AI-102 Implement knowledge mining and information extraction solutions Practice Test 11 — 15 Questions

Question 1

You are developing a knowledge mining solution that extracts insights from customer feedback. Which TWO Azure AI services can be used to analyze the sentiment of the feedback and categorize it into topics?

Accepted Answer

Azure AI Language. Option A (Azure AI Language) provides sentiment analysis and topic extraction. Option D (Azure AI Search with cognitive skills) can incorporate sentiment and topic extraction skills. Option B is for decision, not sentiment. Option C is for text translation. Option E is for computer vision.

Answer

Azure AI Personalizer

Answer

Azure AI Translator

Answer

Azure AI Custom Vision

Question 2

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 3

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 4

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 5

You are a data scientist at a healthcare research organization. You have been tasked with building a knowledge mining solution to extract key information from thousands of medical journal articles stored as PDFs in an Azure Blob Storage container. The articles are in English and contain tables, figures, and structured text. Your organization uses Microsoft Purview for data governance. You need to design a solution that uses Azure AI Search and Azure AI Services to extract and index the following: article title, authors, publication date, abstract, and key findings (as key phrases). The solution must also detect any mentions of drugs and dosages. The extracted information must be indexed and searchable via a custom web application. Which approach should you take?

Accepted Answer

Use Azure AI Search with a blob indexer and a skillset that includes Document Layout skill, Entity Recognition skill to extract drug names, Key Phrase Extraction skill, and custom skill to extract title/authors/date from the first page. Create an index with fields for each required element.. Option C is correct because it uses the appropriate skills for extraction: Document Layout skill handles tables and figures, Entity Recognition extracts drugs (as entities), Key Phrase Extraction extracts key findings, and the index includes all required fields. Option A misses Entity Recognition for drugs. Option B uses Translator unnecessarily. Option D uses OCR skill but the Document Layout skill is more suitable for structured PDFs.

Answer

Use Azure AI Search with a skillset that includes OCR skill, Text Translation skill to translate, Entity Recognition skill for drugs, and Key Phrase Extraction. Index the results.

Answer

Use Azure AI Search with a blob indexer that includes a skillset with Document Layout skill to extract text, Key Phrase Extraction skill to extract key findings, and map built-in metadata for title, authors, date. Use a custom index to store the extracted fields.

Answer

Use Azure AI Search with a skillset that includes OCR skill, Entity Recognition skill, Sentiment skill, and Key Phrase Extraction. Use a knowledge store to project the enriched data.

Question 6

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.

Question 7

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 8

You are a machine learning engineer at a retail company. The company wants to build a product knowledge base by extracting information from product manuals, specifications sheets, and customer reviews. The data sources include PDFs, Word documents, and plain text files stored in Azure Blob Storage. The solution must: (1) extract product name, model number, price, and key features; (2) analyze customer reviews to extract sentiment and common issues; (3) enable natural language queries like 'Which products have the best reviews under $100?'; (4) handle documents in English and Spanish. You need to design a solution using Azure AI Search and Azure AI Services. Which approach meets all requirements with the least development effort?

Accepted Answer

Use a single Azure AI Search pipeline with a skillset that includes Document Layout skill, Text Translation skill (to English), Sentiment skill, and Key Phrase Extraction skill. Enable semantic search.. Option D is correct because one skillset can handle all document types (using Document Layout skill), translate Spanish to English, extract sentiment from reviews, and extract key phrases for features. Document Intelligence is not needed for reviews. Option A misses sentiment analysis. Option B uses two pipelines unnecessarily. Option C misses translation.

Answer

Use Azure AI Document Intelligence custom model to extract product info from manuals/specs. Use a separate Azure AI Search pipeline for customer reviews with sentiment analysis. Enable semantic search.

Answer

Use Azure AI Search with a blob indexer and a skillset that includes OCR skill (for scanned PDFs), Text Translation skill, Sentiment skill, and Entity Recognition skill. Enable semantic search.

Answer

Use Azure AI Document Intelligence to extract product info from all documents, then feed into Azure AI Search. Enable semantic search.

Question 9

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.. Option B is correct because Azure AI Document Intelligence has prebuilt models for handwriting and can extract fields with minimal training. Option A requires custom training and labeling. Option C uses OCR but not extraction. Option D uses Custom Vision which is not suitable for text extraction.

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 10

You are a solution architect at a news agency. The agency publishes thousands of articles daily. You need to build a knowledge mining solution that enables journalists to search for articles by topic, sentiment, key people, and locations mentioned. The articles are stored as HTML files in Azure Blob Storage. The solution must also provide a summary for each article. You plan to use Azure AI Search with cognitive skills and Azure OpenAI. Which combination of skills and features should you include to meet all requirements with the best performance and accuracy?

Accepted Answer

Skillset with Entity Recognition skill, Sentiment skill, and Key Phrase Extraction skill. Use Azure OpenAI service to generate summaries via a custom skill that calls the GPT model. Enable semantic search.. Option C is correct because it uses Entity Recognition for people/locations, Sentiment for sentiment, Key Phrase Extraction for topics, and Azure OpenAI for summarization (via a custom skill). Option A uses Translator unnecessarily. Option B uses Text Analytics for health (not appropriate) and lacks summarization. Option D uses Document Intelligence which is for documents, not HTML.

Answer

Use Azure AI Document Intelligence to extract content from HTML, then use Azure AI Language to extract entities and sentiment. Index in Azure AI Search with semantic search.

Answer

Skillset with Entity Recognition skill, Sentiment skill, Key Phrase Extraction skill, and Text Translation skill. Enable semantic search.

Answer

Skillset with Entity Recognition skill, Sentiment skill, and Text Analytics for Health skill to extract medical terms. Use Azure OpenAI for summarization as a custom skill.

Question 11

A company is building a knowledge mining solution using Azure AI Search. They need to extract entities from a large set of PDF documents stored in Azure Blob Storage. The solution must use a built-in AI skill to identify people, organizations, and locations. Which TWO actions should be taken? (Choose two.)

Accepted Answer

Enable OCR (Optical Character Recognition) in the indexer configuration.. Option A is correct because the Entity Recognition skill identifies people, organizations, and locations. Option D is correct because the built-in OCR skill is needed to extract text from PDFs before entity recognition can occur. Options B, C, and E are either unrelated or unnecessary.

Answer

Configure the index to use a custom analyzer.

Answer

Add the Key Phrase Extraction skill to the skillset.

Answer

Deploy a custom skill using Azure Functions to extract entities.

Question 12

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 13

You are developing a knowledge mining solution for a legal firm that needs to process thousands of legal contracts stored as PDFs in Azure Blob Storage. The solution must extract clauses, parties, and dates using a custom model. You are using Microsoft Foundry with Azure AI Search and Azure AI Document Intelligence. The custom model must be trained on labeled contract data. After training, you deploy the model and integrate it into the AI Search enrichment pipeline. The pipeline must also perform OCR for scanned contracts. You have configured the following:

- A custom classification model in Document Intelligence for document types.
- A custom extraction model in Document Intelligence for clauses, parties, and dates.
- An Azure AI Search index with fields: clause, party, date.
- A skillset with a Document Intelligence skill pointing to the custom extraction model.

During testing, the pipeline runs successfully for digital PDFs but fails for scanned PDFs. The error indicates that OCR is not being applied. What should you do to fix the issue?

Accepted Answer

Modify the Document Intelligence skill configuration to enable OCR processing.. Option B is correct because scanned PDFs require OCR to convert images to text. The Document Intelligence custom extraction model can process images if OCR is enabled in the skill. Option A is wrong because the issue is OCR, not the model type. Option C is wrong because an OCR skill would duplicate functionality; Document Intelligence handles OCR internally. Option D is wrong because reindexing won't fix the missing OCR step.

Answer

Retrain the custom extraction model with scanned document images.

Answer

Delete and recreate the index with a different field mapping.

Answer

Add an OCR skill to the skillset before the Document Intelligence skill.

Question 14

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 15

You are designing a knowledge mining solution for a publishing company that needs to extract metadata from thousands of book manuscripts in various formats (PDF, Word, EPUB). The solution must identify authors, publication dates, and chapter titles. You are using Microsoft Foundry with Azure AI Search and Azure AI Document Intelligence. The manuscripts are stored in Azure Blob Storage. You need to ensure that the solution can handle all file formats. You have configured a skillset with a Document Intelligence skill for the PDFs and Word documents. However, the EPUB files are not being processed. What should you do to include EPUB files in the enrichment pipeline?

Accepted Answer

Develop a custom skill that converts EPUB files to plain text and add it to the skillset.. Option C is correct because Azure AI Search's indexer does not natively support EPUB files. A custom skill can convert EPUB to text or a supported format. Option A is wrong because the skill itself cannot handle unsupported formats. Option B is wrong because the indexer must support the format. Option D is wrong because Document Intelligence does not support EPUB.

Answer

Use Azure AI Document Intelligence to extract text from EPUB files directly.

Answer

Modify the Document Intelligence skill to accept EPUB files.

Answer

Register a new data source type for EPUB in Azure AI Search.