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CCNA Implement knowledge mining and information extraction solutions Questions

75 of 153 questions · Page 1/3 · Implement knowledge mining and information extraction solutions · Answers revealed

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1

MCQeasy

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?

A.Azure AI Bot Service and Azure AI Language

B.Azure AI Document Intelligence and Azure AI Search

C.Azure AI Language and Azure AI Search

D.Azure AI Translator and Azure AI Language

AnswerC

Azure AI Language extracts entities and sentiment; Azure AI Search indexes them.

Why this answer

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

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2

MCQmedium

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?

A.Azure AI Document Intelligence

B.Azure AI Translator

C.Azure AI Language

D.Azure AI Search

AnswerC

Azure AI Language includes pre-built capabilities for entity recognition, key phrase extraction, and relationship extraction.

Why this answer

Azure AI Language provides pre-built capabilities for entity extraction, key phrase extraction, and relationship extraction from text. This service includes features like Named Entity Recognition (NER), key phrase extraction, and document analysis that directly meet the requirements for extracting entities, key phrases, and relationships from legal documents. The other options lack one or more of these specific text analytics capabilities.

Exam trap

The trap here is that candidates often confuse Azure AI Document Intelligence (which handles OCR and form extraction) with Azure AI Language (which handles text analytics like entity and key phrase extraction), leading them to pick A when the question explicitly asks for extracting entities, key phrases, and relationships from text.

How to eliminate wrong answers

Option A is wrong because Azure AI Document Intelligence is designed for extracting structured data (like tables, key-value pairs, and text) from scanned documents and forms, not for performing entity, key phrase, or relationship extraction from unstructured text. Option B is wrong because Azure AI Translator focuses on translating text between languages and does not include entity extraction, key phrase extraction, or relationship analysis. Option D is wrong because Azure AI Search is a search indexing and query service that can index data from various sources but does not natively perform entity extraction, key phrase extraction, or relationship extraction; it relies on an external AI enrichment pipeline (often using Azure AI Language) for those tasks.

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3

MCQhard

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?

A.The --query parameter is incorrectly formatted

B.The skillset name does not exist in the search service

C.The parsingMode should be 'json' for PDF files

D.The data source's connection string is not properly stored or the data source is not configured with the correct credentials

AnswerD

The error indicates invalid connection string; the data source definition likely has a wrong or expired key.

Why this answer

The error message explicitly states that the data source connection string is invalid, even though you have verified the string itself is correct. This typically occurs when the connection string is not stored correctly in the search service (e.g., it was truncated, escaped improperly, or contains special characters that were misinterpreted) or when the data source configuration lacks the correct credentials (e.g., the managed identity is not enabled, or the key/secret is missing). Option D directly addresses this mismatch between a verified string and the service's stored configuration.

Exam trap

The trap here is that candidates assume a 'verified correct' connection string means the data source is fully configured, but Azure Cognitive Search stores the string independently and may silently alter it during ingestion, or the credentials (like a managed identity) may not be properly assigned to the search service.

How to eliminate wrong answers

Option A is wrong because the --query parameter is used to filter or format CLI output, not to define the indexer's behavior; an incorrectly formatted --query would cause a syntax error in the CLI command itself, not a runtime indexer failure about an invalid connection string. Option B is wrong because if the skillset name did not exist, the error would reference a missing skillset (e.g., 'Skillset not found'), not an invalid data source connection string. Option C is wrong because parsingMode determines how the indexer interprets the document content (e.g., 'json' for JSON arrays, 'jsonLines' for newline-delimited JSON), but PDF files are typically indexed using the default 'text' mode or a skillset with OCR; setting parsingMode to 'json' for a PDF would cause a parsing error, not a connection string error.

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4

MCQeasy

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?

A.Upload the PDFs to a SharePoint library and use SharePoint as the knowledge source.

B.Create an Azure AI Search index from the PDFs and connect it to Copilot Studio as a knowledge source.

C.Add a web search connector to the chatbot to search for information online.

D.Store the content in Azure SQL Database and use it as a knowledge source.

AnswerB

Azure AI Search indexes content and can be used as a knowledge source.

Why this answer

Azure AI Search provides a dedicated indexing service that can extract and store text from PDFs, enabling Copilot Studio to perform semantic search and retrieval-augmented generation (RAG). This allows the chatbot to answer questions based on the specific content of the technical manuals, rather than relying on external or unstructured data sources.

Exam trap

The trap here is that candidates often assume SharePoint is the natural choice for document-based knowledge because it is a familiar Microsoft tool, but they overlook that Azure AI Search is specifically designed for deep content indexing and retrieval, which is required for accurate chatbot responses from technical manuals.

How to eliminate wrong answers

Option A is wrong because uploading PDFs to a SharePoint library and using SharePoint as a knowledge source would require the chatbot to query SharePoint's search API, which is not optimized for deep PDF content extraction and may not support the granular indexing needed for accurate Q&A. Option C is wrong because adding a web search connector would make the chatbot search the public internet, which does not guarantee access to the private technical manuals and introduces irrelevant or unverified results. Option D is wrong because storing content in Azure SQL Database would require manual extraction and structuring of text from PDFs into relational tables, which is inefficient and loses the document context that Azure AI Search's built-in PDF parsing provides.

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5

MCQmedium

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?

A.Azure AI Language

B.Azure AI Document Intelligence

C.Azure AI Search

D.Azure AI Vision

AnswerB

Document Intelligence has a pre-built invoice model that extracts invoice-specific fields.

Why this answer

Azure AI Document Intelligence (formerly Form Recognizer) is purpose-built for extracting structured data (fields like invoice number, date, total amount, vendor name) from scanned documents such as invoices and receipts. It uses prebuilt models trained specifically on invoice layouts and supports both OCR and key-value pair extraction from PDFs.

Exam trap

A common mistake is to choose Azure AI Vision because it performs OCR on scanned PDFs, but it does not provide prebuilt models for extracting structured fields like invoice data. Azure AI Document Intelligence (formerly Form Recognizer) is the correct service as it specializes in extracting key-value pairs from forms and documents.

How to eliminate wrong answers

Option A is wrong because Azure AI Language is designed for text analytics (sentiment, key phrases, entity recognition) and does not natively extract structured fields from scanned PDF documents. Option C is wrong because Azure AI Search is a search indexing and query service, not a document data extraction tool; it requires pre-extracted content to index. Option D is wrong because Azure AI Vision provides general OCR and image analysis capabilities but lacks the specialized prebuilt invoice models and field extraction logic that Document Intelligence offers.

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6

Multi-Selectmedium

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?

Select 2 answers

A.Add a built-in Sentiment skill to the skillset.

B.Implement a custom skill to normalize sentiment values.

C.Create a custom sentiment analysis skill using Azure AI Language.

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

E.Configure an output field mapping in the indexer to map the sentiment output to the index field.

AnswersA, E

The sentiment skill generates sentiment scores.

Why this answer

Options A and E are correct. The enrichment pipeline must include a built-in Sentiment skill (A) to analyze sentiment from the support ticket text, and an output field mapping must be configured in the indexer (E) to map the skill's output to the 'sentiment' field in the index. Option B is wrong because a custom skill is not needed as the sentiment skill is built-in.

Option C is wrong because the built-in sentiment skill uses Azure AI Language, so a custom skill is unnecessary. Option D is wrong because Collection(Edm.String) is not the correct type for sentiment; a simple string or numeric type would be appropriate.

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7

Multi-Selectmedium

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?

Select 2 answers

A.Merge skill

B.LanguageDetection skill

C.OCR skill

D.EntityRecognition skill

E.KeyPhraseExtraction skill

AnswersC, D

Extracts text from scanned documents.

Why this answer

The OCR skill extracts text from scanned images. The EntityRecognition skill identifies named entities. The Merge skill is not required because OCR output is already text.

The KeyPhraseExtraction skill extracts key phrases, not entities. The LanguageDetection skill detects language, not entities.

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8

Multi-Selecthard

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?

Select 3 answers

A.Run a full scan of the Blob Storage to discover and classify data.

B.Create custom classification rules for sensitive data types.

C.Apply sensitivity labels to the classified data.

D.Create a scan rule set that includes the desired classification rules.

E.Register the Azure Blob Storage account as a data source in Purview.

AnswersA, D, E

Scanning is required to apply classifiers.

Why this answer

Running a full scan of Azure Blob Storage in Microsoft Purview is the mechanism that discovers and classifies sensitive data. A full scan examines all files and metadata, applying the configured classification rules to identify sensitive information types such as credit card numbers or social security numbers. This action is essential for populating the knowledge map with classified data assets.

Exam trap

The trap here is that candidates often confuse the required actions for configuring scanning (registering the source, creating a scan rule set, and running a scan) with optional or subsequent steps like creating custom rules or applying sensitivity labels, leading them to select B or C instead of the correct three.

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9

MCQhard

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.Reduce the batch size in the indexer

B.Increase the skill timeout

C.Configure a retry policy on the custom skill

D.Schedule the indexer to run less frequently

AnswerC

A retry policy with exponential backoff handles 429 errors by retrying after delays.

Why this answer

Azure AI Search indexers can be configured with a retry policy for custom skills. When a custom skill returns HTTP 429 (Too Many Requests), a retry policy will automatically retry the skill execution after a backoff delay, helping to handle rate limiting from the external API. Option A is incorrect: reducing the batch size may reduce the number of requests per batch, but if the API rate limit is per request, it may not resolve the 429 error.

Option B is incorrect: increasing the skill timeout does not address rate limiting; it only allows more time for a single request. Option D is incorrect: scheduling the indexer less frequently only reduces the frequency of index runs, not the rate of requests during a run.

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10

MCQhard

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?

A.Enable spell correction in the search query

B.Add a custom scoring profile based on term frequency

C.Change the index analyzer to a different language

D.Enable semantic ranking on the search index

AnswerD

Semantic ranking re-ranks results using deep learning models to better match query intent.

Why this answer

Semantic ranking re-ranks search results using deep learning models to understand the intent and context of the query, rather than just keyword matching. Since the content field contains large text blocks from PDFs, semantic ranking can extract the most relevant passages and improve result relevance without requiring re-indexing or modifying the existing index schema.

Exam trap

The trap here is that candidates often confuse semantic ranking with simple scoring profile adjustments or language analyzers, failing to recognize that only semantic ranking can understand the meaning behind a query and extract relevant passages from large text blocks without re-indexing.

How to eliminate wrong answers

Option A is wrong because spell correction only fixes typos in the query string; it does not address the core issue of poor relevance caused by large, unstructured text blocks. Option B is wrong because custom scoring profiles based on term frequency still rely on keyword matching and cannot understand the semantic meaning or context of the query within large text blocks. Option C is wrong because changing the index analyzer to a different language only affects tokenization and stemming for language-specific text; it does not improve relevance for queries in the same language or handle large text blocks.

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11

MCQmedium

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?

A.OCR skill

B.Translation skill

C.Key phrase extraction skill

D.Entity recognition skill

AnswerA

Extracts text from images and embedded images in PDFs.

Why this answer

The OCR skill (Optical Character Recognition) is the correct built-in skill for extracting text from images and performing OCR on embedded images within PDFs in Azure AI Search. It specifically handles image files (e.g., JPEG, PNG) and embedded images in PDFs, outputting text that can be indexed and searched. Other skills like translation, key phrase extraction, or entity recognition do not perform text extraction from images.

Exam trap

The trap here is that candidates may confuse the OCR skill with other text-processing skills like key phrase extraction or entity recognition, mistakenly thinking those can also extract text from images, but only the OCR skill is designed for image-to-text conversion.

How to eliminate wrong answers

Option B is wrong because the Translation skill translates text from one language to another, but it cannot extract text from images or perform OCR on embedded images. Option C is wrong because the Key Phrase Extraction skill identifies key phrases from existing text, but it does not extract text from images or perform OCR. Option D is wrong because the Entity Recognition skill identifies entities (e.g., people, organizations) from text, but it cannot extract text from images or perform OCR on embedded images.

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12

MCQmedium

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?

A.The skill output is not mapped to the index.

B.The skills are in the wrong order.

C.The documents are not in English.

D.The '/document/content' field is an array, but the skill expects a string.

AnswerD

EntityRecognitionSkill expects a string input.

Why this answer

The most likely cause is that the '/document/content' field is an array, but the Entity Recognition skill expects a string input. In Azure AI Search, if the source field is an array, the skill cannot process it directly, resulting in no entities being extracted even though the documents contain clear organization and person names.

Exam trap

The trap here is that candidates often assume entity extraction fails due to language mismatch or skill ordering, but Azure AI Search's Entity Recognition skill is strict about input types, and an array input will cause silent failure without any error in the pipeline output.

How to eliminate wrong answers

Option A is wrong because the skill output not being mapped to the index would still produce entities in the enrichment pipeline, but they simply wouldn't appear in the search index; the question states the pipeline produces outputs but no entities are extracted, indicating the issue is earlier in the pipeline. Option B is wrong because the order of skills (e.g., entity recognition before language detection) does not prevent entity extraction; the Entity Recognition skill can run independently as long as its input field is correctly populated. Option C is wrong because the source documents are explicitly stated to be in English, and the Entity Recognition skill supports English, so language is not the issue.

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13

MCQhard

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?

A.Enable debug mode on the skillset

B.Review the indexer execution history in the portal

C.Re-run the indexer with a fresh document

D.Check the indexer logs in Azure Monitor

AnswerA

Debug mode provides detailed per-document skill execution logs.

Why this answer

When a skillset execution fails during enrichment, the first diagnostic step is to enable debug mode on the skillset. Debug mode allows you to step through each skill execution, inspect inputs and outputs, and identify exactly where the failure occurs. This targeted approach is more efficient than reviewing logs or re-running the indexer, because it provides granular, per-document debugging without waiting for a full indexer run.

Exam trap

The trap here is that candidates often jump to reviewing indexer execution history or logs (options B or D) because they seem like standard troubleshooting steps, but the question specifically asks for the *first* diagnostic step when skillset execution fails, and debug mode is the most direct and efficient tool for that purpose.

How to eliminate wrong answers

Option B is wrong because reviewing the indexer execution history in the portal shows overall status and errors at the document or skill level, but it does not provide the detailed, step-by-step skill execution trace needed to pinpoint the exact failure point in the enrichment pipeline. Option C is wrong because re-running the indexer with a fresh document does not help diagnose why the existing documents failed; it only tests whether the issue is reproducible, which is less efficient than using debug mode to inspect the actual failed documents. Option D is wrong because checking the indexer logs in Azure Monitor aggregates operational data but lacks the per-skill input/output inspection that debug mode offers, making it a secondary step after identifying the failing skill.

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14

MCQeasy

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?

A.Custom extraction model

B.Layout model

C.Read model

D.Premade invoice model

AnswerD

Built specifically for invoices.

Why this answer

The premade invoice model (D) is specifically designed to extract key-value pairs from invoices, including fields like invoice date, total amount, and vendor details, even when the invoices vary in format (PDF, TIFF). It leverages pre-trained deep learning models optimized for invoice layouts, making it the most efficient choice for this task without requiring custom training.

Exam trap

The trap here is that candidates may confuse the Layout model's ability to extract tables and structure with the specific key-value pair extraction needed for invoices, overlooking that the premade invoice model is purpose-built for this exact use case.

How to eliminate wrong answers

Option A is wrong because a custom extraction model requires labeled training data and is overkill for invoices with a consistent layout, as the premade model already handles this scenario. Option B is wrong because the Layout model extracts text, tables, and structure but does not specifically target key-value pairs like invoice fields, requiring additional post-processing. Option C is wrong because the Read model only performs OCR to extract raw text and does not identify or structure key-value pairs, making it unsuitable for invoice data extraction.

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15

MCQeasy

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

A.Computer Vision

B.Azure AI Language Service

C.Azure AI Document Intelligence

D.Azure AI Speech Service

AnswerB

Language Service provides NER capabilities.

Why this answer

Azure AI Language Service provides pre-built capabilities for entity recognition, including extracting dates, locations, and organization names from unstructured text via its Named Entity Recognition (NER) feature. This service is specifically designed for text analytics tasks, making it the correct choice for entity extraction from text documents.

Exam trap

The trap here is that candidates often confuse Azure AI Document Intelligence (which extracts structured data from forms) with Azure AI Language Service (which performs general text analytics like NER), leading them to pick Option C for entity extraction from unstructured text.

How to eliminate wrong answers

Option A is wrong because Computer Vision is designed for analyzing images and video, not unstructured text documents; it extracts visual features like objects, faces, and OCR text, not semantic entities like dates or organizations. Option C is wrong because Azure AI Document Intelligence (formerly Form Recognizer) focuses on extracting structured data from forms and documents using pre-built or custom models, but its primary purpose is layout analysis and key-value pair extraction, not general-purpose entity recognition from unstructured text. Option D is wrong because Azure AI Speech Service handles audio-to-text transcription and speech synthesis, not entity extraction from text; it converts spoken language to text but does not perform semantic analysis like NER.

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16

MCQhard

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?

A.Configure the custom skill to execute in batch mode and set a retry policy on the indexer

B.Increase the number of partitions in the Azure AI Search service

C.Enable indexer error handling to skip failed documents

D.Scale out the Azure Function to multiple instances

AnswerA

Batch mode reduces API calls, and retry policy handles transient failures.

Why this answer

Configuring the custom skill to execute in batch mode reduces the number of HTTP requests to the third-party API by processing multiple documents per invocation, while setting a retry policy on the indexer ensures that failed documents due to transient HTTP 429 errors are automatically retried. This combination prevents data loss by not skipping documents and by handling rate-limiting gracefully.

Exam trap

The trap here is that candidates often confuse scaling the Azure Function (Option D) as a solution for rate limiting, when in fact it increases the problem, or they assume skipping errors (Option C) is acceptable, missing the requirement to not lose data.

How to eliminate wrong answers

Option B is wrong because increasing the number of partitions in Azure AI Search scales the search index and query throughput, not the custom skill execution or the rate at which the indexer calls the Azure Function; it does not address HTTP 429 errors from the third-party API. Option C is wrong because enabling indexer error handling to skip failed documents would cause data loss, which contradicts the requirement to enrich all documents without losing data. Option D is wrong because scaling out the Azure Function to multiple instances increases the concurrency of function invocations, which would actually exacerbate the HTTP 429 errors by sending more requests to the third-party API, not resolve them.

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17

MCQeasy

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?

A.Azure AI Vision's image analysis

B.Azure AI Speech to text

C.Azure Bot Service

D.Azure AI Document Intelligence's OCR capability

AnswerD

OCR extracts text from images, including handwriting.

Why this answer

Azure AI Document Intelligence's OCR capability is specifically designed to extract structured data from scanned documents, including handwritten forms. It uses advanced optical character recognition (OCR) and layout analysis to identify text, tables, and key-value pairs, making it the correct choice for this scenario.

Exam trap

The trap here is that candidates often confuse Azure AI Vision's general image analysis with Document Intelligence's specialized OCR, but the key differentiator is that Document Intelligence is purpose-built for extracting structured data from forms and documents, including handwriting.

How to eliminate wrong answers

Option A is wrong because Azure AI Vision's image analysis focuses on describing images, detecting objects, and generating captions, not on extracting structured data from handwritten text. Option B is wrong because Azure AI Speech to text converts spoken audio into text, not written or handwritten content from images. Option C is wrong because Azure Bot Service is a framework for building conversational agents, not a tool for OCR or document data extraction.

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18

MCQhard

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?

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

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

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

D.The page split mode with overlap may still cut sentences, causing sentiment to be evaluated on incomplete sentences.

AnswerD

Splitting at page boundaries can break sentences, leading to inaccurate sentiment analysis.

Why this answer

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.

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19

MCQeasy

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?

A.Document Extraction skill

B.OCR skill

C.Custom Web API skill

D.Entity Recognition skill

AnswerA

This skill extracts text from files like PDFs; for tables, you need additional processing like Document Intelligence.

Why this answer

The Document Extraction skill is designed to extract text from binary files such as PDFs. However, it does not natively extract embedded tables as structured data. For table recognition, you would typically need additional processing, such as using the OCR skill for image-based tables or integrating Azure Document Intelligence.

Therefore, among the provided options, Document Extraction is the most relevant for text extraction but does not fully address table recognition without supplementary steps.

Exam trap

The pitfall is that candidates may assume the Document Extraction skill can extract embedded tables from PDFs as structured data. In reality, it only extracts raw text from text-based PDFs. For table recognition, you would need additional steps, such as using the OCR skill for scanned tables or integrating Azure Document Intelligence.

How to eliminate wrong answers

Option B is wrong because the OCR skill is used for extracting text from images (e.g., scanned documents or photos) and does not natively handle embedded tables in PDFs as structured data; it outputs raw text without table recognition. Option C is wrong because the Custom Web API skill allows you to call an external endpoint for custom processing, but it is not a built-in skill for PDF text and table extraction—it requires you to build and host your own logic. Option D is wrong because the Entity Recognition skill identifies named entities (e.g., people, organizations, locations) from text, but it does not extract raw text or tables from PDF files.

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20

MCQhard

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?

A.Azure AI Document Intelligence with a custom neural model and Azure AI Language for entity linking

B.Azure AI Document Intelligence with a premade model and Azure AI Computer Vision

C.Azure AI Computer Vision (OCR) and Azure AI Search with integrated vectorization

D.Azure Cognitive Search and Azure AI Document Intelligence with a premade model

AnswerA

Custom neural models support handwriting; Language can enrich entities.

Why this answer

The scenario requires extracting entities from scanned handwritten forms, which demands a custom neural model in Azure AI Document Intelligence to handle the variability of handwriting, signatures, and checkboxes. Azure AI Language's entity linking then enriches the extracted entities by disambiguating and linking them to a knowledge base, providing structured, meaningful output. The premade models in Document Intelligence are designed for printed text and common layouts, not handwritten content, making a custom neural model essential.

Exam trap

The trap here is that candidates assume premade Document Intelligence models can handle handwriting, but they are designed for printed text only, and they overlook the need for entity linking to disambiguate extracted entities from unstructured handwritten forms.

How to eliminate wrong answers

Option B is wrong because a premade model in Azure AI Document Intelligence is optimized for printed text and standard form layouts, not handwritten forms with signatures and checkboxes, and Azure AI Computer Vision's OCR alone cannot reliably extract entities from handwriting or handle the complexity of signatures and checkboxes. Option C is wrong because Azure AI Computer Vision (OCR) provides raw text extraction but lacks the entity extraction and linking capabilities needed for knowledge mining, and Azure AI Search with integrated vectorization is for indexing and retrieval, not for extracting entities from scanned forms. Option D is wrong because Azure Cognitive Search is a search and indexing service, not an extraction service, and Azure AI Document Intelligence with a premade model cannot accurately extract entities from handwritten content.

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21

MCQmedium

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?

A.The parsingMode is set to 'json' instead of 'default' or 'text'

B.The field mapping from 'content' to 'content' is redundant and causes a conflict

C.The field mapping for 'metadata_storage_path' should be to 'metadata_storage_path'

D.The dataToExtract is set to 'contentAndMetadata' which is not supported for PDFs

AnswerA

JSON mode expects JSON files, not PDFs.

Why this answer

The parsingMode set to 'json' tells the indexer to expect JSON files, not PDFs. Since PDFs are binary or text-based, the indexer cannot extract any content, but because it does not fail (JSON parsing simply returns no text), no error is reported. Changing parsingMode to 'default' or 'text' allows the indexer to correctly parse the PDF binary stream and extract text.

Exam trap

The trap here is that candidates assume the indexer will automatically detect the file type and parse accordingly, but Azure AI Search requires explicit configuration of parsingMode to handle non-JSON formats like PDFs, and the absence of errors misleads candidates into looking at other configuration details.

How to eliminate wrong answers

Option B is wrong because a field mapping from 'content' to 'content' is not redundant; it is the default mapping that explicitly passes the extracted content to the search index field, and it does not cause a conflict. Option C is wrong because the field mapping for 'metadata_storage_path' is already correctly mapped to 'metadata_storage_path' in the provided definition; the issue is unrelated to metadata fields. Option D is wrong because 'contentAndMetadata' is a valid dataToExtract value for PDFs; it instructs the indexer to extract both text content and metadata, and it is fully supported for PDF files.

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22

Multi-Selectmedium

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?

Select 2 answers

A.Azure AI Search

B.Azure AI Document Intelligence

C.Azure AI Vision (Read API)

D.Azure AI Language

E.Azure AI Translator

AnswersA, C

Indexes extracted text.

Why this answer

Azure AI Search (A) is the correct service because it provides the indexing and querying capabilities required for a knowledge mining solution. Azure AI Vision's Read API (C) is correct because it extracts printed and handwritten text from images, which is the first step in making handwritten notes searchable. Together, they form a pipeline where the Read API extracts text, and Azure AI Search indexes that extracted text for full-text search.

Exam trap

The trap here is that candidates often confuse Azure AI Document Intelligence (which handles structured forms) with Azure AI Vision's Read API (which handles general OCR including handwriting), leading them to incorrectly select Document Intelligence for handwriting extraction.

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23

MCQeasy

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?

A.Key Phrase Extraction

B.Entity Recognition

C.Sentiment Analysis

D.OCR

AnswerA

Key Phrase Extraction skill extracts key phrases from text.

Why this answer

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

OCR extracts text from images, not from text-based tickets.

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24

MCQhard

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?

A.Add a Document Intelligence skill to classify intents.

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

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

D.Create a custom skill in the skillset that calls the LUIS endpoint and returns the top intent.

AnswerD

Custom skills enable integration with external services like LUIS for custom entity or intent extraction.

Why this answer

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.

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25

MCQhard

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?

A.The outputFieldMapping references '/document/content' which is not produced by the skillset.

B.The indexer schedule is too frequent.

C.The fieldMapping for 'metadata_storage_path' is incorrect.

D.The batchSize is too large, causing some items to fail silently.

AnswerA

Skillset must output that path.

Why this answer

The indexer runs successfully but the 'content' field is empty because the outputFieldMapping maps a path '/document/content' that does not exist in the enrichment tree produced by the skillset. The skillset must output a node at that path; if it doesn't, the mapping cannot populate the field. Option A correctly identifies this as the likely cause.

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26

MCQmedium

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.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.

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

C.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.

D.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.

AnswerC

Covers all requirements: topics, sentiment, entities, and summarization.

Why this answer

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.

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27

MCQmedium

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?

A.Custom template model

B.Prebuilt-layout model

C.Custom neural model

D.Custom composed model

AnswerC

Neural models handle varying layouts and handwriting better.

Why this answer

Custom neural model. Azure AI Document Intelligence offers custom template models for forms with fixed layouts and custom neural models for forms with varying layouts and handwriting. Neural models use deep learning to handle variability in structure and handwriting, making them ideal for this scenario.

Option A (Custom template model) assumes a fixed layout and fails with varying layouts. Option B (Prebuilt-layout model) is a prebuilt model that extracts text, tables, and selection marks but not customized key-value pairs for your forms. Option D (Custom composed model) is a combination of multiple models, but the primary choice for varying layouts is the neural model, not composed.

Therefore, C is the best choice.

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28

MCQeasy

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?

A.Review the search service metrics for high query latency and CPU usage.

B.Check the size of the index storage in the Azure portal.

C.Ensure the index schema does not have too many fields.

D.Verify that the skillset is not running during peak hours.

AnswerA

Metrics help identify performance bottlenecks.

Why this answer

High query latency and CPU usage are direct indicators of performance bottlenecks in Azure AI Search. The search service metrics in the Azure portal provide real-time data on query execution time and resource consumption, which are the first signals to investigate when users report slow response times. Checking these metrics helps identify whether the issue stems from excessive query load, insufficient replicas, or inefficient query execution.

Exam trap

The trap here is that candidates may confuse indexing-related metrics (like skillset execution or index size) with query performance metrics, leading them to check storage size or schema complexity instead of the direct performance indicators of query latency and CPU usage.

How to eliminate wrong answers

Option B is wrong because index storage size alone does not directly cause slow query response times; large indexes can be handled efficiently with proper partitioning and replicas, and storage metrics are more relevant to capacity planning than immediate performance degradation. Option C is wrong because having too many fields in the index schema can increase indexing time but does not typically cause slow query response times; query performance is more affected by the number of searchable fields and the complexity of queries, not the total field count. Option D is wrong because skillsets run during indexing, not querying, and their execution does not impact query response times; query performance is independent of indexing operations unless the service is under-provisioned for concurrent workloads.

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29

Multi-Selecthard

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

Select 3 answers

A.Index

B.Skillset

C.Semantic configuration

D.Data source

E.Indexer

AnswersB, D, E

Correct. A skillset defines the AI enrichment steps such as OCR, entity recognition, or language detection that transform the raw PDF content into enriched data.

Why this answer

To build a knowledge mining solution that extracts and enriches content from PDF files using Azure AI Search, three components are required: a data source (to connect to the PDFs), a skillset (to apply AI enrichment like OCR and entity extraction), and an indexer (to orchestrate the pipeline and ingest the enriched content into the index). While an index is also necessary to store the results, it is not considered a pipeline component in the same context, and the question asks for the three components of the extraction and enrichment pipeline.

Exam trap

The trap here is that candidates often include the index as a required component or overlook the indexer. However, the three core pipeline components are data source, skillset, and indexer. The index is a separate entity that is created as part of the solution but is not one of the three pipeline components asked for.

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30

Multi-Selecteasy

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

Select 2 answers

A.Synonym maps

B.Semantic search

C.Suggesters

D.Scoring profiles

E.Filterable fields

AnswersB, D

Semantic search uses AI to understand the intent of the query and improve ranking.

Why this answer

Semantic search (B) improves relevance by using deep neural networks to understand the intent and context behind a query, re-ranking results based on semantic relevance rather than just keyword matching. This allows users to find more meaningful results even when their query doesn't exactly match indexed terms.

Exam trap

The trap here is that candidates often confuse features that expand recall (synonym maps) or improve user experience (suggesters) with features that directly improve relevance ranking, leading them to select A or C instead of the correct scoring profiles and semantic search.

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31

Multi-Selecteasy

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.)

Select 2 answers

A.Configure the index to use a custom analyzer.

B.Add the Key Phrase Extraction skill to the skillset.

C.Deploy a custom skill using Azure Functions to extract entities.

D.Enable OCR (Optical Character Recognition) in the indexer configuration.

E.Add the Entity Recognition skill to the skillset.

AnswersD, E

OCR extracts text from PDFs so that the Entity Recognition skill can process it.

Why this answer

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.

Exam trap

Candidates may mistakenly believe that a custom analyzer (Option A) or custom skill (Option C) is needed, but built-in skills suffice. Also, they may overlook that OCR (Option D) must be enabled to process scanned PDFs before entity recognition.

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32

MCQmedium

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?

A.Azure AI Document Intelligence

B.Azure AI Search with semantic search

C.Azure AI Language Service with custom question answering

D.Azure AI Computer Vision

AnswerB

Semantic search in Azure AI Search can understand natural language queries and return relevant passages.

Why this answer

Azure AI Search with semantic search is the correct choice because it allows you to index legal documents stored in Azure Blob Storage, then query them using natural language questions. The semantic search capability re-ranks results based on contextual understanding, enabling the system to extract precise answers from the document corpus without moving data out of Azure.

Exam trap

The trap here is that candidates often confuse Azure AI Document Intelligence (which extracts structured data from forms) with a search-based Q&A solution, failing to recognize that Azure AI Search with semantic search is the correct service for querying unstructured text corpora with natural language.

How to eliminate wrong answers

Option A is wrong because Azure AI Document Intelligence is designed for extracting structured data (e.g., key-value pairs, tables) from scanned documents, not for answering natural language questions across a large corpus. Option C is wrong because Azure AI Language Service with custom question answering requires a predefined FAQ or QnA pair structure and does not natively index and search unstructured document content like legal documents. Option D is wrong because Azure AI Computer Vision is focused on image analysis and optical character recognition (OCR), not on text-based question answering or semantic search.

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33

MCQhard

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?

A.Use a scoring profile with a tag boost that references a field in the SQL database.

B.Include the VIP status field in the index by pushing it from the CRM system via the push API, then use a scoring profile with a field boost.

C.Use a scoring profile with a function that queries the CRM system at query time.

D.Configure a custom analyzer that boosts VIP terms.

AnswerB

Field boost in scoring profile works on indexed fields.

Why this answer

The VIP status must be present in the index for a scoring profile to use it. By pushing the VIP status field from the CRM system via the Push API, you include it in the index. Then, a scoring profile with a field boost can apply a higher score to documents where the VIP field matches a specific value, ensuring the boost is applied at query time without external dependencies.

Exam trap

The trap here is that candidates may think scoring profiles can dynamically fetch data from external sources at query time, but Azure AI Search requires all scoring data to be pre-indexed.

How to eliminate wrong answers

Option A is wrong because a tag boost requires a field already in the index to contain the tags; referencing the SQL database directly is not possible as scoring profiles operate only on indexed fields. Option C is wrong because scoring profiles cannot query external systems at query time; all scoring logic must be based on fields already stored in the index. Option D is wrong because custom analyzers affect tokenization and indexing, not scoring; boosting VIP terms would require a scoring profile, not an analyzer.

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34

Multi-Selecteasy

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

Select 3 answers

A.Azure AI Speech

B.Azure AI Search

C.Azure AI Document Intelligence layout model

D.Azure AI Vision OCR

E.Azure AI Language custom NER

AnswersB, C, D

Azure AI Search can extract text from images by leveraging OCR skills (e.g., Azure AI Vision OCR) within a cognitive search enrichment pipeline, making it a valid service for this purpose.

Why this answer

The question asks for three Azure AI services that can extract text from images. Azure AI Document Intelligence layout model (C) and Azure AI Vision OCR (D) are direct text extraction services. Azure AI Search (B) can also extract text from images by using OCR as a built-in cognitive skill in an enrichment pipeline.

Option A (Speech) processes audio, not images. Option E (Language custom NER) works only on pre-extracted text. Therefore, the correct options are B, C, and D.

Exam trap

The trap is that candidates may overlook Azure AI Search as a text extraction service because it is not a dedicated OCR service, but it can indeed extract text from images when configured with an OCR skill.

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35

Multi-Selecthard

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

Select 3 answers

A.The custom skill must be stateless.

B.The custom skill must handle batch requests of up to 1000 records per call.

C.The custom skill must be accessible via HTTPS from the Azure AI Search service.

D.The custom skill must be hosted in an Azure Function.

E.The custom skill must respond with a valid JSON format that includes an 'errors' array for failures.

AnswersB, C, E

The skill must be able to process a batch of records; the max batch size is 1000 by default.

Why this answer

Azure AI Search custom skills must be capable of processing batch requests containing up to 1000 records per call. This requirement ensures efficient indexing by allowing the search service to send multiple documents in a single HTTP request, reducing latency and overhead. The skill's endpoint must accept and return an array of records, with each record processed independently.

Exam trap

The trap here is that candidates often assume custom skills must be stateless or hosted only in Azure Functions, but the actual requirements focus on HTTPS accessibility, batch handling of up to 1000 records, and returning a valid JSON response with an 'errors' array for failures.

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36

MCQmedium

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?

A.The indexer and skillset are in different Azure AI Search services.

B.The skillset name has a typo or is case-sensitive.

C.The indexer's data source is misconfigured.

D.The indexer does not have permission to access the skillset.

AnswerA

Indexer references skillset by name; must be in same service.

Why this answer

The error 'No skillset was found with the name 'skillset-name'' indicates that the indexer cannot locate the skillset. The most likely cause is that the indexer and skillset are in different Azure AI Search services because skillsets are scoped to a specific search service and cannot be referenced across services. Option A (the indexer and skillset are in different Azure AI Search services) is correct.

Option B is incorrect because while skillset names are case-sensitive, the error would likely be different if it were simply a typo or case mismatch. Option C is incorrect because the error is about the skillset, not the data source. Option D is incorrect because permission issues typically result in an authorization error, not a 'not found' error.

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37

MCQeasy

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?

A.The connection string in the data source is invalid

B.The container name is incorrect

C.The indexer does not have a skillset or field mappings to populate the 'content' field from the blob

D.The maxFailedItems parameter is set to -1, which causes the indexer to skip all documents

AnswerC

Without a skillset or field mappings, the blob's content is not extracted into the content field.

Why this answer

When an Azure AI Search indexer runs successfully but indexes zero documents, the most common cause is that the indexer lacks a skillset or field mappings to extract and map the blob content (e.g., the 'content' field) into the search index. Without explicit field mappings or a skillset that defines how to populate the index fields from the blob's default properties, the indexer may complete execution without transferring any data into the index fields.

Exam trap

The trap here is that candidates often assume a successful indexer run means data was indexed, but Azure AI Search can report success even when no documents are written to the index due to missing field mappings or skillset configuration.

How to eliminate wrong answers

Option A is wrong because an invalid connection string would cause the indexer to fail with an authentication or connection error, not run successfully with zero documents indexed. Option B is wrong because an incorrect container name would result in a 'container not found' error during the indexer run, preventing successful execution. Option D is wrong because the maxFailedItems parameter, when set to -1, means no limit on failed items (i.e., the indexer will continue despite failures), not that it skips all documents; a value of 0 would cause the indexer to stop on the first failure.

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38

MCQhard

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?

A.Use Azure AI Translator to translate all reviews to English before indexing.

B.Use a single field with the 'standard' analyzer for all languages.

C.Create separate search fields for each language, each with the corresponding language analyzer (e.g., 'en.microsoft', 'es.microsoft', 'fr.microsoft').

D.Use the 'keyword' analyzer for all fields.

AnswerC

This allows language-specific stemming and tokenization, and you can search across all fields using searchFields parameter.

Why this answer

Azure AI Search supports language-specific analyzers (e.g., 'en.microsoft', 'es.microsoft', 'fr.microsoft') that apply linguistic rules like stemming, lemmatization, and stop-word removal tailored to each language. By creating separate search fields with the corresponding analyzer, a query in English can match relevant terms across all three languages because the search engine evaluates the query against each field using its analyzer, allowing cross-language retrieval without translation.

Exam trap

The trap here is that candidates often assume translation (Option A) is the only way to achieve cross-language search, but Azure AI Search's language analyzers can directly index and query multi-lingual content without altering the original text.

How to eliminate wrong answers

Option A is wrong because translating all reviews to English before indexing loses the original language nuances and requires additional translation costs and latency; Azure AI Search can natively handle multi-lingual search with language analyzers, making translation unnecessary. Option B is wrong because the 'standard' analyzer is designed for general text and does not apply language-specific linguistic processing (e.g., stemming for Spanish or French), leading to poor recall for non-English queries. Option D is wrong because the 'keyword' analyzer treats the entire field content as a single token, which is intended for exact-match scenarios like IDs or codes, not for full-text search across multiple languages.

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39

Multi-Selectmedium

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

Select 2 answers

A.Azure AI Computer Vision

B.Azure AI Video Indexer

C.Azure AI Language Service

D.Azure AI Speech Service

E.Azure AI Translator

AnswersA, C

Computer Vision provides image analysis skills.

Why this answer

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

Exam trap

A common misconception is that any Azure AI service can be used as a built-in skill, but only Computer Vision and Language Service have pre-built skills in Azure AI Search; others require custom skills or are separate services.

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40

MCQhard

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?

A.The filter expression uses incorrect OData syntax.

B.The 'id' field is not used as the key.

C.The 'author' field is not set as filterable in the index definition.

D.The query uses a $orderby parameter that conflicts with the filter.

AnswerA

Using incorrect OData syntax (e.g., wrong operator, missing quotes) is the most likely reason the filter fails.

Why this answer

The most likely reason the filter on author is not working is an incorrect OData syntax in the filter expression. Since the exhibit shows the 'author' field is marked as filterable, the index definition itself is correct. However, if the filter uses invalid syntax (e.g., incorrect operator, missing quotes, or malformed date comparison), the filter will fail.

Option B is incorrect because the 'id' field's role as a key is unrelated to filtering on 'author'. Option C is incorrect because the 'author' field is filterable as shown in the exhibit. Option D is incorrect because the '$orderby' parameter does not conflict with filters; they can be used together.

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41

Multi-Selectmedium

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

Select 2 answers

A.Use the Push API to send data directly to the search index.

B.Use Azure Data Factory to copy data to Blob Storage, then index from Blob.

C.Use Azure AI Document Intelligence to extract data and push to index.

D.Use Azure Event Hubs to stream data into the search index.

E.Use the Azure AI Search SQL Server indexer.

AnswersA, E

The Push API allows programmatic indexing of data.

Why this answer

Azure AI Search supports indexing from SQL Database using either a SQL Server indexer or a push API. Data Factory and Event Hubs are not direct indexers for SQL.

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42

MCQhard

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?

A.Use Azure AI Document Intelligence instead of a custom skill

B.Increase the function timeout and ensure the function is in the same region as the search service

C.Set the function timeout to the maximum of 24 hours

D.Move the custom skill to Azure AI Language custom entity recognition

AnswerB

Longer timeout accommodates large documents.

Why this answer

Increasing the function timeout (up to the Azure Functions maximum of 10 minutes for the Consumption plan or 30 minutes for the Premium plan) and ensuring the function is in the same region as the search service reduces latency and network overhead, directly addressing timeout issues for large documents. Azure AI Search custom skills must complete within the function's configured timeout, and regional colocation minimizes cross-region data transfer delays.

Exam trap

The trap here is that candidates may think increasing the timeout to an arbitrarily high value (like 24 hours) is possible, but Azure Functions enforce strict maximum timeout limits depending on the hosting plan, and the correct approach is to work within those limits while optimizing performance through regional colocation.

How to eliminate wrong answers

Option A is wrong because Azure AI Document Intelligence is a pre-built skill for document analysis, not a replacement for a custom Azure Function that performs specialized logic; it cannot handle arbitrary custom processing that may cause timeouts. Option C is wrong because Azure Functions have a maximum timeout of 10 minutes (Consumption plan) or 30 minutes (Premium plan), not 24 hours; setting a 24-hour timeout is impossible and would violate platform limits. Option D is wrong because moving the custom skill to Azure AI Language custom entity recognition does not solve timeout issues—it changes the service entirely and still requires a custom component if the logic is not entity recognition, and it does not address the underlying timeout problem.

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43

MCQeasy

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?

A.Use Azure AI Document Intelligence to extract QnA pairs.

B.Create a custom question answering project and import the Word document as a source.

C.Use the prebuilt question answering API to parse the document.

D.Use conversational language understanding (CLU) to extract intents and entities.

AnswerB

Custom question answering supports importing FAQs from documents.

Why this answer

Custom question answering in Azure AI Language allows importing FAQ content from Word documents as a source directly. Option A is incorrect because Azure AI Document Intelligence is designed for extracting structured data from documents, not specifically for QnA pair import into a question answering project. Option C is incorrect because the prebuilt question answering API is for out-of-the-box QA, not for custom document import.

Option D is incorrect because conversational language understanding (CLU) is used for intent and entity extraction, not for importing FAQs.

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44

MCQmedium

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?

A.Azure AI Document Intelligence custom extraction model

B.Azure OpenAI Service with GPT-4 prompt engineering

C.Azure AI Search with built-in blob indexing

D.Azure AI Language custom named entity recognition

AnswerA

Custom extraction models are designed for high-accuracy field extraction from documents with minimal labeling.

Why this answer

Azure AI Document Intelligence custom extraction model is the correct choice because it is specifically designed to extract structured fields (like clauses, parties, and dates) from documents such as PDF contracts. It uses a prebuilt layout model combined with custom training on a small set of labeled documents, minimizing manual labeling effort while achieving high accuracy through transfer learning and table/key-value pair extraction.

Exam trap

In the Microsoft AI-102 exam, candidates often confuse Azure OpenAI's generative capabilities with Azure AI Document Intelligence's specialized extraction service. While GPT-4 is flexible, it is not deterministic and requires more labeling effort to achieve high accuracy for structured field extraction from documents, making Document Intelligence the better choice.

How to eliminate wrong answers

Option B is wrong because Azure OpenAI Service with GPT-4 prompt engineering is a generative AI approach that requires extensive prompt tuning and may hallucinate or produce inconsistent extractions, especially for structured fields in legal contracts; it is not optimized for high-accuracy, deterministic extraction from PDFs. Option C is wrong because Azure AI Search with built-in blob indexing provides full-text search and metadata extraction but does not perform custom field-level extraction of clauses, parties, or dates without additional custom skills or AI enrichment pipelines. Option D is wrong because Azure AI Language custom named entity recognition is designed for extracting entities from unstructured text (e.g., news articles, social media) and does not natively handle PDF layout, tables, or multi-page document structure, requiring significant preprocessing and labeling for contract-specific fields.

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45

MCQhard

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?

A.Use the push API to index all data from a custom application that polls all sources.

B.Create a single indexer that reads from all three sources using a data source definition.

C.Use only indexers for all sources by creating a custom indexer for the REST API.

D.Configure indexers for SQL and SharePoint, and use the push API for the REST API. Schedule indexers to run every 5 minutes.

AnswerD

Combines indexers for native sources and push API for custom data.

Why this answer

It combines the strengths of indexers (for SQL Database and SharePoint Online, which have native connectors) with the push API for custom REST APIs, which lack a built-in indexer. Scheduling the indexers to run every 5 minutes ensures that updates are reflected within the required latency window, while the push API can be triggered on demand or via a polling mechanism to meet the same 5-minute SLA.

Exam trap

The trap here is that candidates assume a single indexer can handle multiple data sources or that a custom indexer can be built for any source, when in reality each indexer is tied to one specific data source type and custom REST APIs require the push API.

How to eliminate wrong answers

Option A is wrong because using the push API exclusively requires building a custom application to poll all sources, which is unnecessary overhead for SQL and SharePoint when native indexers exist, and it does not leverage Azure AI Search's built-in change tracking and scheduling capabilities. Option B is wrong because a single indexer cannot read from multiple heterogeneous data sources; each indexer is bound to one data source definition, and you must create separate indexers for SQL, SharePoint, and REST APIs. Option C is wrong because Azure AI Search does not support creating custom indexers for REST APIs; the only way to index data from a custom REST API is via the push API, not an indexer.

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46

Multi-Selecteasy

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

Select 3 answers

A.A database to store intermediate results.

B.A Power Automate flow to orchestrate the skill.

C.A web API endpoint that accepts JSON input and returns JSON output.

D.An HTTPS endpoint for the API.

E.A JSON schema defining inputs and outputs.

AnswersC, D, E

Custom skill must be a web API.

Why this answer

A custom skill in Azure AI Search must be implemented as a web API that receives a JSON payload containing the input fields defined in the skill's context and returns a JSON response with the output fields. This API is called by the AI Search enrichment pipeline during indexing, allowing you to inject custom logic (e.g., entity extraction, classification) into the skillset execution.

Exam trap

The trap here is that candidates often think a custom skill requires an orchestration tool like Power Automate or a persistent storage layer, but Azure AI Search's enrichment pipeline handles orchestration natively and only needs a stateless HTTPS endpoint with a defined JSON schema.

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47

Multi-Selectmedium

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?

Select 2 answers

A.Entity Recognition skill

B.Image Analysis skill

C.OCR skill

D.Key Phrase Extraction skill

E.Text Translation skill

AnswersB, C

Image Analysis skill can generate captions, tags, and objects from images.

Why this answer

(Image Analysis skill) is correct because it extracts rich information from images, such as descriptions, tags, captions, and even celebrities or landmarks, using Azure Cognitive Services Computer Vision. This skill is designed to analyze the visual content of images embedded in documents during AI enrichment.

Exam trap

The trap here is that candidates often confuse the Image Analysis skill with the OCR skill, thinking only one is needed for image extraction, but the question asks for TWO skills that extract information from images—one for visual content and one for text.

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48

MCQmedium

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?

A.Use Azure AI Language key phrase extraction, and enable vector search

B.Use Azure AI Document Intelligence to extract entities, and enable semantic ranking

C.Use Azure AI Language entity extraction as a custom skill, and enable fuzzy search in the index

D.Use Azure AI Vision OCR to extract text, and enable synonyms

AnswerC

Azure AI Language entity extraction as a custom skill can extract attributes like color and brand from product descriptions. Fuzzy search in the index handles misspelled queries.

Why this answer

Using Azure AI Language entity extraction as a custom skill extracts attributes like color, size, and brand from product descriptions, and enabling fuzzy search in the index handles misspellings. Option A is incorrect because key phrase extraction identifies topics, not specific entities like color or brand, and vector search is for similarity matching, not fuzzy matching for typos. Option B is incorrect because Azure AI Document Intelligence is designed to extract text from documents (e.g., PDFs, images), not to extract named entities from text already stored in a database; semantic ranking improves relevance but does not perform entity extraction.

Option D is incorrect because Azure AI Vision OCR extracts text from images, not from product descriptions in Cosmos DB, and synonyms expand queries but do not extract entities needed for filtering.

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49

Multi-Selecthard

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?

Select 2 answers

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

B.Enable semantic search on the index.

C.Provision the Azure AI Search service in at least two regions.

D.Configure the search service with at least two replicas.

E.Enable indexing of large documents using the text split skill.

AnswersC, D

Provisioning in at least two regions provides geo-redundancy and automatic failover across regions.

Why this answer

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.

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50

Multi-Selecteasy

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

Select 2 answers

A.Custom Entity Lookup

B.PII Detection

C.Sentiment Analysis

D.Language Detection

E.Entity Recognition

AnswersB, E

Extracts PII entities.

Why this answer

The two built-in cognitive skills in Azure AI Search that extract entities from text are Entity Recognition and PII Detection. Entity Recognition extracts named entities such as people, organizations, and locations. PII Detection extracts personally identifiable information like phone numbers, email addresses, and social security numbers.

Custom Entity Lookup is a custom skill, not built-in. Sentiment Analysis determines sentiment, and Language Detection identifies language; neither extracts entities.

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51

MCQmedium

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?

A.Azure AI Computer Vision

B.Azure AI Language Service

C.Azure AI Search

D.Azure AI Document Intelligence

AnswerD

Document Intelligence can extract structured data from invoices with varied layouts using prebuilt invoice models.

Why this answer

Azure AI Document Intelligence (formerly Form Recognizer) is the correct service because it is specifically designed to extract structured data (like line-item details) from documents with varied layouts, such as invoices. Its prebuilt invoice model can parse product names, quantities, and prices from PDFs without requiring manual template configuration, and it outputs the data in a structured format that can be ingested into Azure SQL Database.

Exam trap

The trap here is that candidates often confuse Azure AI Computer Vision's OCR capability with Document Intelligence's document understanding, leading them to choose Computer Vision for any text extraction task, even when the requirement involves structured data extraction from varied-layout documents like invoices.

How to eliminate wrong answers

Option A is wrong because Azure AI Computer Vision is optimized for general image analysis (e.g., object detection, OCR for raw text) but lacks the specialized prebuilt models for extracting line-item tables from invoices with varied layouts. Option B is wrong because Azure AI Language Service focuses on text analytics (e.g., sentiment, key phrase extraction, NER) and is not designed for document structure understanding or table extraction from PDFs. Option C is wrong because Azure AI Search is a search indexing and query service, not a document extraction tool; it can index extracted data but cannot perform the initial extraction of line-item details from invoices.

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52

MCQeasy

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?

A.Use the pre-built invoice model in Azure AI Document Intelligence with a regex field extraction

B.Build a custom skill in Azure AI Search using a Python regex

C.Train a custom NER model in Azure AI Language

D.Use Azure OpenAI GPT-4 with document vision to extract the codes

AnswerB

Building a custom skill in Azure AI Search allows you to run a Python regex on the text extracted by Document Intelligence. This is a straightforward and effective way to extract codes matching the specified pattern.

Why this answer

The pre-built invoice model in Azure AI Document Intelligence does not support adding custom fields with regex patterns; custom field extraction with regex is only available in custom models. Option B is correct: you can build a custom skill in Azure AI Search using a Python regex to extract the product codes from the text output of Document Intelligence. This approach allows you to apply a regex pattern to the extracted content, providing accurate and flexible extraction without needing to train a model or use a large language model.

Option C is less suitable because training a custom NER model requires labeled data and may not guarantee exact pattern matching. Option D is overkill for a simple regex pattern.

Exam trap

Candidates often assume that the pre-built invoice model can handle custom regex fields, but in Azure AI Document Intelligence, regex field extraction is only available in custom models. The correct approach is to use a custom skill in Azure AI Search with a regex, not to rely on the pre-built model.

How to eliminate wrong answers

Option B is wrong because Azure AI Search custom skills are used to enrich search indexes, not to extract fields from documents during ingestion; they operate on already-extracted content, not on raw scanned invoices. Option C is wrong because training a custom NER model in Azure AI Language requires a labeled dataset and is overkill for a fixed regex pattern; it is designed for entity recognition in text, not for structured extraction from scanned documents. Option D is wrong because while GPT-4 with vision can extract codes, it introduces unnecessary cost, latency, and complexity compared to a native regex-based extraction in Document Intelligence, which is purpose-built for this task.

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53

MCQmedium

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?

A.The skill output path is incorrect

B.The output field mapping for organizations is missing

C.The 'Organization' category is misspelled

D.The skills must be in reverse order

AnswerB

Without mapping the skill output to the index field, the data will not appear.

Why this answer

The EntityRecognitionSkill outputs entities into a structured format (e.g., '/document/entities'). To populate the 'organizations' field in the index, you need to add a field mapping in the indexer that maps the skill output path (e.g., '/document/entities/organizations') to the index field. Without this mapping, the 'organizations' field remains empty.

Option A is incorrect because the skill output path is typically correct; the issue is the missing field mapping. Option C is incorrect because the 'Organization' category is correctly spelled in the skill configuration. Option D is incorrect because the order of skills does not affect the output; entity recognition and key phrase extraction are independent.

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54

MCQeasy

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?

A.Named Entity Recognition (NER)

B.Sentiment analysis with opinion mining

C.Entity linking

D.Key phrase extraction

AnswerB

Opinion mining provides sentiment at the entity or aspect level.

Why this answer

Sentiment analysis with opinion mining is the correct feature because it not only detects the overall sentiment of a text but also associates specific sentiments with particular entities or aspects mentioned in the text. This allows you to determine, for example, that a customer feels positively about 'product quality' but negatively about 'customer support', which is exactly what the question requires.

Exam trap

The trap here is that candidates often confuse Named Entity Recognition (NER) with the ability to extract sentiment about entities, but NER only identifies entities without any sentiment analysis, while opinion mining is the specific feature that combines entity detection with sentiment scoring.

How to eliminate wrong answers

Option A is wrong because Named Entity Recognition (NER) only identifies and categorizes entities (e.g., person, organization, location) but does not analyze sentiment or opinion towards those entities. Option C is wrong because Entity Linking disambiguates entities by linking them to a knowledge base (like Wikipedia) and does not perform sentiment analysis. Option D is wrong because Key Phrase Extraction returns a list of important phrases from the text but does not evaluate sentiment or associate opinions with specific entities.

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55

MCQhard

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?

A.Create a tagging scoring profile that boosts by the title field with a weight of 10.

B.Create a freshness scoring profile with a boosting duration of 30 days.

C.Create a distance scoring profile with a reference point parameter.

D.Create a magnitude scoring profile with a boosting function of 'linear'.

AnswerA

A tagging profile boosts documents that have matching terms in a specific field, like title.

Why this answer

A tagging scoring profile boosts documents based on matching tags from a specific field (like title), and the weight parameter controls the boost magnitude. Option B is incorrect because a freshness scoring profile boosts by recency, not by term presence in a field. Option C is incorrect because a distance scoring profile boosts based on geospatial proximity, not text fields.

Option D is incorrect because a magnitude scoring profile boosts based on numeric field values, not text fields; a linear boosting function is used for magnitude profiles, but this does not apply to boosting by title.

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56

MCQeasy

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?

A.Set the 'filterable' property to true on the category field

B.Set the 'facetable' property to true on the category field

C.Set the 'searchable' property to true on the category field

D.Set the 'sortable' property to true on the category field

AnswerA

Filterable fields allow OData filter expressions to be applied in queries.

Why this answer

Setting the 'filterable' property to true on the category field enables Azure AI Search to apply OData filter expressions (e.g., `$filter=category eq 'Networking'`) during query execution. This allows users to narrow search results by document category without requiring the field to be full-text searchable, which is essential for efficient filtering in a technical manuals index.

Exam trap

The trap here is that candidates often confuse 'facetable' with 'filterable' because both are used in search UIs for narrowing results, but faceting only provides aggregation counts for navigation, not the ability to apply server-side OData filters.

How to eliminate wrong answers

Option B is wrong because setting 'facetable' to true enables drill-down navigation (e.g., showing category counts in a UI), but it does not support direct filtering via $filter; faceting and filtering are separate capabilities. Option C is wrong because setting 'searchable' to true enables full-text search on the category field, but filtering does not require searchability—in fact, marking a field as searchable consumes additional storage and processing overhead unnecessarily. Option D is wrong because setting 'sortable' to true allows ordering results by the category field (e.g., $orderby=category), but it does not enable the $filter parameter to restrict results based on category values.

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57

MCQhard

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?

A.The maximumPageLength of 5000 is too high for sentiment analysis

B.The input source for SentimentSkill should be '/document/pages/*' but the SplitSkill output is named 'pages', so the input should be '/document/pages'

C.The context of the SentimentSkill is set to an array, which is not supported

D.The SentimentSkill uses an incorrect @odata.type version

AnswerC

Correct. Setting the context to an array (e.g., '/document/pages') without the wildcard causes the skill to run once on the entire array, leading to incorrect sentiment analysis. The context should be '/document/pages/*' for per-page processing.

Why this answer

Setting the context of the SentimentSkill to an array (e.g., '/document/pages') causes the skill to run once on the entire array of pages, rather than iterating over each page individually. This results in incorrect sentiment analysis because the skill receives a collection of strings as a single input, which is not the intended per-page processing. The correct configuration is to set the context to '/document/pages/*' so that the skill executes on each page separately.

Exam trap

Candidates often confuse the skill context with the input source path. Setting the context to an array does not automatically iterate; the context must end with '/*' to run the skill per element.

How to eliminate wrong answers

Option A is wrong because the maximumPageLength of 5000 characters is well within the supported range for sentiment analysis; Azure AI Language service handles up to 5120 characters per document, so 5000 is acceptable. Option C is wrong because the SentimentSkill context can be set to an array (e.g., '/document/pages/*') and it will run on each element; the issue is the missing wildcard, not the array context itself. Option D is wrong because the @odata.type version for SentimentSkill is standard and does not affect correctness; the problem is a data path configuration error, not an API version mismatch.

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58

MCQhard

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?

A.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.

B.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.

C.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.

D.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.

AnswerA

Best approach for structured extraction, PII redaction, and natural language query.

Why this answer

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.

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59

MCQmedium

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?

A.Run 'az search indexer run --name myindexer' to trigger a new run.

B.Run 'az search indexer show --name myindexer' and review the 'warnings' array in the output.

C.Ignore the warnings because they are not errors.

D.Run 'az search indexer reset --name myindexer' to reset the indexer.

AnswerB

The indexer show command returns detailed execution history including warnings.

Why this answer

The 'az search indexer list' command with the query you used returns a summary of indexer status and last result, but it does not include the detailed 'warnings' array. To investigate the 5 warnings, you need to use 'az search indexer show --name myindexer', which returns the full indexer execution history, including a 'warnings' array that lists each warning with its message and details. This allows you to understand the nature of each warning and take corrective action if needed.

Exam trap

The trap here is that candidates assume the 'list' command provides full details, but it only returns a filtered projection; the 'show' command is required to access the nested 'warnings' array, which is a common pattern in Azure CLI where list commands return summaries and show commands return full objects.

How to eliminate wrong answers

Option A is wrong because 'az search indexer run' triggers a new execution but does not retrieve or display existing warnings; it would only produce a new set of warnings or errors. Option C is wrong because warnings in Azure Cognitive Search indexers often indicate issues like field mapping conflicts, data truncation, or unsupported types that can degrade indexing quality or cause silent data loss, so they should not be ignored. Option D is wrong because 'az search indexer reset' resets the indexer's change tracking state, forcing a full reindex of all documents, which is an aggressive action unrelated to investigating warnings.

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60

MCQhard

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?

A.Use a different custom skill that doesn't call the external API.

B.Schedule the indexer to run every 2 hours with a batch size of 20.

C.Increase the indexer's batch size to 100.

D.Increase the indexer's maximum parallelism to 10.

AnswerB

Spreads requests over time, staying under limit.

Why this answer

Avoids the rate limit by not calling the external API, but it fails to meet the requirement to extract custom metadata using the specified custom skill. Option B schedules the indexer to run every 2 hours with a batch size of 20, which reduces the frequency of runs and limits the number of documents per batch, thereby keeping requests to the external API under 100 per minute. Option C increases the batch size to 100, which could cause each batch to make 100 requests, potentially exceeding the rate limit if multiple batches are processed concurrently.

Option D increases maximum parallelism, allowing more batches to run concurrently and increasing the request rate, which could exceed the limit.

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61

MCQmedium

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?

A.Named Entity Recognition skill

B.Language Detection skill

C.Optical Character Recognition (OCR) skill

D.Key Phrase Extraction skill

AnswerA

NER extracts entities like persons, organizations, dates.

Why this answer

The Named Entity Recognition (NER) skill in Azure AI Search is specifically designed to extract entities such as dates, organizations, and persons from text. When added to a skillset, it processes the content extracted from PDF files and outputs structured entity information that can be indexed and queried. This directly matches the requirement to extract and index named entities from legal documents.

Exam trap

The trap here is that candidates often confuse entity extraction with key phrase extraction or OCR, mistakenly thinking that extracting 'important terms' or 'text from images' is equivalent to identifying specific named entities like dates and organizations.

How to eliminate wrong answers

Option B is wrong because Language Detection skill identifies the language of text (e.g., English, French) but does not extract specific entities like dates or organizations. Option C is wrong because Optical Character Recognition (OCR) skill extracts text from images or scanned PDFs, but it does not perform entity extraction; it only converts visual text into machine-readable text. Option D is wrong because Key Phrase Extraction skill identifies important phrases or topics in text, not specific named entities such as persons, organizations, or dates.

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62

MCQmedium

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?

A.Azure AI Document Intelligence's pre-built invoice model and Azure Bot Service

B.Azure AI Document Intelligence's custom extraction model and Azure AI Language's custom text classification

C.Azure AI Search's built-in OCR skill and a custom skill using Azure Functions

D.Azure AI Language's pre-built entity extraction and Azure AI Search

AnswerB

Custom models can handle domain-specific extraction and classification.

Why this answer

It combines Azure AI Document Intelligence's custom extraction model to extract domain-specific fields like author names, publication dates, and references, with Azure AI Language's custom text classification to categorize research papers into topics. This pairing directly addresses the need for custom models tailored to the specialized domain, unlike pre-built or generic solutions.

Exam trap

A common mistake on the Azure AI-102 exam is choosing pre-built models (like the invoice model or generic entity extraction) thinking they can be adapted to domain-specific needs, but the question explicitly requires custom models. Always verify if the scenario demands custom training.

How to eliminate wrong answers

Option A is wrong because the pre-built invoice model is designed for invoice-specific fields (e.g., total amount, vendor name) and cannot be customized to extract author names, publication dates, or references from research papers, nor does it handle topic classification. Option C is wrong because Azure AI Search's built-in OCR skill only extracts raw text from images, not structured fields, and a custom Azure Functions skill would require building extraction logic from scratch, lacking the pre-built custom extraction and classification capabilities needed. Option D is wrong because Azure AI Language's pre-built entity extraction recognizes generic entities (e.g., person names, dates) but cannot be trained on domain-specific categories like research paper topics, and Azure AI Search alone does not provide custom classification or extraction models.

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63

MCQhard

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?

A.Custom Entity Lookup skill and Sentiment skill

B.PII detection skill

C.Text Translation skill and Entity Recognition skill

D.Entity Recognition skill and Key Phrase Extraction skill

AnswerB

PII detection skill can identify and redact sensitive information like names, dates, and SSNs.

Why this answer

The PII detection skill in Azure AI Search uses Azure AI Services to identify and redact personally identifiable information from text, which is exactly what is needed to de-identify sensitive patient data before indexing. The other options are incorrect: A) Custom Entity Lookup skill requires a predefined list and does not perform redaction, and Sentiment skill is irrelevant; C) Text Translation skill translates text, and Entity Recognition skill identifies entities but does not redact them; D) Entity Recognition skill identifies entities but does not redact, and Key Phrase Extraction skill extracts key phrases, neither of which de-identify data.

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64

Multi-Selecthard

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

Select 3 answers

A.The API endpoint must be reachable from the search service

B.The skill can only accept one input and produce one output

C.The skill must be written in Python

D.The skill must handle payloads up to 16 MB

E.The skill must complete within 230 seconds

AnswersA, D, E

The search service must be able to call the API over the network.

Why this answer

Correct options are A, D, and E. Custom skills must have an API endpoint reachable from the search service (public or via private endpoint) [A]. They must handle payloads up to 16 MB due to data size limits [D].

They must complete within 230 seconds (the default timeout) [E]. Option B is incorrect because custom skills can accept multiple inputs and produce multiple outputs. Option C is incorrect because custom skills can be written in any language that supports JSON.

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65

MCQmedium

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?

A.The first document has a higher value in a scoring profile field

B.The first document is more similar to the query in vector space

C.The first document was boosted by a semantic ranking function

D.The first document has a higher term frequency and better term proximity for the query terms

AnswerD

Default scoring favors higher term frequency and proximity.

Why this answer

The default scoring algorithm in Azure AI Search uses term frequency (TF) and term proximity. The first document likely has a higher term frequency for 'brown' and 'fox', and the terms are closer together (e.g., appearing as a phrase), resulting in a higher score. Option D correctly identifies this.

Option A is incorrect because no scoring profile is mentioned. Option B is incorrect because vector search is not used. Option C is incorrect because no semantic ranking is configured.

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66

MCQeasy

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?

A.Azure Bot Service

B.Azure AI Document Intelligence

C.Azure AI Search with built-in skills

D.Azure AI Foundry model catalog

AnswerB

It extracts structured fields from documents.

Why this answer

Azure AI Document Intelligence (formerly Form Recognizer) is specifically designed to extract structured data like invoice numbers, dates, and total amounts from scanned documents. It can be scheduled to run automatically via APIs or pipelines. Azure Bot Service is for conversational AI, not document extraction.

Azure AI Search with built-in skills can index and search documents but is not optimized for extracting specific fields from invoices. Azure AI Foundry model catalog is for discovering and deploying AI models, not for direct document extraction.

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67

MCQmedium

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?

A.Use the sentiment analysis capability in Azure AI Language

B.Train custom models in Azure AI Language for key phrase extraction and sentiment analysis

C.Use Azure AI Language's pre-built key phrase extraction API

D.Use Azure AI Document Intelligence with the pre-built read model

AnswerB

Custom NER and custom text classification in Azure AI Language are not pre-built; you define the entities and labels, making them a valid solution for key phrase extraction and sentiment analysis.

Why this answer

Azure AI Language supports training custom models for named entity recognition (NER) to extract key phrases and custom text classification to predict sentiment labels. You can first extract text from PDFs using Azure AI Document Intelligence or other OCR services, then pass the text to the custom Language models. This approach meets the requirement without using any pre-built Azure AI Language models.

Options A and C rely on pre-built APIs and are disallowed. Option D only provides OCR and cannot perform semantic analysis.

Exam trap

A common pitfall is to assume that Azure AI Document Intelligence custom models can handle semantic tasks like key phrase extraction or sentiment analysis. Document Intelligence is designed for structured field extraction (e.g., forms), not natural language understanding. When pre-built Language models are prohibited, custom models in Azure AI Language are the correct choice.

How to eliminate wrong answers

Option A is wrong because it uses the pre-built sentiment analysis capability in Azure AI Language, which is explicitly prohibited by the requirement. Option C is wrong because it uses Azure AI Language's pre-built key phrase extraction API, which is also prohibited. Option D is wrong because Azure AI Document Intelligence's pre-built read model only extracts text and layout information from documents, not key phrases or sentiment scores; it does not perform semantic analysis like sentiment detection.

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68

MCQeasy

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?

A.Set the 'query' field to a blob prefix that corresponds to the folder containing PDF files.

B.Change the container name to 'pdfs' and move all PDFs there.

C.Add a 'fileExtension' property to the container object.

D.Change the connection string to use a different storage account that contains only PDFs.

AnswerA

Filters to that folder.

Why this answer

Azure AI Search data sources support a 'query' property that accepts a blob prefix or virtual directory path. By setting 'query' to the folder path containing PDFs (e.g., 'documents/pdfs/'), the indexer will only process blobs under that prefix, effectively filtering to PDF files without moving or renaming containers.

Exam trap

The trap here is that candidates often assume filtering must be done by file extension or container name, but Azure AI Search's blob indexer supports prefix-based filtering via the 'query' field, which is the simplest and most performant method for selecting blobs from a specific folder or path.

How to eliminate wrong answers

Option B is wrong because changing the container name to 'pdfs' and moving files is an unnecessary manual workaround; Azure AI Search can filter blobs natively using the 'query' field without restructuring storage. Option C is wrong because the container object in Azure Blob Storage does not support a 'fileExtension' property; file extension filtering is done via the indexer's 'indexedFileNameExtensions' configuration or a custom skill, not on the data source definition. Option D is wrong because using a different storage account is an overengineered solution that adds complexity and cost; the existing account can be filtered with the 'query' prefix to achieve the same result.

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69

MCQhard

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?

A.Retrain the custom extraction model with scanned document images.

B.Delete and recreate the index with a different field mapping.

C.Modify the Document Intelligence skill configuration to enable OCR processing.

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

AnswerC

Document Intelligence can perform OCR on images; enabling it in the skill allows processing of scanned PDFs.

Why this answer

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.

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70

MCQmedium

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?

A.Configure the indexer to process documents in smaller batches.

B.Replace the custom skill with a Document Intelligence custom extraction model.

C.Split the skillset into multiple skillsets and run them sequentially.

D.Change the Azure Function to a Premium plan and increase the function timeout.

AnswerD

Premium plan allows longer timeouts, giving the script more time to execute.

Why this answer

The custom skill fails with time-out errors because the Azure Function's default timeout of 230 seconds is too short for processing large documents. Option D is correct because upgrading to a Premium plan allows you to increase the function timeout (up to 30 minutes with Premium plan, or unlimited with Dedicated plan). Option A is wrong because the indexer's batch size controls how many documents are processed simultaneously, but the timeout is per document (or per skill execution), so smaller batches won't solve the per-document timeout issue.

Option B is wrong because Document Intelligence custom extraction models are pre-built for common forms and are not suitable for custom Python extraction logic. Option C is wrong because splitting the skillset does not change the execution time of the individual skill; the same timeout per invocation would still apply.

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71

MCQmedium

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?

A.Prebuilt Named Entity Recognition (NER)

B.Custom Named Entity Recognition (NER)

C.PII detection

D.Entity linking

AnswerB

Custom NER allows you to train a model on your specific domain vocabulary.

Why this answer

Custom Named Entity Recognition allows you to train a model to recognize custom entities like medical terms. Option A is wrong because prebuilt NER only recognizes general entities like person, location, etc. Option C is wrong because PII detection is for personal information.

Option D is wrong because entity linking links to external knowledge bases.

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72

MCQmedium

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?

A.Azure AI Language and Azure AI Search

B.Azure AI Language and a custom skill in Azure Functions

C.Azure AI Translator and Azure AI Search

D.Azure AI Document Intelligence and Azure AI Search

AnswerA

Language provides the required skills; Search indexes the enriched content.

Why this answer

The solution requires key phrase extraction, entity recognition, and sentiment analysis, which are all capabilities of Azure AI Language. Azure AI Search is the other required service because it provides the search index and the enrichment pipeline (skillset) that invokes the AI Language skills to process documents and store the enriched content for full-text search.

Exam trap

The trap here is that candidates may confuse Azure AI Document Intelligence or Azure AI Translator as the source for text analytics, when in fact Azure AI Language is the specific service that provides key phrase, entity, and sentiment extraction as built-in cognitive skills in the search enrichment pipeline.

How to eliminate wrong answers

Option B is wrong because while Azure AI Language is needed, a custom skill in Azure Functions is unnecessary for standard key phrase, entity, and sentiment extraction—these are built-in cognitive skills in Azure AI Search. Option C is wrong because Azure AI Translator handles language translation, not key phrase extraction, entity recognition, or sentiment analysis, which are the required enrichments. Option D is wrong because Azure AI Document Intelligence (formerly Form Recognizer) extracts text and layout from documents but does not perform key phrase, entity, or sentiment analysis; those are AI Language capabilities.

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73

MCQmedium

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?

A.Azure AI Search with integrated vectorization and semantic search

B.Azure OpenAI Service with GPT-4o and Azure AI Search as a data source

C.Azure AI Language custom question answering with the documents as sources

D.Azure AI Document Intelligence to extract text and then use Azure AI Search

AnswerC

Provides direct answers from documents.

Why this answer

Azure AI Language custom question answering is specifically designed to ingest documents (like PDFs) and provide a natural language Q&A interface over them. It uses a built-in extractive reader to find answer spans directly from the source text, making it the most straightforward solution for answering questions from a static knowledge base of technical manuals without requiring additional search or vectorization infrastructure.

Exam trap

The trap here is that candidates often confuse retrieval (search) with extraction (question answering), assuming that any solution involving Azure AI Search or GPT-4o is automatically the best for Q&A, when in fact custom question answering is the purpose-built service for direct answer extraction from documents.

How to eliminate wrong answers

Option A is wrong because Azure AI Search with integrated vectorization and semantic search is a retrieval system that returns relevant document chunks or passages, not a direct question-answering service that extracts precise answer spans from the text. Option B is wrong because Azure OpenAI Service with GPT-4o and Azure AI Search as a data source uses a RAG (Retrieval-Augmented Generation) pattern that requires custom orchestration and prompt engineering to generate answers, whereas the question asks for a built-in solution that directly answers from the manuals. Option D is wrong because Azure AI Document Intelligence only extracts text from PDFs (OCR/layout analysis) and does not provide any natural language question-answering capability; pairing it with Azure AI Search still only gives retrieval, not answer extraction.

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74

MCQeasy

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?

A.Decrease the batch size to 5

B.Increase batch size to 20

C.Increase maxFailedItems to a higher value, such as 100

D.Remove the schedule to run the indexer on demand

AnswerC

Increasing maxFailedItems allows more failures before stopping.

Why this answer

The indexer is failing after processing 6 documents because the `maxFailedItems` threshold has been reached. By increasing `maxFailedItems` to a higher value like 100, the indexer will continue processing even if more documents fail, as long as the total number of failed items stays below the new threshold.

Exam trap

The trap here is that candidates confuse batch size with failure tolerance, thinking that reducing batch size will prevent the indexer from stopping, when in fact the `maxFailedItems` parameter is the direct control for how many document failures are tolerated before the indexer halts.

How to eliminate wrong answers

Option A is wrong because decreasing the batch size to 5 would reduce the number of documents processed per batch, but it does not affect the `maxFailedItems` threshold that causes the indexer to stop after 6 failures. Option B is wrong because increasing the batch size to 20 would process more documents per batch, but it does not change the `maxFailedItems` limit; the indexer would still stop after 10 failures (default). Option D is wrong because removing the schedule to run the indexer on demand does not alter the failure handling behavior; the indexer would still stop after exceeding `maxFailedItems` regardless of how it is triggered.

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75

MCQmedium

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.Azure AI Document Intelligence

B.Azure AI Vision

C.Azure AI Language

D.Azure AI Search

AnswerA

Azure AI Document Intelligence (formerly Form Recognizer) is designed to extract data from documents, including scanned PDFs.

Why this answer

Azure AI Document Intelligence (formerly Form Recognizer) is the appropriate service for extracting structured data like parties, effective dates, and termination conditions from scanned contract PDFs. It offers prebuilt models for contracts and custom extraction capabilities. Azure AI Language is for text analytics and NLP but lacks the OCR and layout understanding needed for scanned documents.

Azure AI Vision provides OCR but not structured extraction. Azure AI Search is for indexing and searching, not extraction. Therefore, Option A is correct.

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