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

Question 1

An organization uses Azure AI Search to power an internal knowledge base. They notice that search results are returning irrelevant documents. The index includes a 'content' field with full text and a 'tags' field with metadata. Users often search for specific terms that appear in the 'tags' field. How should you configure the search index to improve relevance?

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

Accepted Answer

Configure a scoring profile with a higher weight for the 'tags' field.. Configuring a scoring profile with a higher weight for the 'tags' field increases the relevance score of documents where search terms match the tags, thereby prioritizing those results. Option A (freshness-based scoring) would favor newer documents but does not address matching on tags. Option C sets the 'tags' field to use the 'keyword' analyzer, which changes tokenization but does not adjust field weighting. Option D enables semantic search on the 'content' field, which enhances understanding of natural language queries but does not specifically boost the weight of the tags field.

Answer

Add a custom scoring profile based on freshness.

Answer

Set the 'tags' field to use the 'keyword' analyzer.

Answer

Enable semantic search on the 'content' field.

Question 2

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. Azure AI Language provides pre-built sentiment analysis and key phrase extraction capabilities that can determine the sentiment (positive, negative, neutral) of customer feedback. Additionally, its named entity recognition and topic modeling features allow you to categorize feedback into topics, making it a direct fit for this requirement.

Answer

Azure AI Personalizer

Answer

Azure AI Translator

Answer

Azure AI Custom Vision

Question 3

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 TWO actions should you take to meet these requirements?

Accepted Answer

Provision the Azure AI Search service in at least two regions.. To achieve high availability and automatic failover in Azure AI Search, you need to provision the service in at least two regions (Option C) to provide geo-redundancy, and configure at least two replicas within a region (Option D) for high availability and load balancing. Option A is incorrect because index data is stored within the search service, not in Azure Storage, so GRS is not applicable. Option B (semantic search) enhances query capabilities but does not affect availability. Option E (text split skill) is for processing large documents, not for availability.

Answer

Use geo-redundant storage (GRS) for the index data.

Answer

Enable semantic search on the index.

Answer

Enable indexing of large documents using the text split skill.

Question 4

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

Accepted Answer

Azure AI Document Intelligence. Azure AI Document Intelligence (Option A) provides document analysis and extraction capabilities, including OCR and handwriting recognition. Azure AI Vision (Option C) offers the Read API for extracting printed and handwritten text from images. Azure AI Speech (Option B) is for speech-to-text, not image OCR. Azure AI Search (Option D) is for indexing and searching data, not text extraction. Azure AI Language (Option E) provides NLP capabilities, not OCR.

Answer

Azure AI Speech

Answer

Azure AI Search

Answer

Azure AI Language

Question 5

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.. 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 6

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.. It combines the Document Layout skill (to extract text from PDFs including tables and figures), Entity Recognition skill (to detect drug names), Key Phrase Extraction skill (to identify key findings), and a custom skill (to parse the first page for title, authors, and publication date). This approach handles the unstructured nature of medical journal articles while meeting all extraction requirements and indexing them into a custom searchable index.

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 7

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.. Azure AI Document Intelligence can be trained with a custom extraction model to accurately extract specific financial fields like company name, fiscal year, revenue, net income, and CEO name from annual report PDFs. The PII detection skill in the Azure AI Search enrichment pipeline redacts sensitive information such as email addresses and phone numbers before indexing. Enabling semantic search allows users to query using natural language. Option B relies on Azure AI Vision OCR and Azure AI Language entity extraction, which are less precise for structured table extraction and cannot guarantee the specific fields needed. Option C uses generic skills like Document Layout and Entity Recognition, which are not tailored for financial data extraction and may miss critical fields. Option D uses GPT-4, which can be inconsistent for structured data extraction from tables and does not include built-in PII redaction within the search pipeline.

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 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.. It uses a single Azure AI Search pipeline with a skillset that includes Document Layout skill (to handle various document formats like PDFs, Word docs, and text files), Text Translation skill (to convert Spanish documents to English, unifying the language), Sentiment skill (to analyze customer reviews for sentiment), and Key Phrase Extraction skill (to extract key features and common issues). Enabling semantic search allows natural language queries such as 'Which products have the best reviews under $100?'. This approach meets all requirements with the least development effort, as it avoids the need for multiple pipelines or custom model training required by other options.

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.. 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.

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.. It combines Entity Recognition (for people/locations), Sentiment (for sentiment), Key Phrase Extraction (for topics), and a custom skill using Azure OpenAI for summarization, with semantic search for optimal performance. Option A is incorrect: Azure AI Document Intelligence is designed for documents like PDFs and images, not HTML; using Azure AI Language for entities and sentiment is okay but alone lacks key phrase extraction for topics and summarization. Option B is incorrect: it includes Text Translation skill, which is not needed, and lacks Key Phrase Extraction for topics and summarization. Option D is incorrect: Text Analytics for Health is specialized for medical terms, which is irrelevant, and the skill set lacks Key Phrase Extraction for topics.

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.. OCR (Optical Character Recognition) is required to extract text from scanned PDFs before entity recognition can be applied. Option E is correct because the built-in Entity Recognition skill identifies people, organizations, and locations. Option A is incorrect because custom analyzers affect indexing and querying, not entity extraction. Option B is incorrect because the Key Phrase Extraction skill extracts key phrases, not named entities. Option C is incorrect because a custom skill is unnecessary when a built-in skill already meets the requirement.

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.. The Text Analytics for Health skill in an Azure AI Search skillset applies the pre-built healthcare model to extract medical conditions, medications, and treatment procedures during indexing. Option B is correct because the Azure AI Search index stores the extracted healthcare entities and supports search and retrieval. Option C is correct because the Text Analytics for Health model is a pre-built model available in Microsoft Foundry (Azure AI Foundry) for direct use in knowledge mining solutions. Option D is incorrect because Azure AI Document Intelligence custom model is designed for extracting structured data from forms and requires training, not for pre-built healthcare entity extraction. Option E is incorrect because Language Understanding (LUIS) is used for conversational intent and entity recognition, not for extracting medical concepts from documents.

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.. Scanned PDFs contain images of text, so OCR must be performed to extract text before the custom extraction model can process it. Azure AI Document Intelligence's custom extraction model can perform OCR internally if the skill configuration has OCR enabled. By default, the Document Intelligence skill in the skillset does not automatically enable OCR for scanned documents; you must set the `enableOcr` property to `true` in the skill configuration. Option A is incorrect because retraining the model with scanned images would not enable OCR; the model still needs text input. Option B is incorrect because recreating the index does not address the missing OCR step. Option D is incorrect because adding a separate OCR skill is not necessary; Document Intelligence can handle OCR internally when configured correctly.

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.. Azure AI Search allows you to create a custom skill that can call an external API, such as a LUIS endpoint, to extract intents specific to your business. This integrates seamlessly into the enrichment pipeline. Option A is incorrect because Document Intelligence is for document analysis, not intent classification. Option B is incorrect because custom analyzers are for indexing text, not enrichment. Option C is incorrect because the built-in Entity Recognition skill can identify general entities but cannot identify custom business-specific intents. Option D is correct because a custom skill can invoke the LUIS model to return the top intent, and this can be mapped to a field in the index.

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.. Azure AI Search indexer does not natively support EPUB files. The Document Intelligence skill can only process formats it supports (PDF, Word, etc.). Therefore, a custom skill is needed to convert EPUB files to plain text or a supported format before they can be processed by the enrichment pipeline. Option A is wrong because Azure AI Document Intelligence does not support EPUB directly. Option C is wrong because the Document Intelligence skill cannot be modified to accept new formats. Option D is wrong because registering a new data source type does not enable processing of unsupported file formats.

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.