Implement knowledge mining and information extraction solutions

You are building a knowledge mining solution to extract insights from a large set of PDF contracts. The solution must identify parties, dates, and monetary amounts. Which Azure AI service should you use as the primary extraction engine?

Your team has built a knowledge mining pipeline using Azure AI Search and Document Intelligence. After ingestion, you notice that some documents are not appearing in search results. What is the most likely cause?

You are designing a knowledge mining solution that must extract entities from scanned handwritten forms. The forms contain signatures and checkboxes. Which combination of Azure AI services should you recommend?

Your knowledge mining solution uses Azure AI Search. Users complain that search results are not relevant. You have enabled semantic search but results still lack context. What should you do to improve relevance?

You need to extract key-value pairs from a large set of invoices. The invoices have a consistent layout but vary in format (PDF, TIFF). Which Document Intelligence model should you use?

Your knowledge mining solution ingests documents from multiple tenants. Each tenant's data must be isolated and searchable only by that tenant. You have a single Azure AI Search service. How should you implement multi-tenancy?

You are using Azure AI Search to index customer support tickets. You want to automatically extract the customer's sentiment and key phrases from each ticket. Which Azure AI service should you integrate as a skillset?

Your organization has a knowledge base of technical manuals in PDF format. You need to enable users to ask natural language questions and get answers from the manuals. Which solution should you build?

Your knowledge mining pipeline uses Azure AI Search with a custom skillset that calls an Azure Function. The function sometimes times out for large documents. What is the best way to handle this?

Which TWO actions should you take to ensure that an Azure AI Search indexer can access data from an Azure Storage account that contains sensitive data?

Which THREE components are required to build a knowledge mining solution using Azure AI Search that extracts and enriches content from PDF files?

Which TWO Azure AI services can be used to extract text from images as part of a knowledge mining pipeline?

You are reviewing a skillset definition for an Azure AI Search indexer. The indexer is configured to index 1000 PDF documents. After running the indexer, you notice that only 500 documents have sentiment scores. What is the most likely cause?

You are reviewing an index definition created with PowerShell. The index is used for a knowledge mining solution that extracts people and organizations from documents. Users report that when they type partial names in the search bar, the suggester does not return suggestions. What is the most likely reason?

You review the configuration for an Azure AI Search indexer. The indexer runs successfully but no documents are indexed. What is the most likely cause?

Your organization has a large set of PDF invoices stored in Azure Blob Storage. You need to extract line-item details (product names, quantities, prices) and store them in Azure SQL Database for downstream reporting. The invoices have varied layouts. Which Azure AI service should you use?

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?

You are building an Azure AI Search solution to index a collection of technical manuals. Users need to find documents by searching for specific terms and also have the ability to filter by document category. Which feature should you configure in the index to support filtering?

Your organization has a large corpus of legal documents stored in Azure Blob Storage. You need to build a solution that allows lawyers to ask natural language questions and get answers directly from the documents, without moving data out of Azure. Which service should you use?

You are using Azure AI Search to index a set of PDF documents. The index includes a 'content' field with the extracted text. Users report that when they search for 'budget forecast', documents containing only 'budget' or 'forecast' are ranked lower than expected. Which configuration change would improve the ranking for multi-word queries?

You are building a knowledge mining solution using Azure AI Search. You need to ensure that sensitive information such as credit card numbers is automatically removed from the indexed content. Which built-in skill should you add to your skillset?

Your organization is using Azure AI Document Intelligence to process expense reports. The reports are submitted as images and need to be classified into categories (e.g., travel, office supplies) before extraction. Which feature of Document Intelligence should you use?

You are building an Azure AI Search solution that indexes data from multiple sources, including SQL Database and Azure Blob Storage. The index must be updated within 15 minutes of any source change. Which approach should you use to achieve near-real-time indexing?

You are using Azure AI Language Service to extract key phrases from customer reviews. You notice that for reviews containing the word 'not good', the service sometimes extracts 'good' as a key phrase. What is the most likely reason?

You are building a knowledge mining solution using Azure AI Search with AI enrichment. Which TWO built-in skills can be used to extract information from images embedded in documents?

Your organization is using Azure AI Document Intelligence to process a mix of invoices and purchase orders. You need to ensure that documents are correctly classified before extraction. Which THREE steps should you take?

Which TWO features of Azure AI Search allow you to improve the relevance of search results for users?

You are building a solution to extract customer feedback from PDF documents stored in Azure Blob Storage. The solution must extract key phrases and sentiment scores, but you cannot use any pre-built models from Azure AI Language. What should you use?

You need to implement a solution that searches through a collection of scanned invoices and extracts invoice numbers, dates, and total amounts. The solution must run on a schedule without manual intervention. Which Azure service should you use?

Your company has a large collection of legal contracts in PDF format stored in Azure Blob Storage. You need to extract key clauses, parties, and effective dates using a custom model in Azure AI Document Intelligence. The model must be retrained monthly as new contract templates are added. What is the recommended approach to handle model versioning and retraining?

You are designing a knowledge mining solution that ingests documents from SharePoint Online and makes them searchable using Azure AI Search. The solution must extract text from images and perform optical character recognition (OCR) on embedded images within PDFs. Which built-in skill should you include in the skillset?

Your organization has a large repository of technical manuals in PDF format. You need to build a chatbot that can answer questions about the content of these manuals. Which combination of Azure services should you use?

You are implementing a knowledge mining solution using Azure AI Search. The data source is a large Azure Cosmos DB collection containing customer support tickets. Each ticket has fields: ticket_id, description, category, and resolution. You need to ensure that the search index can support fuzzy search and autocomplete suggestions. What should you configure in the index definition?

You need to extract personally identifiable information (PII) from a set of text documents before indexing them in Azure AI Search. The PII must be redacted. Which Azure AI service and configuration should you use?

Your team is building a knowledge mining solution for research papers. You need to automatically categorize papers into topics and extract author names, publication dates, and references. The solution must use custom models because the papers are domain-specific. Which combination of Azure services should you use?

You are designing a solution to extract structured data from a large number of handwritten forms. The forms are scanned and stored as images. Which Azure AI feature should you use?

Which TWO actions should you take to optimize the performance of an Azure AI Search solution that indexes large volumes of data?

Which THREE components are essential when building a custom skill for Azure AI Search?

Which TWO Azure AI services can be used to extract text from images as part of an Azure AI Search enrichment pipeline?

You are reviewing the skillset definition for an Azure AI Search indexer. The SplitSkill splits the document content into pages of 5000 characters. The SentimentSkill is set to run on each page. However, the sentiment analysis is not producing correct results. What is the most likely cause?

You have an Azure AI Search indexer that is configured to index PDF files from Azure Blob Storage. The indexer is not extracting any text from the PDFs, and no errors are reported. You review the indexer definition as shown. What is the most likely cause?

You executed the Azure CLI command shown to create an indexer. However, the indexer fails to run. The error indicates that the data source connection string is invalid. You have verified that the connection string is correct. What is the most likely issue?

You are designing a knowledge mining solution for a large legal firm. The solution must extract key clauses, parties, and dates from thousands of PDF contracts. You need to minimize manual labeling effort while achieving high extraction accuracy. Which Azure AI service should you use?

Your company uses Azure AI Search for an internal knowledge base. Users complain that searches for 'annual report 2023' return irrelevant results. You analyze the search index and find that the content field contains large blocks of text from PDFs. You need to improve relevance without re-indexing all documents. Which approach should you take?

You are building a knowledge mining solution to extract insights from customer support call transcripts. The solution must identify the customer's issue, the resolution provided, and the sentiment of the call. Which combination of Azure AI services should you use?

Your team is implementing a knowledge mining solution using Azure AI Search with custom skills. The custom skill, deployed as an Azure Function, calls a third-party API to enrich documents. You notice that some documents fail enrichment with HTTP 429 (too many requests) errors. You need to ensure all documents are enriched without losing data. What should you do?

You are building a knowledge mining solution that indexes technical manuals in multiple languages. The solution must enable users to search in their native language and retrieve results in the same language. Which approach should you use?

You need to extract product codes (e.g., 'PRD-12345') from scanned invoices using Azure AI Document Intelligence. The product codes always follow a pattern of three uppercase letters, a hyphen, and five digits. Which approach should you use?

You are designing a knowledge mining solution for a medical research organization. The solution must extract relationships between drugs, diseases, and genes from scientific articles. The data will be stored in a knowledge graph for querying. Which Azure AI service should you use for the extraction?

Your Azure AI Search indexer is failing to index a large number of PDFs from Azure Blob Storage. The error log shows 'Document extraction timeout' for many documents. You need to resolve this issue without losing data. What should you do?

You are building a knowledge mining solution to extract key information from handwritten forms. The forms contain checkboxes, signatures, and handwritten text. Which Azure AI service should you use?

Which TWO actions should you perform to ensure that an Azure AI Search indexer can successfully enrich documents using a custom skill that calls an external API?

Which THREE factors should you consider when designing a knowledge mining solution that uses Azure AI Search and custom skills to extract insights from large volumes of documents?

Which TWO configurations are required to enable Azure AI Search to index content from an Azure SQL database?

Which THREE Azure AI services can be used to extract text from images?

You deploy the ARM template shown in the exhibit to create an Azure AI Search indexer. The indexer fails to run, and you see an error that the skillset 'demo-skillset' does not exist. What is the most likely cause?

You have defined the custom WebApiSkill shown in the exhibit. The skill calls an Azure Function that can process up to 10 documents per second. However, you notice that the skill is failing with 429 errors. What is the most likely cause?

You are building a knowledge mining solution for legal documents using Azure AI Search. The solution must extract entities like dates, organizations, and persons from PDF files and index them. Which built-in skill should you add to the skillset to perform this extraction?

You have an Azure AI Search solution that indexes customer support tickets. The index includes a 'category' field that should be automatically populated using a custom skill that calls an Azure Machine Learning model. However, the skill fails intermittently with HTTP 429 errors. What is the most likely cause and the best fix?

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?

You are implementing a knowledge mining solution using Azure AI Search with a custom skillset. The custom skill is an Azure Function that enriches documents with additional metadata. You need to ensure that the custom skill receives the entire document content as input. How should you configure the skill's context and inputs?

Your organization uses Microsoft Purview to catalog data assets. You need to enable knowledge mining on these assets to allow users to search across structured and unstructured data. Which integration should you use to connect Microsoft Purview with Azure AI Search?

You are troubleshooting an Azure AI Search indexer that is failing with the error 'No skillset was found with the name 'skillset-name'.' The skillset exists in the same resource group. What is the most likely cause?

You need to extract key-value pairs from scanned forms as part of a knowledge mining solution. Which Azure AI service should you use?

You are designing a knowledge mining solution that ingests content from SharePoint Online. The solution must index documents and also extract custom metadata such as project name and client ID using a custom skill. The custom skill is an Azure Function that calls an external API. The external API has a rate limit of 100 requests per minute. Your indexer processes 1000 documents per hour. How should you configure the indexer to avoid hitting the rate limit?

You are using Azure AI Search to build a knowledge base for a customer support portal. The index includes a 'sentiment' field that should be populated using the Sentiment skill. However, the sentiment scores are not being written to the index. The skillset runs successfully. What is the most likely cause?

Which TWO configurations are required to enable incremental enrichment in Azure AI Search?

Which TWO Azure AI services can be used together to build a knowledge mining solution that extracts text from handwritten notes and indexes them for search?

Which THREE components are required to build a custom skill for Azure AI Search enrichment?

You have the above Azure AI Search skillset. The indexer fails with the error 'The skill 'sentiment-skill' cannot find the input '/document/pages/*' because the path does not exist.' What is the most likely cause?

You have the above Azure AI Search indexer definition. The indexer runs successfully but the 'content' field in the index is empty for all documents. What is the likely cause?

You have the above data source definition for Azure AI Search. You want to index only PDF files from the 'documents' container. How should you modify the data source?

Your organization is using Azure AI Search to index a large collection of PDF documents stored in Azure Blob Storage. The index currently returns search results, but users complain that the results are not relevant when they search using natural language phrases. You need to improve the relevance of search results without rewriting the application. What should you do?

Your company deploys an Azure AI Document Intelligence solution to extract data from invoices. During testing, you notice that some fields are not being extracted correctly, especially for invoices from a specific vendor with a non-standard layout. You need to improve extraction accuracy for this vendor's invoices. What should you do?

You are designing an enterprise search solution using Azure AI Search. The solution must index data from multiple sources: SQL Database, SharePoint Online, and custom REST APIs. The search index must support faceted navigation and filtering by metadata such as department and document type. You also need to ensure that updates to source data are reflected in the index within 5 minutes. Which approach should you use?

You are building a knowledge mining solution using Azure AI Search and Azure AI Language. The solution must extract key phrases, entities, and sentiment from customer feedback documents. After processing, the enriched content should be stored in the search index for full-text search. You need to configure the enrichment pipeline. Which two Azure AI services should you integrate?

Your company uses Azure AI Search to power a customer support portal. The search index includes product documentation and known issues. Recently, the portal's search performance has degraded, and users report slow response times. You need to identify the cause of the performance issue. What should you check first?

You are implementing a knowledge mining solution for a legal firm. The solution must ingest large volumes of legal documents (PDFs and Word files) stored in Azure Blob Storage. You need to extract text, recognize named entities (e.g., parties, judges, case numbers), and index the content for full-text search. The solution should also support redaction of sensitive information before indexing. Which combination of Azure AI services should you use?

Your organization uses Microsoft Syntex to automatically classify and extract metadata from documents stored in SharePoint. You need to extend this capability to also extract entities such as invoice numbers and dates from PDF invoices that are uploaded to SharePoint. What should you do?

You are building a chatbot using Microsoft Copilot Studio that needs to answer questions based on content from a set of technical manuals stored as PDFs. The content must be indexed and made available to the chatbot. You need to configure the knowledge source for the chatbot. What should you do?

You are designing an Azure AI Search solution that indexes customer support tickets from a SQL database and also from a custom CRM system via a custom API. The index must support scoring profiles that boost tickets from VIP customers. The VIP status is stored in the CRM system. You need to ensure that the boost factor is applied correctly. What should you do?

You are deploying a knowledge mining solution using Azure AI Search and Azure AI Document Intelligence. The solution must extract text from scanned documents, identify named entities, and index the content. You need to configure the skillset. Which TWO built-in skills should you include in the skillset?

You are using Microsoft Purview to create a knowledge map of your organization's data assets. The solution must automatically scan and classify sensitive data in Azure Blob Storage. You need to configure the scanning and classification. Which THREE actions should you perform?

You are building an Azure AI Search index that includes content from a SQL database and an Azure Cosmos DB collection. The index must support autocomplete suggestions as users type. Which TWO configurations are required to enable autocomplete?

You have an Azure AI Search skillset defined as shown in the exhibit. When you run the indexer, the enrichment pipeline produces outputs but no entities are extracted. The source documents are in English and contain clear organization and person names. What is the most likely cause?

You have an Azure AI Search index defined as shown in the exhibit. Users want to filter search results by author and by a date range, and also see a count of documents per tag. However, the filter on author is not working. What is the most likely reason?

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?

You are building a knowledge mining solution for legal documents stored in Azure Blob Storage. The solution must extract entities, key phrases, and relationships from the documents. Which Azure AI service should you use?

Your knowledge mining solution uses Azure AI Search with a custom skill that calls an Azure Function to perform complex data validation. The custom skill returns an error for some documents, but the indexer continues without raising an error. What is the most likely cause?

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?

You have the above skillset in Azure AI Search. The indexer processes a document with 12,000 characters of content. How many entity recognition skill executions occur?

Your knowledge mining pipeline uses Azure AI Search to index PDF files. You need to extract text from the PDFs and also recognize embedded tables. Which built-in skill should you use?

Which TWO options are valid ways to index content from Azure SQL Database into Azure AI Search? (Select TWO.)

You run the Azure CLI command 'az search indexer list --search-service mysearch --query "[].{name:name, status:status, lastResult:lastResult}"' and get the above output. Your indexer shows 5 warnings. What should you do to investigate the warnings?

Your organization uses Azure AI Search to index customer product reviews. The reviews are in English, Spanish, and French. You need to enable multi-lingual search so that a query in English returns relevant results from all three languages. What should you do?

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

Which THREE conditions must be met for Azure AI Search to use a custom skill? (Select THREE.)

Your company has a large repository of scanned invoices in PDF format. You need to extract invoice number, date, total amount, and vendor name from these PDFs. Which Azure AI service should you use?

You have the above indexer configuration. The indexer processes a batch of 10 documents. In that batch, 3 documents fail. What happens?

You plan to use Azure AI Search to index a large number of text documents stored in Azure Blob Storage. The documents are in English. You want to automatically extract key phrases from the content during indexing. What should you add to the skillset?

Which TWO Azure AI Search features are used to map skill outputs to search index fields? (Select TWO.)

Your knowledge mining solution uses Azure AI Document Intelligence to extract data from purchase orders. The extracted data is then indexed by Azure AI Search. You need to ensure that the search index includes the purchase order number and total amount as searchable fields. What should you do?

You are building a solution to extract key information from scanned invoices. The invoices are in PDF format and contain both printed and handwritten fields. Which Azure AI service should you use?

Your team is using Azure AI Search to index a large collection of technical manuals. Users report that searches for 'disk failure' do not return relevant results because the manuals use terms like 'hard drive crash'. Which feature should you implement to improve recall?

You need to extract entities such as dates, locations, and organization names from unstructured text documents. Which Azure AI service should you use?

You are designing a knowledge mining solution for a legal firm. The solution must index contracts and support queries like 'find all contracts where the governing law is New York'. The contracts are stored as PDFs in Azure Blob Storage. Which approach should you use?

You are using Azure AI Document Intelligence to extract data from purchase orders. The purchase orders have a table of line items. Which prebuilt model should you use?

Your knowledge mining solution uses Azure AI Search with cognitive skills. During testing, you notice that some documents are not being enriched because the skillset execution fails. Which diagnostic step should you take first?

You need to build a chatbot that answers questions based on your company's internal knowledge base. The knowledge base consists of Word documents and PDFs. Which service should you use to create a conversational interface that retrieves answers from these documents?

You are designing a knowledge mining solution that must handle sensitive customer data. The solution must ensure that personally identifiable information (PII) is not returned in search results. What should you do?

You are extracting text from scanned documents that are in French. Which capability of Azure AI Document Intelligence should you use?

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

Which THREE actions should you take when designing a custom skill for an Azure AI Search enrichment pipeline? (Choose three.)

Which TWO capabilities are available in Azure AI Search to improve search relevance? (Choose two.)

Refer to the exhibit. You have this skillset definition for an Azure AI Search enrichment pipeline. You notice that the entity recognition skill is not executing on any document. What is the most likely cause?

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?

Refer to the exhibit. You execute a search query on an Azure AI Search index and get these results. The query was 'brown fox'. Why is the first result scored higher than the second?

You are building a solution to extract key information from invoices using Azure AI Document Intelligence. The invoices contain fields such as invoice number, date, total amount, and line items. However, the model is not correctly extracting the line items. Which prebuilt model should you use?

Your company uses Azure Cognitive Search to index millions of documents. Users report that search results include irrelevant documents. You need to improve search relevance by boosting documents that contain the search term in the title field. Which scoring profile configuration should you use?

You are using Azure AI Language to perform entity recognition on customer feedback. You need to identify the sentiment expressed towards specific entities. Which feature should you use?

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?

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?

Your company has a large set of PDF documents stored in Azure Blob Storage. You need to index these documents in Azure Cognitive Search so that users can search the text content. What is the first step you should take?

You are implementing a knowledge mining solution using Azure Cognitive Search with built-in AI enrichment. The pipeline must extract named entities and key phrases from documents. The enrichment pipeline should be triggered only for documents that are larger than 1 MB. Which approach should you use?

You are using Azure AI Language to extract information from medical research papers. You need to identify terms like 'dosage', 'side effects', and 'contraindications' specific to the medical domain. Which capability should you use?

You are building a question answering solution using Azure AI Language. You have a set of frequently asked questions (FAQs) in a Word document. You need to import the FAQs into a project. Which approach should you use?

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?

You are using Azure AI Document Intelligence to extract data from scanned contracts. The contracts contain tables and handwritten signatures. Which TWO features should you enable?

You are building a knowledge mining solution that uses Azure Cognitive Search and Azure AI Language. The solution must extract key phrases and detect the language of documents. Which THREE components are required?

You are a data scientist for Contoso Pharmaceuticals. The company has thousands of research documents in PDF format stored in Azure Blob Storage. You need to build an Azure Cognitive Search solution that enables researchers to search for documents based on chemical compound names, disease mentions, and experimental results. The solution must extract these entities using a custom AI model built in Azure AI Language. Additionally, the solution must support semantic search for natural language queries. The search index must be updated daily with new documents. You have an existing Azure AI Language custom entity extraction model that recognizes chemical compounds and diseases. The model is deployed as an endpoint. You need to configure the enrichment pipeline. What should you do?

You are building a knowledge mining solution for a legal firm that needs to extract key clauses from thousands of scanned contract PDFs. The solution must identify parties, effective dates, and termination conditions. Which Azure AI service should you use as the primary component?

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?

You are designing a knowledge mining solution using Azure AI Search. The solution must process large volumes of PDFs daily. You need to minimize the cost of cognitive skills execution while ensuring the pipeline can handle transient failures. Which approach should you recommend?

Your team is building a custom ChatGPT-like copilot using Microsoft Foundry that answers questions based on internal HR policies stored in SharePoint. The solution must retrieve only the most relevant documents to minimize token usage. Which Azure AI Search feature should you configure?

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?

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?

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?

Your organization is building a knowledge base from technical manuals stored in multiple formats (PDF, Word, HTML). You need to extract text and images from these documents and create a searchable index. The solution must handle tables and preserve their structure. Which approach should you use?

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?

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?

Which THREE considerations are important when designing a custom skill for Azure AI Search that calls an external API for specialized data extraction?

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

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?

You are a solution architect at a legal firm. The firm wants to build a copilot using Microsoft Foundry that answers questions about case law documents stored in Azure Blob Storage. The copilot should use the Retrieval Augmented Generation (RAG) pattern with Azure AI Search as the vector store. The documents are in PDF format and include complex tables and footnotes. The solution must ensure that the answers are grounded in the documents and that the copilot can handle follow-up questions. You need to design the ingestion pipeline. Which approach should you take?

You are a developer at an e-commerce company. The company wants to build a product search feature that allows customers to search for products using natural language phrases like "red running shoes under $100". The product catalog is stored in Azure Cosmos DB and includes product descriptions, prices, and categories. The solution must use Azure AI Search and must extract entities from product descriptions to enable filtering (e.g., color, size, brand). The search must also support fuzzy matching for misspelled queries. You need to design the indexing pipeline. Which actions should you take?

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?

A company uses Azure AI Search to index customer support transcripts. They want to enable users to find relevant answers by asking natural language questions. Which feature should they enable in the search service?

You are deploying an Azure AI Search solution that indexes medical research papers. The papers contain sensitive patient data that must be de-identified before indexing. You need to use Azure AI Services to detect and redact personal information. Which combination of skills should you include in a skillset?

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?

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?

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?

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?

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

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?

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?

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?

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?

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?

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?

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?

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.)

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.)

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?

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?

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?

Your organization is implementing a knowledge mining solution for a research institute that needs to extract chemical compound names and reactions from scientific articles in PDF format. The solution must use a custom model because the scientific terminology is not covered by built-in skills. You have trained a custom model using Azure AI Language's custom entity recognition (NER) and deployed it as a REST endpoint. You are using Azure AI Search with a skillset. How should you integrate the custom NER model into the enrichment pipeline?

You are building a knowledge mining solution for a financial services company that needs to extract key financial terms (e.g., revenue, EBITDA, net income) from annual reports in PDF format. The solution must use a custom skill that runs a Python script to perform the extraction. The Python script is deployed as an Azure Function. You have added the custom skill to the skillset and tested it with a small set of documents. However, when processing the full dataset, the custom skill fails with time-out errors. The Azure Function has a default timeout of 230 seconds. What should you do to resolve the issue without changing the extraction logic?

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?