrase Extraction skill extracts key phrases from text. Option A is incorrect because Entity Recognition extracts named entities. Option B is incorrect because Sentiment Analysis determines sentiment. Option D is incorrect because OCR extracts text from images.

Answer

Entity Recognition

Answer

Sentiment Analysis

Answer

OCR

Question 9

You are troubleshooting an Azure AI Search indexer that fails to index a PDF file stored in Azure Blob Storage. The error message indicates that the document is encrypted. What is the most likely cause and solution?

Accepted Answer

The PDF is encrypted; remove encryption before indexing. Option C is correct because Azure AI Search cannot index encrypted documents; the solution is to decrypt the PDF before uploading or use a decryption step. Option A is incorrect because indexers can handle large files (up to 16 MB). Option B is incorrect because unsupported file types would give a different error. Option D is incorrect because the indexer does not need a specific parser for encrypted files.

Answer

The indexer is not configured with the PDF parser; set the parsing mode

Answer

The file format is unsupported; convert to PDF/A

Answer

The file is too large; split it into smaller parts

Question 10

You are implementing a knowledge mining solution with Azure AI Search that ingests data from Azure Blob Storage. The pipeline includes a custom skill that calls an external API for specialized entity extraction. The custom skill sometimes returns HTTP 429 (Too Many Requests). How should you handle this to ensure reliable indexing?

Accepted Answer

Configure a retry policy on the custom skill. Option D is correct because the indexer retry policy can be configured to handle transient failures, including 429 errors, by retrying after a delay. Option A is incorrect because reducing the batch size might not help if the API rate limit is per request. Option B is incorrect because increasing timeouts does not address rate limiting. Option C is incorrect because scheduling the indexer less frequently does not prevent failures during execution.

Answer

Reduce the batch size in the indexer

Answer

Increase the skill timeout

Answer

Schedule the indexer to run less frequently

Question 11

Refer to the exhibit. You are using Azure AI Search with a skillset that splits documents into pages and then analyzes sentiment per page. You notice that the sentiment analysis is returning unexpected results, such as positive sentiment for negative content. What is the most likely cause?

Accepted Answer

The page split mode with overlap may still cut sentences, causing sentiment to be evaluated on incomplete sentences.. Option D is correct because the SplitSkill uses 'pages' mode which often splits at page boundaries, potentially cutting sentences in half. This can cause the sentiment analysis to receive incomplete text, leading to incorrect sentiment. Option A is not the primary issue. Option B is about the initial text, not splitting. Option C is about the split context, but the sentiment context is correct.

Answer

The SentimentSkill is receiving the entire document text instead of individual pages.

Answer

The split skill context '/document' should be '/document/content'.

Answer

The defaultLanguageCode is set to 'en' but the documents contain other languages.

Question 12

Your company is building a knowledge base for customer support using Azure AI Search. You have a large dataset of customer emails stored in Azure Blob Storage. The solution must extract key phrases, detect sentiment, and identify customer intents (e.g., complaint, inquiry, feedback). You plan to use built-in AI skills for key phrase extraction and sentiment detection. For intent identification, you need a custom solution because the intents are specific to your business. You have trained a custom Language Understanding (LUIS) model and published it. How should you integrate the LUIS model into the Azure AI Search enrichment pipeline to extract intents?

Accepted Answer

Create a custom skill in the skillset that calls the LUIS endpoint and returns the top intent.. Option A is correct because Azure AI Search supports custom skills that can call external APIs, such as a LUIS endpoint. This allows you to integrate the LUIS model for intent extraction. Option B is wrong because LUIS is not a built-in skill. Option C is wrong because modifying the index does not run enrichment. Option D is wrong because Document Intelligence is not for intent extraction.

Answer

Add a Document Intelligence skill to classify intents.

Answer

Configure the index to use a custom analyzer to parse intents.

Answer

Use the built-in Entity Recognition skill to extract intents.

Question 13

Which TWO Azure AI Search features should you enable to improve the relevance of search results for a knowledge mining solution that supports natural language queries?

Accepted Answer

Semantic ranking. Options A and D are correct. Semantic ranking re-orders results using deep learning models to match query intent, and scoring profiles allow customizing relevance based on field weights, boosting, and freshness. Option B is incorrect because search mode 'all' returns documents containing all terms, which may not improve relevance. Option C is incorrect because synonyms expand queries but do not re-rank. Option E is incorrect because filters narrow results but do not improve ranking.

Answer

Synonyms

Answer

Search mode 'all'

Answer

Filters

Question 14

You are a developer at a legal firm. The firm has a repository of court case documents stored as PDFs in Azure Blob Storage. You need to build a knowledge mining solution that enables lawyers to search for cases by parties involved, judge name, case number, date, and key legal topics. The documents are in English and contain both typed and handwritten text. The solution must extract the aforementioned metadata and also identify citations to other cases (e.g., 'Smith v. Jones'). You plan to use Azure AI Search with cognitive skills. Which combination of skills should you include in your skillset?

Accepted Answer

Document Layout skill, Entity Recognition skill (for person names and organizations), Custom Entity Lookup skill (for case citation patterns), and Key Phrase Extraction skill for legal topics.. Option A is correct because Document Layout skill handles typed and handwritten text, Entity Recognition can extract person names (parties, judge) and law-related entities, and a custom skill can parse case citations. Option B misses entity extraction for parties and judge. Option C uses OCR but Document Layout is better. Option D uses Translator unnecessarily.

Answer

OCR skill (to handle handwriting), Key Phrase Extraction skill, Sentiment skill, and Language Detection skill.

Answer

OCR skill, Document Layout skill, Entity Recognition skill, and Text Translation skill.

Answer

Document Layout skill, Text Translation skill, Sentiment skill, and Key Phrase Extraction skill.

Question 15

You are building a knowledge mining solution for a legal firm to extract clauses from contracts. The contracts are stored as PDFs in Azure Blob Storage. You need to design the solution to minimize cost while ensuring high accuracy for clause extraction. Which approach should you use?

Accepted Answer

Use Azure AI Document Intelligence with a custom extraction model trained on contract clauses.. Option B is correct because Azure AI Document Intelligence (formerly Form Recognizer) offers custom extraction models with high accuracy and is optimized for document understanding. Azure AI Search with blob indexers is for indexing, not extraction. Azure OpenAI GPT-4 can be costlier and may not be as optimized for structured extraction from PDFs. Custom Vision is for images, not documents.

Answer

Use Azure AI Custom Vision to detect clause regions in scanned documents.

Answer

Use Azure OpenAI GPT-4 to process each PDF and extract clauses using prompts.

Answer

Use Azure AI Search with a blob indexer to extract clauses during indexing.

Question 16

Which TWO Azure AI services are most appropriate for extracting text from images and recognizing handwritten text?

Accepted Answer

Azure AI Document Intelligence. Option A (Azure AI Vision) includes OCR and handwriting recognition. Option D (Azure AI Document Intelligence) is optimized for document extraction including handwriting. Option B is for search, not OCR. Option C is for text analytics. Option E is for speech.

Answer

Azure AI Speech

Answer

Azure AI Search

Answer

Azure AI Language

Question 17

Refer to the exhibit. You are configuring an Azure AI Search skillset. The skillset includes an EntityRecognitionSkill and a KeyPhraseExtractionSkill. After running the indexer, you notice that the 'organizations' field is empty in the index. What is the most likely cause?

Accepted Answer

The output field mapping for organizations is missing. Option B is correct because the entity recognition skill outputs entities into a specific structure; to get organizations, you need to map the '/document/organizations' path to the index field. Option A is incorrect because the skill is correctly configured to extract organizations. Option C is incorrect because the order of skills doesn't affect entity recognition. Option D is incorrect because the skill output path is standard.

Answer

The skill output path is incorrect

Answer

The 'Organization' category is misspelled

Answer

The skills must be in reverse order

Question 18

You are designing a knowledge mining solution for a manufacturing company that needs to extract information from equipment maintenance manuals. The manuals are in multiple languages (English, French, German). You need to ensure that the extracted content is searchable in English only. Which approach should you use?

Accepted Answer

Use the Text Translation skill to translate all content to English during indexing.. Option C is correct because you can use the Text Translation skill to translate content to English during indexing, and then index only the translated text. Option A would not translate. Option B only detects language. Option D uses two skills unnecessarily.

Answer

Use the Entity Recognition skill to extract entities and then index entities only.

Answer

Use the Language Detection skill to identify language and then index all content as-is.

Answer

Use the Key Phrase Extraction skill to extract key phrases and then index them.

Question 19

You are a data engineer at a multinational corporation. The company has thousands of research reports in PDF format stored in Azure Blob Storage. The reports contain text, tables, charts, and handwritten annotations. Your team needs to build a knowledge mining solution using Azure AI Search that allows researchers to query the reports using natural language. The solution must extract text, table structures, and handwritten annotations. Additionally, the solution must handle multiple languages (English, Spanish, and French) and ensure that the index is updated daily as new reports are added. The search should prioritize the most recent reports. You have an Azure AI Search service in the S2 tier. Which combination of actions should you take to meet these requirements?

Accepted Answer

Use Azure AI Document Intelligence layout model with OCR, add a custom translation skill, and configure a scoring profile with freshness boosting. Option B is correct. Using Azure AI Document Intelligence's layout and OCR capabilities extracts text, tables, and handwriting. The enrichment pipeline with a custom skill using the translation service handles multilingual content, and a scoring profile with freshness boosting prioritizes recent reports. Option A is incorrect because Azure AI Vision OCR alone does not extract table structure. Option C is incorrect because the Language service does not handle document layout. Option D is incorrect because scheduling the indexer once a week does not meet the daily update requirement.

Answer

Use Azure AI Vision OCR skill for text extraction, add a translation skill, and use a simple search query

Answer

Use Azure AI Document Intelligence prebuilt-read model, add a custom skill for language detection, and schedule the indexer weekly

Answer

Use Azure AI Language text extraction, a custom entity recognition skill, and enable semantic ranking

Question 20

You are a solution architect at a financial services company. You need to implement a knowledge mining solution that extracts information from annual reports (PDF) of publicly traded companies. The reports contain financial tables, executive summaries, and legal disclaimers. The solution must: (1) extract the company name, fiscal year, revenue, net income, and CEO name; (2) redact any personally identifiable information (PII) like email addresses and phone numbers before indexing; (3) index the extracted data in Azure AI Search; (4) allow users to query using natural language (e.g., 'Which company had the highest revenue in 2023?'). The reports are uploaded to an Azure Blob Storage container. You have access to Azure AI Services and Azure OpenAI. Which combination of services and configurations should you use?

Accepted Answer

Use Azure AI Document Intelligence custom extraction model trained on annual reports to extract fields. In the Azure AI Search pipeline, add a PII detection skill to redact PII. Enable semantic search for natural language queries.. Option C is correct because it uses Document Intelligence for structured extraction (tables, financial data), PII detection skill for redaction, and semantic search for natural language queries. Option A lacks PII redaction. Option B uses OpenAI for extraction but may be less reliable for structured tables and lacks PII redaction. Option D uses OCR but Document Intelligence is better for digital PDFs.

Answer

Use Azure AI Vision OCR to extract text from PDFs, then use Azure AI Language to extract entities and key phrases. Index in Azure AI Search with semantic search.

Answer

Use Azure AI Search with blob indexer, include a skillset with Document Layout skill, Entity Recognition skill (for financial entities), and Key Phrase Extraction. Enable semantic search.

Answer

Use Azure OpenAI GPT-4 to process each report via a custom extraction prompt, then send extracted JSON to Azure AI Search. Enable semantic search.