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CCNA Implement knowledge mining and information extraction solutions Questions

75 of 153 questions · Page 2/3 · Implement knowledge mining and information extraction solutions · Answers revealed

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76
MCQhard

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

A.The entity recognition skill requires a language code that is missing
B.The split skill is not producing pages because the content is too short
C.The entity recognition skill is not registered in the skillset
D.The input source path in the entity recognition skill should be relative to the context, not absolute
AnswerD

The absolute path '/document/pages/*' conflicts with the context; relative path should be used.

Why this answer

The entity recognition skill has its context set to '/document/pages/*', which means it iterates over each page in the split output. However, its input source is '/document/pages/*' (absolute path), which points to the entire page array element rather than the text content within each page. The input source should be relative to the context, e.g., 'text' if the page had a property, or simply the context itself if the page is a string.

Using an absolute path that matches the context can cause the skill to fail because it expects a scalar input, but the context iteration provides the array element. Therefore, the correct fix is to make the input source relative, such as using 'text' or '/document/pages/*/text' appropriately. Option D correctly identifies this issue.

Exam trap

In Azure AI Search skillsets, input source paths must be relative to the skill's context when the context iterates over an array, unless the absolute path points to a property of the array element. Using an absolute path that equals the context path can cause the skill to not execute.

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77
Multi-Selecthard

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

Select 2 answers
A.Configure the indexer to run in 'once' mode.
B.Enable blob metadata extraction in the indexer.
C.Add a custom skill that outputs a hash of the document content.
D.Define a projection in the skillset to store enriched data.
E.Set the 'cacheKey' property in the skillset to a unique document identifier.
AnswersD, E

Projection stores intermediate state.

Why this answer

Defining a projection in the skillset is required to store enriched data in a knowledge store, which is a prerequisite for incremental enrichment. Incremental enrichment relies on caching enriched content so that only changed documents are reprocessed, and projections define how that enriched data is stored in Azure Storage for later reuse.

Exam trap

The trap here is that candidates often confuse enabling blob metadata extraction (Option B) with the caching mechanism required for incremental enrichment, but metadata extraction alone does not create or manage the enrichment cache.

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78
MCQmedium

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

A.Synonyms
B.Scoring profiles
C.Semantic ranking
D.Filters
AnswerC

Semantic ranking uses deep learning to re-rank results for better relevance.

Why this answer

Semantic ranking is the correct feature because it uses deep neural networks to re-rank search results based on semantic relevance to the query, ensuring only the most contextually appropriate documents are returned. This directly minimizes token usage by reducing the number of irrelevant documents passed to the copilot, which is critical for cost and performance in a ChatGPT-like system.

Exam trap

The trap here is that candidates often confuse semantic ranking with scoring profiles or filters, assuming any ranking or filtering mechanism can achieve semantic relevance, but only semantic ranking uses deep learning to understand query intent beyond keyword matching.

How to eliminate wrong answers

Option A is wrong because synonyms expand queries to include alternate terms (e.g., 'vacation' vs. 'leave'), which can increase the number of retrieved documents and token usage, not minimize it. Option B is wrong because scoring profiles boost results based on static field values or freshness (e.g., 'last modified date'), but they do not re-rank for semantic relevance to the query, so they may still return many irrelevant documents. Option D is wrong because filters narrow results by exact field values (e.g., 'department = HR'), but they cannot assess semantic similarity between the query and document content, so they may miss relevant documents or include irrelevant ones that match the filter criteria.

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79
MCQmedium

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

A.Use Azure AI Document Intelligence to extract text from EPUB files directly.
B.Develop a custom skill that converts EPUB files to plain text and add it to the skillset.
C.Modify the Document Intelligence skill to accept EPUB files.
D.Register a new data source type for EPUB in Azure AI Search.
AnswerB

A custom skill can convert unsupported formats into text that the pipeline can process.

Why this answer

Azure AI Search indexer does not natively support EPUB files. The Document Intelligence skill can only process formats it supports (PDF, Word, etc.). Therefore, a custom skill is needed to convert EPUB files to plain text or a supported format before they can be processed by the enrichment pipeline.

Option A is wrong because Azure AI Document Intelligence does not support EPUB directly. Option C is wrong because the Document Intelligence skill cannot be modified to accept new formats. Option D is wrong because registering a new data source type does not enable processing of unsupported file formats.

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80
MCQeasy

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

A.Custom model
B.Read API
C.Layout model
D.Prebuilt invoice model
AnswerB

Read API supports OCR in over 100 languages.

Why this answer

The Read API is the correct choice because it is specifically designed for extracting printed and handwritten text from scanned documents, including support for multiple languages like French. It performs optical character recognition (OCR) to digitize text without requiring any additional training or customization, making it ideal for general text extraction from scanned documents.

Exam trap

The trap here is that candidates often confuse the Read API with the Layout model, assuming that structural analysis is required for text extraction, but the Read API is the dedicated OCR solution for plain text extraction from scanned documents.

How to eliminate wrong answers

Option A is wrong because a custom model requires labeled training data and is used for extracting specific fields from structured documents, not for general text extraction from arbitrary scanned documents. Option C is wrong because the Layout model extracts text along with structural information like tables and selection marks, which is more than what is needed for simple text extraction from scanned documents. Option D is wrong because the prebuilt invoice model is specialized for extracting fields from invoices (e.g., totals, dates) and is not designed for general text extraction from arbitrary scanned documents in French.

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81
MCQhard

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

A.The indexer is configured with 'allowSkillsetToExecuteIfError' set to false.
B.The indexer is configured with 'allowSkillsetToExecuteIfError' set to true.
C.The Azure Function returns HTTP 200 with an error message in the body.
D.The custom skill's output field mapping is incorrect.
AnswerB

When true, skill errors are treated as warnings and the indexer continues processing other documents.

Why this answer

By default, indexers continue on error. To stop on skill errors, set 'allowSkillsetToExecuteIfError' to false. The indexer treats skill errors as warnings by default.

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82
MCQeasy

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

A.Key Phrase Extraction skill
B.Sentiment skill
C.Language Detection skill
D.Entity Recognition skill
AnswerA

This skill extracts key phrases from text.

Why this answer

The Key Phrase Extraction skill is the correct choice because it is specifically designed to identify and extract important phrases from text content, which aligns with the requirement to automatically extract key phrases during indexing. This skill is part of Azure AI Search's cognitive skillset and operates on English text to produce a list of key phrases per document.

Exam trap

The trap here is that candidates may confuse Entity Recognition (which extracts specific named entities) with Key Phrase Extraction (which extracts general important phrases), leading them to select the wrong skill for the requirement.

How to eliminate wrong answers

Option B is wrong because the Sentiment skill is used to determine the emotional tone (positive, negative, neutral) of text, not to extract key phrases. Option C is wrong because the Language Detection skill identifies the language of the text (e.g., English, Spanish), but does not extract key phrases from the content. Option D is wrong because the Entity Recognition skill extracts named entities such as people, organizations, and locations, not general key phrases.

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83
MCQmedium

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

A.Language Service
B.Speech Service
C.Computer Vision
D.Azure AI Document Intelligence (formerly Form Recognizer)
AnswerD

Azure AI Document Intelligence (formerly Form Recognizer) is the correct choice because it is built for extracting information from forms and documents, including printed and handwritten text, and provides prebuilt models for invoices.

Why this answer

Azure AI Document Intelligence (formerly Form Recognizer) is the correct service because it is specifically designed to extract text, key-value pairs, and tables from documents like scanned invoices, and it supports both printed and handwritten text. Option A (Language Service) is used for text analytics and NLP, not structured document extraction. Option B (Speech Service) handles audio transcription and speech, not documents.

Option C (Computer Vision) can extract text via OCR but is less specialized than Document Intelligence for invoice-specific extraction.

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84
MCQeasy

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

A.Azure AI Search and Azure OpenAI
B.Azure AI Speech and Azure OpenAI
C.Azure AI Language and Azure AI Document Intelligence
D.Azure AI Document Intelligence and Azure Bot Service
AnswerA

Search indexes the manuals; Azure OpenAI provides conversational Q&A (RAG pattern).

Why this answer

Azure AI Search provides the indexing and retrieval capabilities needed to search through the PDF content, while Azure OpenAI (specifically GPT models) can generate natural language answers based on the retrieved passages. This combination enables a RAG (Retrieval-Augmented Generation) pattern where the search engine finds relevant text chunks from the manuals and the language model formulates a coherent answer.

Exam trap

The trap here is that candidates often confuse Azure AI Language (which handles text analytics) with the search and generative AI capabilities needed for a question-answering system, or they incorrectly assume that Azure Bot Service alone can handle document-based Q&A without a search backend.

How to eliminate wrong answers

Option B is wrong because Azure AI Speech is used for speech-to-text and text-to-speech, not for searching or understanding document content; it does not index PDFs or retrieve relevant passages. Option C is wrong because Azure AI Language provides pre-built NLP capabilities like entity recognition or sentiment analysis, but it is not designed for full-text search over a large repository of PDFs; Azure AI Document Intelligence is for extracting text from documents, not for answering questions. Option D is wrong because Azure AI Document Intelligence extracts text from PDFs but does not index or search that text, and Azure Bot Service is a framework for building chatbots but lacks the search and generative AI components needed to answer questions from a document repository.

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85
MCQhard

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

A.Upload documents directly to Azure AI Search
B.Use Azure AI Language custom entity extraction
C.Use Azure AI Document Intelligence layout model as a custom skill
D.Use Azure AI Vision OCR skill in the skillset
AnswerC

The layout model extracts text, tables, and structure preserving relationships.

Why this answer

Azure AI Document Intelligence Layout model extracts text, tables, and their structure from documents, making it suitable for preserving table structure and building a searchable knowledge base. It can be used as a custom skill in an Azure AI Search enrichment pipeline. Option A is incorrect because uploading documents directly to Azure AI Search only indexes raw text without extraction or table structure preservation.

Option B is incorrect because Custom Entity Extraction in Azure AI Language focuses on identifying entities, not extracting tables or preserving structure. Option D is incorrect because Azure AI Vision OCR extracts text but not table structure, and is not designed for document layout analysis.

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86
MCQhard

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

A.Use separate skillsets for each tenant
B.Create a separate search service for each tenant
C.Use a single index with a tenant ID field and filter queries by that field
D.Use separate data sources within the same index
AnswerC

Index-level security with filters is the recommended approach.

Why this answer

Azure AI Search supports multi-tenancy within a single service by using a shared index with a tenant ID field. Each document is tagged with a tenant identifier, and queries are scoped using OData `$filter` expressions (e.g., `$filter=tenantId eq 'tenant123'`). This ensures data isolation while keeping costs low and management simple, as only one search service and one index are needed.

Exam trap

The trap here is that candidates confuse data sources (which are just ingestion pipelines) with data partitioning, leading them to think separate data sources or skillsets provide isolation, when in fact only query-time filtering or separate indexes enforce tenant boundaries.

How to eliminate wrong answers

Option A is wrong because skillsets define enrichment pipelines (e.g., OCR, entity extraction) and are not used for data isolation; they apply to all documents in an index regardless of tenant. Option B is wrong because creating a separate search service for each tenant is unnecessarily expensive and complex, violating the requirement to use a single Azure AI Search service. Option D is wrong because separate data sources within the same index still store all documents together; data sources only define where data is pulled from, not how it is partitioned or secured at query time.

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87
MCQmedium

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

A.The timeout of 30 seconds is too short for the function to respond
B.The batch size of 5 is too large, causing the function to receive too many documents at once
C.The context '/document' is incorrect, causing all documents to be processed as one
D.The degreeOfParallelism of 3 causes too many concurrent requests, exceeding the function's capacity
AnswerD

With batchSize 5 and degreeOfParallelism 3, up to 15 documents are sent concurrently.

Why this answer

The `degreeOfParallelism` of 3 causes the AI Search enrichment pipeline to invoke the Azure Function with up to 3 concurrent batches, each of size 5, resulting in up to 15 documents per second. Since the function can only handle 10 documents per second, this exceeds its capacity and triggers HTTP 429 (Too Many Requests) errors.

Exam trap

The trap here is that candidates often focus on the batch size as the sole cause of rate limiting, overlooking that `degreeOfParallelism` multiplies the effective request rate, which is the actual trigger for 429 errors.

How to eliminate wrong answers

Option A is wrong because a 30-second timeout is typically sufficient for an Azure Function processing documents; 429 errors indicate rate limiting, not timeout. Option B is wrong because a batch size of 5 means the function receives 5 documents per invocation, which is within the 10-document-per-second capacity if only one batch is processed at a time. Option C is wrong because the context '/document' is the standard path for per-document processing in AI Search skills; using it does not cause all documents to be processed as one, but rather each document is processed individually.

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88
MCQmedium

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

A.Use Azure AI Search with a skillset that includes OCR skill, Text Translation skill to translate, Entity Recognition skill for drugs, and Key Phrase Extraction. Index the results.
B.Use Azure AI Search with a blob indexer that includes a skillset with Document Layout skill to extract text, Key Phrase Extraction skill to extract key findings, and map built-in metadata for title, authors, date. Use a custom index to store the extracted fields.
C.Use Azure AI Search with a blob indexer and a skillset that includes Document Layout skill, Entity Recognition skill to extract drug names, Key Phrase Extraction skill, and custom skill to extract title/authors/date from the first page. Create an index with fields for each required element.
D.Use Azure AI Search with a skillset that includes OCR skill, Entity Recognition skill, Sentiment skill, and Key Phrase Extraction. Use a knowledge store to project the enriched data.
AnswerC

Covers all requirements with appropriate skills.

Why this answer

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

Exam trap

The trap here is that candidates often assume built-in blob metadata (like 'metadata_storage_name') can extract article-specific fields like authors or publication date, but those are only file-level properties and require a custom skill to parse from the document content.

How to eliminate wrong answers

Option A is wrong because it uses OCR skill (unnecessary for digital PDFs with selectable text) and Text Translation skill (not needed since articles are in English), and it lacks a custom skill to extract title, authors, and date from the first page. Option B is wrong because it relies on built-in metadata for title, authors, and date, but blob indexer metadata only captures file-level properties (e.g., filename, size) and cannot extract article-specific fields like authors or publication date from within the document. Option D is wrong because it includes Sentiment skill (irrelevant for extracting structured information) and uses a knowledge store instead of directly indexing the enriched data for search, and it omits the Document Layout skill and custom skill needed for title/authors/date extraction.

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89
Multi-Selectmedium

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

Select 2 answers
A.Azure AI Personalizer
B.Azure AI Translator
C.Azure AI Custom Vision
D.Azure AI Language
E.Azure AI Search with cognitive skills
AnswersD, E

Azure AI Language includes sentiment analysis and key phrase extraction for topic identification.

Why this answer

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

Exam trap

The trap here is that candidates often confuse Azure AI Language with Azure AI Translator or Azure AI Personalizer, mistakenly thinking translation or personalization services can perform sentiment analysis and topic categorization, when in fact only Azure AI Language provides those text analytics capabilities.

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90
MCQhard

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

A.The field mapping source field 'metadata_storage_path' is incorrect
B.The schedule start time is in the past, causing the indexer to be disabled
C.The data source 'demo-datasource' does not exist
D.The skillset resource was not deployed before the indexer
AnswerD

The indexer depends on the skillset, which must exist. The template does not include the skillset resource.

Why this answer

The ARM template deploys resources in parallel by default, so the indexer can be created before the skillset. Since the indexer references 'demo-skillset' in its configuration, the deployment fails because the skillset resource does not exist at the time the indexer is created. To fix this, you must add a 'dependsOn' property in the indexer resource definition to ensure the skillset is deployed first.

Exam trap

The trap here is that candidates assume ARM templates deploy resources in the order they appear in the template, but Azure deploys them in parallel by default, so missing 'dependsOn' causes dependency errors like the one shown.

How to eliminate wrong answers

Option A is wrong because 'metadata_storage_path' is a valid system-generated field in Azure Blob Storage data sources, and an incorrect field mapping would cause a field mapping error, not a 'skillset does not exist' error. Option B is wrong because a schedule start time in the past does not disable the indexer; it simply means the first run is scheduled immediately, and the indexer remains active. Option C is wrong because the error message explicitly states the skillset does not exist, not the data source; if the data source were missing, the error would reference 'demo-datasource' instead.

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91
MCQeasy

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

A.Create an index with a field for each metadata property.
B.Create a skillset to extract text from PDFs.
C.Create a data source that connects to Azure Blob Storage.
D.Create an indexer that runs daily.
AnswerC

A data source is required to specify where the data is located.

Why this answer

The correct first step is to create a data source that connects to Azure Blob Storage (option C). In Azure Cognitive Search, the indexing pipeline requires a data source definition to specify the location and type of data. After creating the data source, you can then create an index, and finally an indexer to automate the process.

Creating a skillset (option B) or an indexer running daily (option D) would come later. Option A is incorrect because the index fields are defined when you create the index, which is not the first step.

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92
MCQhard

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

A.Create a custom skill in the skillset that calls the custom entity extraction endpoint via HTTP.
B.Deploy the custom model to Azure AI Document Intelligence and use a Document Intelligence skill.
C.Add the custom entity extraction as a field mapping in the indexer.
D.Use the built-in Entity Recognition skill and configure it to use your custom model endpoint.
AnswerA

Custom skills can call external APIs, including custom model endpoints.

Why this answer

To integrate a custom AI model from Azure AI Language into an Azure Cognitive Search enrichment pipeline, you need to create a custom skill in the skillset that calls the custom entity extraction endpoint via HTTP. The built-in Entity Recognition skill only supports prebuilt models and cannot be configured to use a custom model endpoint. Deploying the model to Azure AI Document Intelligence and using a Document Intelligence skill is not appropriate because the model is already deployed in Azure AI Language as a custom entity extraction model.

Field mappings in the indexer are for direct field-to-field mappings from the data source to the index, not for calling external AI services. Therefore, Option A is the correct approach.

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93
MCQmedium

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

A.Use the entity recognition skill in Azure AI Search and map to a target field
B.Use Azure AI Document Intelligence with a custom model to identify PII fields
C.Use the built-in PII detection skill in Azure AI Search with redaction mode enabled
D.Use Azure AI Language's key phrase extraction to find PII
AnswerC

The PII detection skill can redact detected entities.

Why this answer

Azure AI Search provides a built-in PII detection skill that can automatically identify and redact PII entities (such as names, phone numbers, and email addresses) from text documents during the indexing pipeline. The skill supports a 'redactionMode' configuration that replaces detected PII with a placeholder (e.g., '***'), meeting the requirement to redact PII before indexing.

Exam trap

The trap here is that candidates confuse the general entity recognition skill (which identifies entities but does not redact) with the PII-specific skill (which is designed for redaction), leading them to choose Option A instead of C.

How to eliminate wrong answers

Option A is wrong because the entity recognition skill in Azure AI Search identifies entities (e.g., people, organizations) but does not natively support PII-specific redaction; it outputs entity categories and values, not redacted text. Option B is wrong because Azure AI Document Intelligence with a custom model is designed for extracting structured data from forms and documents, not for general-purpose PII detection and redaction from arbitrary text. Option D is wrong because Azure AI Language's key phrase extraction identifies key topics and phrases, not PII entities, and lacks any redaction capability.

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94
Multi-Selecthard

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

Select 2 answers
A.Train a custom neural model to recognize handwritten signatures.
B.Enable table extraction in the custom model.
C.Enable OCR to read scanned text.
D.Use form recognition to capture key-value pairs.
E.Use the prebuilt-layout model for all extraction.
AnswersA, B

Neural models can learn to extract signatures.

Why this answer

Options A and B are correct because a custom neural model can be trained to recognize handwritten signatures (A), and enabling table extraction in the custom model extracts tables from contracts (B). Option C is incorrect because OCR is already enabled by default in Document Intelligence and does not need to be separately enabled. Option D is wrong because form recognition for key-value pairs is not required for this scenario, as the focus is on tables and signatures.

Option E is wrong because the prebuilt-layout model does not support custom training for signature recognition and may not handle handwritten elements as accurately as a custom model.

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95
MCQmedium

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

A.Ensure the index includes a semantic configuration with title and content fields
B.Increase the number of partitions to handle more data
C.Configure a scoring profile with boosting based on metadata
D.Increase the number of replicas to improve query performance
AnswerA

Semantic configuration is required for semantic ranking to work.

Why this answer

Semantic search in Azure AI Search requires a semantic configuration that explicitly maps the title and content fields to be used for semantic ranking. Without this configuration, the search engine cannot apply the deep neural network models that understand context and intent, so results remain based on keyword matching even when the semantic search feature is enabled.

Exam trap

The trap here is that candidates assume enabling the semantic search feature alone is sufficient, but Azure AI Search requires an explicit semantic configuration to map the fields that the semantic model will use for reranking.

How to eliminate wrong answers

Option B is wrong because increasing the number of partitions scales the index for larger data volumes but does not improve relevance or semantic understanding of search results. Option C is wrong because scoring profiles with boosting based on metadata can adjust ranking weights but do not provide the contextual, language-understanding capabilities that semantic search offers. Option D is wrong because increasing replicas improves query throughput and availability, not the relevance or contextual quality of search results.

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96
MCQhard

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

A.4
B.2
C.3
D.1
AnswerC

Three pages result from the split, each triggering an entity recognition execution.

Why this answer

The Azure AI Search entity recognition skill has a maximum text length per execution of 5,000 characters. A document with 12,000 characters is split into chunks of up to 5,000 characters, resulting in three chunks (5,000 + 5,000 + 2,000). Each chunk triggers one skill execution, so three executions occur.

Exam trap

The Azure AI Search entity recognition skill has a maximum text length per execution of 5,000 characters. Candidates might incorrectly divide 12,000 by 5,000 and round down to 2, or assume a single execution can handle the entire document, ignoring the chunking behavior.

How to eliminate wrong answers

Option A is wrong because 4 executions would require more than 15,000 characters (4 × 5,000), but the document has only 12,000 characters. Option B is wrong because 2 executions would cover only 10,000 characters (2 × 5,000), leaving 2,000 characters unprocessed. Option D is wrong because 1 execution can handle only up to 5,000 characters, but the document has 12,000 characters, so it must be split into multiple chunks.

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97
MCQmedium

You have configured an Azure AI Search indexer with a Cosmos DB data source as shown in the exhibit. The indexer runs successfully, but you notice that the index is missing some documents that were recently added to Cosmos DB. What is the most likely cause?

A.The indexer is not configured to track changes using _ts.
B.The container name is misspelled.
C.The high water mark is not being updated correctly, causing some documents to be skipped.
D.The query does not select all fields required by the index.
AnswerC

If the high water mark is not updated, documents with _ts <= high water mark are skipped.

Why this answer

The most likely cause is that the high water mark (the _ts value) is not being updated correctly, causing the indexer to skip documents that were recently added. Azure AI Search uses a high water mark strategy to track changes from Cosmos DB, and if the _ts value is not properly recorded or the indexer's change tracking logic fails, new documents may be missed even though the indexer runs successfully.

Exam trap

The trap here is that candidates often assume the indexer must be failing entirely or that a configuration error is obvious, but the question describes a successful run with missing documents, which points to a subtle change tracking issue rather than a connectivity or schema mismatch.

How to eliminate wrong answers

Option A is wrong because the indexer is already configured to track changes using _ts (as shown in the exhibit), so this is not the cause. Option B is wrong because if the container name were misspelled, the indexer would fail to connect and would not run successfully. Option D is wrong because the query not selecting all fields required by the index would cause indexing errors or missing fields, not the omission of entire documents that were recently added.

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98
MCQhard

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

A.Add a separate field for each word in the document
B.Change the analyzer to a custom analyzer that splits on spaces only
C.Enable semantic search on the index
D.Set the 'content' field to a higher boosting value
AnswerC

Semantic search uses advanced ranking models that consider the meaning and relationship between words.

Why this answer

Semantic search in Azure AI Search uses deep neural network models to understand the intent and context of multi-word queries like 'budget forecast', rather than relying solely on keyword matching. This improves ranking by capturing the semantic relationship between terms, so documents containing both words (or conceptually related content) are ranked higher even if the exact phrase is not present.

Exam trap

The trap here is that candidates often confuse boosting (Option D) with semantic understanding, but boosting only increases term frequency weight and does not address the semantic gap between query terms in multi-word searches.

How to eliminate wrong answers

Option A is wrong because adding a separate field for each word would fragment the index and break the ability to score multi-word queries as a unit, degrading relevance rather than improving it. Option B is wrong because splitting on spaces only is the default behavior for standard analyzers; a custom analyzer that does the same would not change ranking behavior for multi-word queries. Option D is wrong because boosting the 'content' field increases the weight of all terms in that field uniformly, which does not specifically address the need to rank documents containing both query terms higher than those containing only one.

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99
MCQeasy

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

A.Azure AI Document Intelligence
B.Azure AI Computer Vision
C.Azure AI Translator
D.Azure AI Language
AnswerD

Offers sentiment and key phrase extraction skills.

Why this answer

Azure AI Language provides pre-built capabilities for sentiment analysis and key phrase extraction, which are exactly the skills needed to process customer support ticket text. When integrated as a skillset in Azure AI Search, it enriches the indexing pipeline by automatically extracting these insights from each document. The other services focus on different modalities (vision, translation, document structure) and do not offer native sentiment or key phrase extraction.

Exam trap

The AI-102 exam often tests the distinction between Azure AI Language (for text analytics) and Azure AI Document Intelligence (for document structure extraction), leading candidates to mistakenly choose Document Intelligence when the task involves analyzing text content rather than extracting form fields.

How to eliminate wrong answers

Option A is wrong because Azure AI Document Intelligence is designed for extracting structured data (like tables, forms, and key-value pairs) from scanned documents, not for analyzing sentiment or extracting key phrases from text. Option B is wrong because Azure AI Computer Vision analyzes images and video, not text content, so it cannot perform sentiment analysis or key phrase extraction on ticket text. Option C is wrong because Azure AI Translator focuses on language translation, not on extracting sentiment or key phrases from the original language text.

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100
MCQhard

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

A.Azure AI Document Intelligence, Azure AI Translator, and Azure AI Search
B.Azure AI Document Intelligence, Azure AI Video Indexer, and Azure AI Search
C.Azure AI Document Intelligence, Azure AI Language, custom skill for redaction, and Azure AI Search
D.Azure AI Document Intelligence, Azure AI Content Safety, and Azure AI Search
AnswerC

Document Intelligence extracts text, Language recognizes entities, custom skill redacts, Search indexes.

Why this answer

It combines Azure AI Document Intelligence for OCR and text extraction from PDFs and Word files, Azure AI Language for named entity recognition (e.g., parties, judges, case numbers), a custom skill for redaction (to remove sensitive information before indexing), and Azure AI Search to index the cleaned content for full-text search. This stack directly addresses all requirements: ingestion, entity extraction, redaction, and search indexing.

Exam trap

The trap here is that candidates often confuse Azure AI Content Safety (for moderation) with redaction capabilities, or assume Azure AI Translator can handle entity recognition, when in fact redaction requires a custom skill and entity recognition requires Azure AI Language.

How to eliminate wrong answers

Option A is wrong because Azure AI Translator is a translation service, not designed for named entity recognition or redaction; it would not extract legal entities or support redaction. Option B is wrong because Azure AI Video Indexer is for analyzing video and audio content, not for processing legal documents (PDFs/Word files); it cannot extract text or entities from documents. Option D is wrong because Azure AI Content Safety is for detecting harmful or offensive content (e.g., hate speech, violence), not for recognizing named entities or performing redaction of sensitive information like case numbers or party names.

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101
MCQeasy

You are a data engineer at a university. The university wants to digitize its historical student records (paper forms) to make them searchable. The records are scanned as images (JPEG) and stored in Azure Blob Storage. Each form contains handwritten fields: student name, ID number, date of birth, and degree. You need to extract these fields and index them in Azure AI Search. The solution must use Azure AI Services and minimize manual labeling effort. Which approach should you take?

A.Use Azure AI Custom Vision to train a model to detect handwriting regions, then use Azure AI Vision OCR to read text.
B.Use Azure AI Search with a blob indexer and a skillset that includes OCR skill and Entity Recognition skill.
C.Use Azure AI Document Intelligence to train a custom extraction model with a few labeled samples, then deploy as a custom skill in Azure AI Search.
D.Use Azure AI Vision OCR to extract text from images, then use Azure AI Language to extract entities like name, date, and degree.
AnswerC

Document Intelligence is designed for extraction from forms with minimal labeling.

Why this answer

Azure AI Document Intelligence (formerly Form Recognizer) is specifically designed to extract structured fields from forms with handwritten text. By training a custom extraction model with a few labeled samples, you minimize manual labeling effort while achieving high accuracy for fields like student name, ID, date of birth, and degree. The model can then be deployed as a custom skill in Azure AI Search to index the extracted data.

Exam trap

The trap here is that candidates often confuse general OCR (Azure AI Vision) with form-specific extraction (Azure AI Document Intelligence), overlooking that Document Intelligence is purpose-built for structured field extraction from forms with minimal labeling.

How to eliminate wrong answers

Option A is wrong because Azure AI Custom Vision is for image classification and object detection, not for handwriting recognition or OCR; it cannot extract text from handwritten fields. Option B is wrong because Azure AI Search's built-in OCR skill extracts raw text but lacks the ability to identify specific fields like student name or degree without additional custom logic, and Entity Recognition skill is designed for named entities in text, not for form field extraction. Option D is wrong because Azure AI Vision OCR extracts all text from an image but does not parse it into structured fields; Azure AI Language's entity recognition would require additional post-processing and manual mapping to identify specific form fields, increasing effort.

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102
MCQhard

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

A.Set the 'searchable' attribute on the description field and define a suggester
B.Set the 'filterable' attribute on the description field
C.Set the 'sortable' attribute on the ticket_id field
D.Set the 'facetable' attribute on the category field
AnswerA

Searchable enables full-text search; suggester enables autocomplete.

Why this answer

Fuzzy search requires the 'searchable' attribute on fields to enable full-text search, and autocomplete suggestions require a 'suggester' configured on the index. The suggester defines which fields are used to generate suggestion candidates, and the 'searchable' attribute allows the description field to be tokenized and matched against partial or misspelled queries.

Exam trap

The trap here is that candidates often confuse 'searchable' with 'filterable' or 'facetable', thinking any attribute that enables querying will also support fuzzy search and autocomplete, but only 'searchable' fields are analyzed and tokenized for these features, and a suggester is a separate required configuration.

How to eliminate wrong answers

Option B is wrong because the 'filterable' attribute is used for exact match filtering (e.g., category equals 'billing'), not for fuzzy search or autocomplete; it does not enable partial or approximate matching. Option C is wrong because the 'sortable' attribute on ticket_id only allows ordering results by that field, which has no relevance to fuzzy search or autocomplete suggestions. Option D is wrong because the 'facetable' attribute on category enables faceted navigation (e.g., drill-down counts), but does not support fuzzy matching or suggestion generation.

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103
MCQmedium

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

A.Prebuilt-receipt model
B.Prebuilt-idDocument model
C.Prebuilt-invoice model
D.Prebuilt-layout model
AnswerC

Prebuilt-invoice is designed for invoices and extracts line items, totals, and other fields.

Why this answer

The prebuilt-invoice model is specifically trained to extract key fields from invoices, including invoice number, date, total amount, and line items. Unlike other prebuilt models, it has dedicated field extraction for line item details such as description, quantity, unit price, and total, making it the correct choice for this scenario.

Exam trap

The trap here is that candidates may confuse the prebuilt-layout model's ability to extract table structure with the prebuilt-invoice model's trained field extraction for invoice-specific data, leading them to choose option D thinking layout analysis is sufficient for line item extraction.

How to eliminate wrong answers

Option A is wrong because the prebuilt-receipt model is optimized for receipt documents, which typically lack structured line items with descriptions and unit prices found in invoices. Option B is wrong because the prebuilt-idDocument model is designed for identity documents like passports and driver's licenses, not financial documents with line items. Option D is wrong because the prebuilt-layout model extracts text and table structure but does not have trained field extraction for invoice-specific fields like line items, invoice number, or total amount.

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104
MCQmedium

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

A.Detect the language of the query using Azure AI Language and then use a generic analyzer
B.Translate all queries to English using Azure AI Translator before searching
C.Use a single non-language-specific analyzer like 'standard.lucene' for all documents
D.Use language-specific analyzers in the Azure AI Search index for each language
AnswerD

Language analyzers provide stemming and stopword removal per language, improving search relevance.

Why this answer

Azure AI Search supports language-specific analyzers (e.g., 'de.microsoft' for German, 'fr.microsoft' for French) that apply linguistic rules such as stemming, lemmatization, and stop-word removal tailored to each language. This ensures that queries and documents are processed in the same language, enabling users to search and retrieve results in their native language without translation loss.

Exam trap

The trap here is that candidates assume translation or generic analyzers are sufficient, overlooking that Azure AI Search's language-specific analyzers are designed to preserve linguistic integrity and meet the exact requirement of native-language search and retrieval without cross-language conversion.

How to eliminate wrong answers

Option A is wrong because detecting the query language and then using a generic analyzer (like 'standard.lucene') would ignore language-specific linguistic rules, leading to poor recall and precision for non-English text (e.g., German compound words or French accents). Option B is wrong because translating all queries to English introduces translation latency, potential semantic errors, and forces results to be returned in English, violating the requirement to retrieve results in the user's native language. Option C is wrong because a non-language-specific analyzer like 'standard.lucene' performs only basic tokenization and lowercasing, failing to handle language-specific morphology (e.g., stemming for Arabic or diacritics for Spanish), which degrades search quality.

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105
MCQmedium

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

A.Azure AI Language (custom NER)
B.Azure AI Search with integrated vectorization
C.Azure OpenAI Service with GPT-4o
D.Azure AI Document Intelligence
AnswerD

Designed for extracting fields from forms and documents.

Why this answer

Azure AI Document Intelligence (formerly Form Recognizer) is the correct choice because it is specifically designed for document analysis and extraction of structured data such as parties, dates, and monetary amounts from PDFs and images. Its prebuilt models (e.g., the 'prebuilt-invoice' or 'prebuilt-contract' model) use optical character recognition (OCR) and deep learning to extract key-value pairs and entities from contracts without requiring custom training.

Exam trap

The trap here is that candidates often confuse Azure AI Language (custom NER) as a suitable choice for PDF extraction, overlooking that it requires pre-extracted text and lacks native PDF processing, while Azure AI Document Intelligence is the dedicated service for document-based extraction.

How to eliminate wrong answers

Option A is wrong because Azure AI Language custom NER is designed for extracting custom entities from unstructured text, but it lacks native OCR capabilities for PDF documents and requires manual text extraction and labeling, making it inefficient for direct PDF processing. Option B is wrong because Azure AI Search with integrated vectorization is a retrieval and indexing service, not a primary extraction engine; it relies on upstream extractors (like Document Intelligence) to populate its index and does not directly parse PDFs for entities. Option C is wrong because Azure OpenAI Service with GPT-4o is a generative AI model that can perform extraction via prompt engineering, but it is not optimized for precise, deterministic extraction of structured fields from PDFs, often suffers from hallucination, and incurs higher latency and cost compared to a dedicated document extraction service.

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106
MCQeasy

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

A.Azure AI Translator and Azure AI Search
B.Azure AI Speech and Azure AI Search
C.Azure AI Document Intelligence and Azure AI Language
D.Azure AI Language (key phrase extraction, entity recognition, sentiment analysis)
AnswerD

Azure AI Language provides built-in capabilities for extracting issues, resolutions, and sentiment from text.

Why this answer

Azure AI Language provides the specific capabilities needed: key phrase extraction to identify the customer's issue, entity recognition to identify the resolution, and sentiment analysis to determine the call sentiment. These are all built-in features of the Language service, making it the single-service solution for this knowledge mining task.

Exam trap

This question tests the misconception that multiple services are needed for a task that a single service can handle, leading candidates to overcomplicate the solution with unnecessary combinations like Speech + Search or Document Intelligence + Language.

How to eliminate wrong answers

Option A is wrong because Azure AI Translator is for language translation, not for extracting issues, resolutions, or sentiment from text; Azure AI Search indexes and retrieves data but does not perform NLP extraction. Option B is wrong because Azure AI Speech transcribes audio to text but does not perform entity recognition or sentiment analysis; Azure AI Search again only handles indexing and search, not extraction. Option C is wrong because Azure AI Document Intelligence is designed for extracting text and structure from documents (e.g., forms, invoices), not for analyzing conversational transcripts for issues, resolutions, or sentiment; while Azure AI Language could help, the combination is unnecessary and Document Intelligence adds no value here.

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107
Multi-Selectmedium

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

Select 2 answers
A.Create a private endpoint in the storage account for the search service
B.Use a shared access key (SAS) in the data source definition
C.Configure the search service to use a system-assigned managed identity
D.Allow the search service's IP address in the storage account firewall
E.Disable the storage account firewall entirely
AnswersC, D

Managed identity provides secure access without keys.

Why this answer

To ensure that an Azure AI Search indexer can access data from an Azure Storage account containing sensitive data, you should use a system-assigned managed identity for the search service (Option C) and allow the search service's IP address in the storage account firewall (Option D). Using a managed identity eliminates the need for keys and provides secure authentication. Allowing the search service's IP ensures that the indexer can connect through the firewall.

Option A is incorrect because a private endpoint is for accessing the storage from a virtual network, not for indexer access. Option B is incorrect because shared access keys are less secure and expose the storage account. Option E is incorrect because disabling the firewall entirely is insecure.

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108
MCQmedium

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

A.Create a custom skill that calls Azure AI Document Intelligence and returns extracted fields, then use outputFieldMappings to map to index fields.
B.Use Azure AI Document Intelligence's pre-built model to analyze documents and store results in a database, then use a SQL indexer to index the database.
C.Use the OCR skill to extract text and then use regular expressions to find PO number and total.
D.Manually enter the extracted data into the search index.
AnswerA

This integrates Document Intelligence into the skillset and maps outputs to index fields.

Why this answer

Azure AI Document Intelligence extracts structured data (like PO number and total amount) from documents, but this data must be explicitly mapped to Azure AI Search index fields using outputFieldMappings in a custom skill. The custom skill invokes Document Intelligence via the Skillset, and outputFieldMappings bridge the extracted fields to the search index schema, making them searchable. This approach ensures the extracted fields are ingested into the index without manual intervention.

Exam trap

The trap here is that candidates assume Azure AI Document Intelligence's output is automatically indexed by Azure AI Search, but in reality, you must explicitly define a custom skill and outputFieldMappings to transfer extracted fields into the search index.

How to eliminate wrong answers

Option B is wrong because it introduces an unnecessary intermediate database and SQL indexer, adding complexity and latency; Azure AI Search can directly ingest Document Intelligence output via skillsets without a database hop. Option C is wrong because the OCR skill only extracts raw text, not structured fields like PO number or total amount; using regular expressions on OCR output is fragile and error-prone compared to Document Intelligence's pre-trained models that natively extract key-value pairs. Option D is wrong because manual data entry defeats the purpose of an automated knowledge mining solution and is not scalable or reliable for production workloads.

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109
MCQhard

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

A.Increase the indexer execution timeout in the indexer definition
B.Change the parsing mode of the indexer to 'text'
C.Split large PDFs into smaller files before uploading
D.Enable incremental enrichment on the skillset
AnswerA

The timeout can be increased to allow large documents to be processed.

Why this answer

The 'Document extraction timeout' error indicates that the indexer is taking longer than the default 24-hour timeout to process certain PDFs. Increasing the indexer execution timeout in the indexer definition allows the indexer to continue processing these large documents without losing data, as it extends the maximum time the indexer can run for a single execution.

Exam trap

The trap here is that candidates often confuse 'indexer execution timeout' with 'document extraction timeout' and assume the solution must involve changing parsing modes or splitting files, rather than adjusting the indexer's maximum runtime.

How to eliminate wrong answers

Option B is wrong because changing the parsing mode to 'text' would skip the native PDF parsing and extract raw text, which might lose structured content like tables or metadata, and does not address the timeout issue for large documents. Option C is wrong because splitting large PDFs into smaller files before uploading would require manual intervention and data restructuring, potentially losing the original document context, and does not solve the timeout within the indexer configuration. Option D is wrong because enabling incremental enrichment on the skillset only caches enrichment outputs to avoid reprocessing unchanged documents, but it does not extend the execution timeout for the indexer, so it would not resolve the timeout error for large PDFs.

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110
MCQmedium

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

A.Custom classification model
B.OCR capability
C.Layout extraction
D.Prebuilt expense report model
AnswerA

Custom classification models can categorize documents based on their content.

Why this answer

Azure AI Document Intelligence's custom classification model is specifically designed to categorize documents (such as expense report images) into user-defined classes (e.g., travel, office supplies) before any extraction occurs. This model uses a trained classifier to assign a document type based on its visual and textual features, enabling downstream processing with the appropriate extraction model.

Exam trap

The trap here is that candidates often confuse the prebuilt expense report model (which extracts data) with the classification model (which categorizes documents), leading them to select Option D despite the question explicitly asking for classification before extraction.

How to eliminate wrong answers

Option B is wrong because OCR (Optical Character Recognition) capability only extracts text from images and does not perform document classification or categorization. Option C is wrong because layout extraction analyzes the structure (tables, paragraphs, headers) of a document but does not assign it to a predefined category. Option D is wrong because the prebuilt expense report model is designed to extract fields (e.g., vendor, total) from a known expense report format, not to classify arbitrary submitted images into categories like travel or office supplies.

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111
MCQeasy

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

A.Entity Recognition skill
B.Conditional skill
C.PII Detection skill
D.Text Translation skill
AnswerC

The PII Detection skill can identify and redact sensitive information like credit card numbers.

Why this answer

The PII Detection skill is the correct built-in skill for automatically identifying and redacting sensitive information like credit card numbers from indexed content in Azure AI Search. It uses pre-trained models to detect patterns such as credit card numbers, social security numbers, and other personally identifiable information, and can either mask or remove them from the text before it is stored in the search index.

Exam trap

The trap here is that candidates may confuse the Entity Recognition skill (which can identify entities but not redact them) with the PII Detection skill, assuming that entity extraction inherently includes removal, when in fact redaction requires a separate skill designed for that purpose.

How to eliminate wrong answers

Option A is wrong because the Entity Recognition skill extracts named entities like people, organizations, and locations, but it does not have built-in redaction capabilities for sensitive data patterns like credit card numbers. Option B is wrong because the Conditional skill is used to apply conditional logic (if-then-else) to skill outputs, not to detect or remove sensitive information. Option D is wrong because the Text Translation skill translates text between languages and has no functionality for identifying or redacting sensitive data such as credit card numbers.

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112
MCQhard

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

A.Use Azure AI Search with encryption at rest
B.Implement role-based access control on the search index
C.Use a custom skill in the skillset to detect and redact PII before indexing
D.Configure field mappings to exclude PII fields
AnswerC

Redacting PII in the enrichment pipeline prevents it from appearing in search results.

Why this answer

Using a custom skill in the enrichment pipeline allows you to detect and redact PII from documents before they are indexed, ensuring that sensitive data is not stored in the index or returned in search results. Option A is incorrect because encryption at rest protects data at the storage level but does not prevent PII from being returned. Option B is incorrect because role-based access control restricts who can search but does not remove PII from results.

Option D is incorrect because field mappings control which fields are imported but do not remove PII content from those fields.

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113
MCQhard

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

A.Train a new model version using the 'compose' operation or copy the existing model and retrain with new samples
B.Use a multi-model ensemble by training separate models per template
C.Retrain the model from scratch each month using all historical data
D.Use Azure Machine Learning pipelines to automate retraining and deploy a new endpoint
AnswerA

Model composition allows building on top of existing models.

Why this answer

Azure AI Document Intelligence supports model versioning through the 'compose' operation, which allows you to combine multiple trained models into a single composed model, or by copying an existing model and retraining it with new samples. This approach preserves the existing model's knowledge while incrementally updating it with new contract templates, avoiding the need to retrain from scratch each month.

Exam trap

The trap here is that candidates may assume retraining from scratch (Option C) is the only way to incorporate new data, overlooking the 'compose' operation and model copying features that enable incremental updates without losing prior training.

How to eliminate wrong answers

Option B is wrong because using a multi-model ensemble by training separate models per template would require managing multiple endpoints and manually routing documents to the correct model, which is inefficient and not the recommended approach for handling versioning and retraining in Azure AI Document Intelligence. Option C is wrong because retraining the model from scratch each month using all historical data is computationally expensive, time-consuming, and does not leverage the built-in versioning capabilities like the 'compose' operation or model copying, which are designed for incremental updates. Option D is wrong because while Azure Machine Learning pipelines can automate retraining and deployment, they are not the recommended approach for Azure AI Document Intelligence custom models; Document Intelligence provides its own native model management and retraining features (such as the 'compose' operation) that are simpler and more directly integrated for this specific service.

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114
Multi-Selecthard

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

Select 3 answers
A.Define input and output parameters in the skillset definition
B.Handle errors in the skill and return appropriate status codes
C.Ensure the skill executes within 2 minutes
D.Write the skill in a language supported by Azure AI Search
E.Deploy the skill as an Azure Function or other HTTP endpoint
AnswersA, B, E

The skillset must specify inputs and outputs.

Why this answer

Custom skills in Azure AI Search require explicit input and output parameter definitions in the skillset JSON. These parameters map data from the enrichment pipeline into the skill and return results back, enabling the search service to pass context (e.g., document fields) and consume the skill's output for further indexing.

Exam trap

The trap here is that candidates confuse the 2-minute timeout often associated with Azure Functions (default 230 seconds) with the 30-second timeout enforced by Azure AI Search for custom skills, leading them to incorrectly select Option C.

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115
MCQhard

Your company is developing a knowledge mining solution for a legal firm that needs to extract information from scanned legal documents. The documents contain handwritten notes in addition to printed text. You need to extract both printed and handwritten text. You are using Azure AI Document Intelligence with the Read OCR model. The solution must be integrated into Azure AI Search. During testing, the printed text is extracted correctly, but handwritten text is often missing or incorrect. What should you do to improve the extraction of handwritten text?

A.Preprocess the documents to increase image resolution before sending to Document Intelligence.
B.Add an OCR skill from Azure AI Search's built-in skills to the skillset.
C.Train a custom Document Intelligence model on handwritten samples.
D.Ensure the Document Intelligence skill is configured with the Read OCR model and handwriting recognition enabled.
AnswerD

The Read model with handwriting option extracts both printed and handwritten text.

Why this answer

The Document Intelligence Read OCR model supports handwriting recognition, but it must be explicitly enabled in the skill configuration. By ensuring handwriting recognition is enabled, the solution will extract handwritten text. Option C is incorrect because training a custom model is unnecessary; the Read OCR model already handles handwriting when enabled.

Option A is incorrect because increasing resolution may improve overall quality but does not specifically enable handwriting recognition. Option B is incorrect because the built-in OCR skill in Azure AI Search does not support handwriting.

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116
MCQmedium

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

A.Set context to '/document/content' and input source to '/document/metadata'.
B.Set context to '/document/content' and input source to '/document/content'.
C.Set context to '/document' and input source to '/document/normalized_images/*'.
D.Set context to '/document' and input source to '/document/content'.
AnswerD

Correct: passes entire content.

Why this answer

To pass the entire document content as input to a custom skill, the skill's context should be set to '/document', which allows the skill to run once per document. The input source should be '/document/content' to reference the entire content field of the document. Options A and B set context to '/document/content', which runs the skill on each content node individually, not the whole document.

Option C uses '/document/normalized_images/*' as input, which is for images, not the content. Therefore, option D is correct.

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117
MCQmedium

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

A.The people and organizations fields should be Edm.String instead of Collection(Edm.String)
B.The id field is not defined as a key in the index
C.The suggester sourceFields do not include people or organizations
D.The suggester searchMode should be 'analyzingInfixMatching' which is incorrect
AnswerC

Suggestions are only generated from the content field.

Why this answer

A suggester in Azure Cognitive Search only returns suggestions for fields explicitly listed in its `sourceFields` property. If the `people` and `organizations` fields are not included in `sourceFields`, the suggester cannot match partial names typed in the search bar, even if those fields are indexed and searchable. The suggester relies on prefix matching against the specified source fields to generate suggestions.

Exam trap

The trap here is that candidates may focus on data types or index keys instead of recognizing that the suggester's `sourceFields` property explicitly controls which fields participate in suggestion generation, a detail frequently tested in AI-102.

How to eliminate wrong answers

Option A is wrong because `Collection(Edm.String)` is the appropriate type for fields that contain multiple values (e.g., multiple people or organizations per document); changing them to `Edm.String` would lose multi-value support and is not related to suggester functionality. Option B is wrong because the `id` field being defined as a key is required for any index, but its presence or absence does not affect whether a suggester returns suggestions for other fields. Option D is wrong because `analyzingInfixMatching` is not a valid `searchMode` for a suggester; the correct `searchMode` is `analyzingInfixMatching` (note the typo in the option) but the real issue is that the suggester's `sourceFields` must include the target fields, not the search mode.

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118
MCQeasy

You are designing a knowledge mining solution to extract information from scanned invoices stored as multi-page TIFF images. Which two Azure AI services should you combine to extract text and structure the data?

A.Azure AI Language and Azure AI Search
B.Azure AI Document Intelligence and Azure AI Vision
C.Azure AI Search and Azure AI Vision
D.Azure AI Translator and Azure AI Document Intelligence
AnswerB

Document Intelligence extracts structured data; Vision OCR extracts text.

Why this answer

Azure AI Document Intelligence (formerly Form Recognizer) can extract structured data like invoice fields, while Azure AI Vision provides OCR to extract text from multi-page TIFF images. Option A is incorrect because Azure AI Language does not process images directly. Option C is incorrect because Azure AI Search indexes data but does not extract text from images.

Option D is incorrect because Azure AI Translator translates text but does not extract or structure data.

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119
MCQhard

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

A.The skills are defined in the wrong order; sentiment should run before split
B.The SentimentSkill is not supported in this region
C.The SplitSkill outputs are not correctly mapped to the SentimentSkill input; the skill runs only on the first page of each document
D.The context of the SentimentSkill should be "/document" instead of "/document/pages/*"
AnswerC

The context "/document/pages/*" should iterate over pages, but if split output is only one item, it only processes one page.

Why this answer

The SplitSkill divides each PDF into pages, and by default the SentimentSkill runs in the context of each page (e.g., /document/pages/*). If the SplitSkill outputs are not correctly mapped to the SentimentSkill input, the sentiment skill may only process the first page or fail to receive the split content, resulting in sentiment scores for only a subset of documents. Correct mapping ensures that each page's content is passed to the SentimentSkill for analysis.

Exam trap

The trap here is that candidates often assume the issue is with skill ordering or regional availability, but the real cause is the misalignment of skill context and input/output mappings, which is a subtle but critical detail in skillset definitions.

How to eliminate wrong answers

Option A is wrong because the order of skills (split before sentiment) is actually correct; sentiment must run after split to analyze individual pages, not before. Option B is wrong because the SentimentSkill is a generally available cognitive skill supported in all Azure regions where AI Search is available, so regional unavailability is not a plausible cause. Option D is wrong because setting the context to "/document" would cause the SentimentSkill to run once per document on the entire concatenated text, not per page, which would not explain why only 500 out of 1000 documents have scores; the issue is specifically about missing per-page sentiment due to mapping errors.

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120
Multi-Selecteasy

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

Select 2 answers
A.Azure AI Document Intelligence
B.Azure AI Speech
C.Azure AI Vision
D.Azure AI Search
E.Azure AI Language
AnswersA, C

Document Intelligence (Form Recognizer) extracts text from documents, including handwriting.

Why this answer

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

Azure AI Search (Option D) is for indexing and searching data, not text extraction. Azure AI Language (Option E) provides NLP capabilities, not OCR.

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121
Multi-Selecthard

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

Select 3 answers
A.Train the classification model with one sample per type
B.Create a custom classification model in Document Intelligence
C.Label at least 5 samples for each document type
D.Chain the classification model with extraction models
E.Use the prebuilt invoice and purchase order models for classification
AnswersB, C, D

Custom classification models categorize documents by type.

Why this answer

Azure AI Document Intelligence requires you to create a custom classification model to distinguish between document types like invoices and purchase orders. This model is built by training on labeled samples, enabling it to assign a document type before extraction. Without a custom classifier, the service cannot automatically route documents to the appropriate extraction model.

Exam trap

The trap here is that candidates confuse prebuilt models (which perform extraction) with classification capabilities, assuming they can automatically identify document types without a dedicated classifier.

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122
MCQhard

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

A.Use Azure AI Vision OCR skill for text extraction, add a translation skill, and use a simple search query
B.Use Azure AI Document Intelligence layout model with OCR, add a custom translation skill, and configure a scoring profile with freshness boosting
C.Use Azure AI Document Intelligence prebuilt-read model, add a custom skill for language detection, and schedule the indexer weekly
D.Use Azure AI Language text extraction, a custom entity recognition skill, and enable semantic ranking
AnswerB

Document Intelligence extracts tables and handwriting; translation skill handles multilingual; scoring profile boosts recent docs.

Why this answer

Using Azure AI Document Intelligence's layout and OCR capabilities extracts text, tables, and handwriting. The enrichment pipeline with a custom skill using the translation service handles multilingual content, and a scoring profile with freshness boosting prioritizes recent reports. Option A is incorrect because Azure AI Vision OCR alone does not extract table structure.

Option C is incorrect because the Language service does not handle document layout. Option D is incorrect because scheduling the indexer once a week does not meet the daily update requirement.

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123
MCQmedium

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

A.Create a custom entity extraction model in Syntex using AI Builder.
B.Integrate Azure AI Search with SharePoint to extract entities.
C.Use Power Automate with AI Builder to extract entities from invoices.
D.Create a document understanding model in Syntex that extracts entities from invoices.
AnswerD

Syntex document understanding models can extract custom entities.

Why this answer

Microsoft Syntex document understanding models can classify documents and extract entities such as invoice numbers and dates. Option A is incorrect because Syntex does not use AI Builder's out-of-the-box entity extraction; it uses its own model training. Option B is incorrect because Azure AI Search is a search service, not an entity extraction service integrated with Syntex.

Option C is incorrect because Power Automate with AI Builder is a separate workflow automation approach, not part of Syntex. Therefore, option D is correct: create a document understanding model in Syntex that extracts entities from invoices.

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124
MCQeasy

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

A.Azure AI Language
B.Azure AI Speech
C.Azure AI Vision OCR
D.Azure AI Document Intelligence layout model
AnswerD

The layout model extracts checkboxes, signatures, and handwritten text from forms.

Why this answer

Azure AI Document Intelligence's layout model is designed to extract text, tables, checkboxes, signatures, and structure from documents, including handwritten forms. It combines OCR with deep learning models to understand the spatial relationships between elements, making it the correct choice for this scenario.

Exam trap

The trap here is that candidates often confuse Azure AI Vision OCR (which handles printed text) with Document Intelligence's layout model (which handles handwritten text, checkboxes, and signatures), leading them to choose option C incorrectly.

How to eliminate wrong answers

Option A is wrong because Azure AI Language focuses on text analytics (e.g., sentiment, key phrase extraction, entity recognition) and does not process images or handwritten content. Option B is wrong because Azure AI Speech handles audio-to-text transcription and speech recognition, not visual document analysis. Option C is wrong because Azure AI Vision OCR extracts printed text from images but lacks native support for detecting checkboxes, signatures, and the layout structure of forms.

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125
MCQmedium

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

A.The indexer is not configured with the PDF parser; set the parsing mode
B.The file format is unsupported; convert to PDF/A
C.The file is too large; split it into smaller parts
D.The PDF is encrypted; remove encryption before indexing
AnswerD

Encrypted documents cannot be processed; decryption is required.

Why this answer

Azure AI Search cannot index encrypted documents; the solution is to remove encryption before indexing. Option A is incorrect: the indexer does not require a specific PDF parser; it can index PDFs by default with the appropriate skillset. Option B is incorrect: unsupported file formats would generate a different error, and encryption is not a format issue.

Option C is incorrect: file size limits exist (up to 16 MB for PDFs), but the error specifically mentions encryption, not size.

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126
MCQmedium

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

A.Create a custom skill that calls the custom NER endpoint and map the output to the index fields.
B.Use a Language Understanding (LUIS) app to extract entities and call it from a custom skill.
C.Use the built-in Entity Recognition skill and configure it with your custom model's endpoint.
D.Configure the indexer to call the custom NER endpoint directly during indexing.
AnswerA

Custom skills allow integration with any REST API, including custom NER.

Why this answer

Custom NER models must be integrated into the enrichment pipeline via a custom skill that calls the model's REST endpoint and maps the extracted entities to index fields. Built-in Entity Recognition skills cannot use custom models (so C is wrong). LUIS is designed for language understanding, not custom NER (so B is wrong).

Indexers cannot directly call external APIs; they rely on skills in the skillset (so D is wrong).

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127
MCQeasy

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

A.Entity Recognition skill
B.Document Extraction skill
C.Custom Web API skill
D.Cognitive Services skill
AnswerD

This skill allows you to call Azure AI Language APIs for key phrases and sentiment.

Why this answer

Key phrase extraction and sentiment analysis are cognitive skills available in the Azure AI Language service. The Cognitive Services skill references a Cognitive Services resource that provides these capabilities.

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128
MCQhard

You are designing a solution to extract customer names and addresses from scanned handwritten forms. The forms are stored as images in Azure Blob Storage. The extraction must achieve high accuracy with minimal manual review. Which combination of Azure AI services should you use?

A.Azure AI Document Intelligence with prebuilt invoice and receipt models
B.Azure AI Document Intelligence with a custom model trained on handwritten forms
C.Azure AI Language Service with custom Named Entity Recognition (NER)
D.Azure AI Computer Vision with OCR and Azure AI Search
AnswerB

Custom models can be trained on handwriting samples to achieve high accuracy.

Why this answer

Azure AI Document Intelligence's custom model capability allows you to train a model specifically on handwritten forms, enabling it to learn the unique handwriting patterns and layout structures present in your scanned documents. This tailored approach achieves high accuracy with minimal manual review, as the model is optimized for your specific form type rather than generic invoice or receipt templates.

Exam trap

The trap here is that candidates often confuse prebuilt models (which work well for printed documents) with custom models (which are necessary for handwritten forms), or they assume OCR alone is sufficient without considering the need for structured field extraction.

How to eliminate wrong answers

Option A is wrong because prebuilt invoice and receipt models are designed for structured, printed documents and cannot reliably extract handwritten text with high accuracy, leading to increased manual review. Option C is wrong because Azure AI Language Service with custom NER extracts entities from text but does not perform OCR or handle image-based handwritten input, so it cannot process scanned forms directly. Option D is wrong because Azure AI Computer Vision with OCR provides raw text extraction but lacks the document understanding and field-level extraction capabilities needed to accurately parse structured fields like customer names and addresses from forms, and Azure AI Search is for indexing and querying, not extraction.

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129
Multi-Selecteasy

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

Select 2 answers
A.Azure Bot Service
B.Azure AI Speech to text
C.Azure AI Language translation
D.Azure AI Document Intelligence's read model
E.Azure AI Search's built-in OCR skill
AnswersD, E

Extracts text from documents and images.

Why this answer

Azure AI Document Intelligence's read model is specifically designed to extract printed and handwritten text from images and documents, and it can be integrated as a custom skill in an Azure AI Search enrichment pipeline to populate searchable text fields. Option E is correct because Azure AI Search includes a built-in OCR (optical character recognition) skill that can be added to a skillset to extract text from image files during the indexing process.

Exam trap

The trap here is that candidates often confuse Azure AI Document Intelligence's read model (which is a dedicated OCR skill) with the general-purpose Computer Vision OCR, or mistakenly think that Azure AI Search's built-in OCR skill is not a valid option because it is part of the search service rather than a standalone AI service.

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130
Multi-Selectmedium

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

Select 3 answers
A.Text Analytics for Health skill in an Azure AI Search skillset.
B.Azure AI Search index.
C.Text Analytics for Health model in Microsoft Foundry.
D.Azure AI Document Intelligence (formerly Form Recognizer) custom model.
E.Language Understanding (LUIS) model.
AnswersA, B, C

This skill integrates the model into the indexing pipeline.

Why this answer

The Text Analytics for Health skill in an Azure AI Search skillset applies the pre-built healthcare model to extract medical conditions, medications, and treatment procedures during indexing. Option B is correct because the Azure AI Search index stores the extracted healthcare entities and supports search and retrieval. Option C is correct because the Text Analytics for Health model is a pre-built model available in Microsoft Foundry (Azure AI Foundry) for direct use in knowledge mining solutions.

Option D is incorrect because Azure AI Document Intelligence custom model is designed for extracting structured data from forms and requires training, not for pre-built healthcare entity extraction. Option E is incorrect because Language Understanding (LUIS) is used for conversational intent and entity recognition, not for extracting medical concepts from documents.

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131
MCQhard

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

A.Apply a filter
B.Configure a scoring profile
C.Enable semantic search
D.Add a synonym map to the index
AnswerD

Synonyms map equivalent terms to improve recall.

Why this answer

A synonym map in Azure AI Search allows you to define equivalent terms (e.g., 'disk failure' = 'hard drive crash') so that queries automatically expand to include synonyms. This directly addresses the vocabulary mismatch between user queries and indexed content, improving recall without requiring changes to the documents or queries.

Exam trap

The trap here is that candidates often confuse semantic search (which improves ranking via language models) with synonym expansion (which directly addresses vocabulary mismatch by broadening the query), leading them to choose option C instead of D.

How to eliminate wrong answers

Option A is wrong because a filter narrows results based on structured field criteria (e.g., date range, category) and does not expand query terms to match synonyms. Option B is wrong because a scoring profile boosts relevance ranking based on fields or functions (e.g., freshness, magnitude) but does not alter which documents match the query. Option C is wrong because semantic search re-ranks results using language understanding to improve relevance, but it does not expand the query to include synonymous terms; it still relies on the original query tokens for matching.

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132
MCQeasy

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

A.Azure AI Vision
B.Azure AI Language
C.Azure AI Search
D.Azure AI Document Intelligence
AnswerD

Specialized for form extraction.

Why this answer

Azure AI Document Intelligence (formerly Form Recognizer) is the correct service because it is specifically designed to extract key-value pairs, tables, and structured data from scanned forms and documents using prebuilt and custom models. This aligns directly with the requirement for knowledge mining from scanned forms.

Exam trap

The trap here is that candidates often confuse Azure AI Vision's OCR capability with form-specific extraction, not realizing that Document Intelligence is the dedicated service for key-value pair extraction from scanned forms, while Vision only provides raw text coordinates without semantic understanding.

How to eliminate wrong answers

Option A is wrong because Azure AI Vision provides image analysis capabilities like OCR, object detection, and captioning, but it does not have native support for extracting key-value pairs from forms; it would require additional processing to structure the data. Option B is wrong because Azure AI Language focuses on text analytics, sentiment analysis, and entity recognition from written text, not from scanned forms or document layouts. Option C is wrong because Azure AI Search is a search indexing and query service that can index extracted data but does not perform the extraction itself; it relies on other services like Document Intelligence to provide the structured input.

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133
MCQhard

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

A.Use Azure AI Document Intelligence custom model to extract product info from manuals/specs. Use a separate Azure AI Search pipeline for customer reviews with sentiment analysis. Enable semantic search.
B.Use a single Azure AI Search pipeline with a skillset that includes Document Layout skill, Text Translation skill (to English), Sentiment skill, and Key Phrase Extraction skill. Enable semantic search.
C.Use Azure AI Search with a blob indexer and a skillset that includes OCR skill (for scanned PDFs), Text Translation skill, Sentiment skill, and Entity Recognition skill. Enable semantic search.
D.Use Azure AI Document Intelligence to extract product info from all documents, then feed into Azure AI Search. Enable semantic search.
AnswerB

Single pipeline handles all document types, translates, extracts sentiment, and enables natural language queries.

Why this answer

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

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134
Multi-Selecteasy

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

Select 2 answers
A.Azure AI Language
B.Azure AI Document Intelligence
C.Azure AI Computer Vision
D.Azure AI Video Indexer
E.Azure AI Custom Vision
AnswersB, C

Includes OCR and layout extraction.

Why this answer

Azure AI Document Intelligence (formerly Form Recognizer) is correct because it is specifically designed to extract text, tables, and key-value pairs from scanned documents and images using optical character recognition (OCR) and deep learning models. It is a core service for knowledge mining pipelines that require structured data extraction from unstructured documents.

Exam trap

The trap here is that candidates often confuse Azure AI Computer Vision's OCR capabilities with Azure AI Document Intelligence, but Document Intelligence is the dedicated service for structured document extraction in knowledge mining, while Computer Vision provides general-purpose image analysis and OCR without the same level of document-specific parsing.

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135
MCQeasy

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

A.The indexer encountered errors and marked the documents as failed
B.The index does not have a semantic configuration
C.The search service has insufficient replicas
D.The search service is throttled due to high query volume
AnswerA

Indexer errors prevent documents from being indexed.

Why this answer

If the indexer encounters errors while processing specific documents (e.g., unsupported formats, parsing failures, or skill errors), those documents are marked as failed and not indexed. This explains why only some documents are missing from search results. Option B is incorrect because semantic configuration affects ranking and relevance features, not whether documents are indexed.

Option C is incorrect because insufficient replicas affect query performance and availability, not document ingestion. Option D is incorrect because throttling during high query volume would impact all search queries, not the indexing of specific documents.

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136
Multi-Selectmedium

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

Select 2 answers
A.Use the appropriate search tier (S1, S2, etc.) based on document size
B.Use the free tier for production workloads
C.Increase the replica count for better indexing throughput
D.Batch documents in groups of up to 1000 per index operation
E.Disable scoring profiles to speed up indexing
AnswersA, D

Higher tiers have better indexing capacity.

Why this answer

Choosing the appropriate search tier (S1, S2, etc.) ensures that the service has sufficient resources (CPU, memory, disk I/O) to handle the indexing load and document size. Higher tiers provide better indexing throughput and storage capacity, which is critical for large volumes of data. Using an undersized tier can lead to throttling, timeouts, or failed indexing operations.

Exam trap

The trap here is confusing replicas (which scale query performance) with partitions (which scale indexing throughput), leading candidates to incorrectly select option C as a way to improve indexing speed.

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137
MCQmedium

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

A.Azure AI Search with Azure AI Bot Service
B.Azure AI Language Service - Custom Question Answering
C.Azure AI Computer Vision
D.Azure AI Document Intelligence
AnswerA

Index documents with Search and use Bot Service for Q&A.

Why this answer

Azure AI Search indexes the content from Word documents and PDFs, enabling full-text and vector search over the knowledge base. Azure AI Bot Service provides the conversational interface that queries the search index and returns answers to users. Together, they form a retrieval-augmented generation (RAG) pipeline that answers questions from unstructured documents.

Exam trap

Azure exam often tests the distinction between a dedicated Q&A service (Custom Question Answering) and a general-purpose search-and-retrieve pipeline (Azure AI Search + Bot Service), leading candidates to choose Option B when the requirement is to answer from unstructured documents rather than curated Q&A pairs.

How to eliminate wrong answers

Option B is wrong because Azure AI Language Service - Custom Question Answering is designed for extracting Q&A pairs from structured FAQ-like content, not for ad-hoc retrieval from arbitrary Word and PDF documents without predefined question-answer pairs. Option C is wrong because Azure AI Computer Vision is an image analysis service for extracting text from images (OCR) and describing visual content, not for building conversational retrieval systems over documents. Option D is wrong because Azure AI Document Intelligence (formerly Form Recognizer) extracts structured data (like tables, key-value pairs) from documents, but it does not provide a conversational interface or semantic search over the extracted content.

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138
MCQmedium

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

A.Use the Entity Recognition skill to extract entities and then index entities only.
B.Use the Language Detection skill to identify language and then index all content as-is.
C.Use the Text Translation skill to translate all content to English during indexing.
D.Use the Key Phrase Extraction skill to extract key phrases and then index them.
AnswerC

Text Translation skill translates documents to a target language, enabling search in English only.

Why this answer

You can use the Text Translation skill to translate content to English during indexing, and then index only the translated text. Option A would not translate. Option B only detects language.

Option D uses two skills unnecessarily.

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139
Multi-Selecthard

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

Select 3 answers
A.A Web API endpoint that processes documents
B.Field mappings to pass data between the skill and the indexer
C.A machine learning model trained in Azure Machine Learning
D.An Azure Function to trigger the skill on a schedule
E.Input and output definitions in JSON format
AnswersA, B, E

Custom skills are implemented as web APIs.

Why this answer

A custom skill in Azure AI Search must be implemented as a Web API endpoint that integrates with the AI Search enrichment pipeline. This endpoint receives JSON payloads containing documents to be processed, performs custom logic (e.g., entity extraction, classification), and returns enriched JSON results. The Web API must be hosted (e.g., on Azure Functions, App Service) and conform to the specific request/response schema defined by Azure AI Search.

Exam trap

The trap here is that candidates often assume a custom skill must involve a machine learning model (Option C) or a scheduled trigger (Option D), but the core requirement is simply a Web API endpoint with proper JSON input/output definitions and field mappings to integrate with the indexer.

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140
Multi-Selectmedium

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

Select 2 answers
A.Enable CORS on the Azure Function app to allow cross-origin requests
B.Configure a retry policy in the skillset definition for the custom skill
C.Provide a managed identity for the search service to access the Azure Function
D.Set the indexer's execution timeout to unlimited
E.Add the external API endpoint to the indexer's allowed domains list
AnswersB, C

Retry policy handles transient errors when calling the custom skill.

Why this answer

A custom skill in an Azure AI Search skillset can fail due to transient errors when calling an external API. Configuring a retry policy in the skillset definition allows the indexer to automatically retry failed skill executions, improving resilience. Option C is correct because using a managed identity for the search service eliminates the need to manage credentials when accessing an Azure Function, providing secure authentication without storing secrets.

Exam trap

The trap here is that candidates often confuse client-side CORS requirements with server-to-server authentication, leading them to select Option A, when in fact managed identity and retry policies are the correct mechanisms for secure and resilient custom skill execution.

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141
Multi-Selecteasy

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

Select 2 answers
A.Filters
B.Indexers
C.Scoring profiles
D.Semantic ranking
E.Synonym maps
AnswersC, D

Scoring profiles boost results based on criteria.

Why this answer

Scoring profiles allow you to boost search results based on specific criteria such as field weight, freshness, or geographic distance, directly influencing relevance. Semantic ranking uses deep neural networks to re-rank results based on the semantic meaning of the query and documents, improving relevance beyond simple keyword matching.

Exam trap

The trap here is that candidates confuse features that expand query scope (like synonym maps or filters) with features that directly alter relevance scoring or ranking, leading them to pick options that affect recall rather than relevance.

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142
MCQhard

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

A.Enable incremental enrichment on the indexer
B.Disable field mappings
C.Increase the number of replicas
D.Use the free tier for the indexer
AnswerA

Incremental enrichment caches skill outputs, so on failure only changed documents are reprocessed, saving cost.

Why this answer

Enabling incremental enrichment caches intermediate results and recovers from failures without re-processing unchanged documents, reducing cost. Option B is incorrect because disabling field mappings would break the pipeline. Option C is incorrect because increasing the number of replicas improves query performance, not indexing.

Option D is incorrect because using a free tier is not feasible for large volumes.

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143
MCQhard

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

A.Add a custom scoring profile based on freshness.
B.Configure a scoring profile with a higher weight for the 'tags' field.
C.Set the 'tags' field to use the 'keyword' analyzer.
D.Enable semantic search on the 'content' field.
AnswerB

Field weighting boosts the importance of matches in the 'tags' field, improving relevance.

Why this answer

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

Option D enables semantic search on the 'content' field, which enhances understanding of natural language queries but does not specifically boost the weight of the tags field.

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144
Multi-Selectmedium

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

Select 3 answers
A.A custom skill to combine key phrases and language.
B.A search index that contains fields for the extracted data.
C.A skillset that includes the built-in Key Phrase Extraction and Language Detection skills.
D.A data source that points to the document store.
E.An indexer that runs on a schedule.
AnswersB, C, D

The index stores the enriched content.

Why this answer

A search index is the destination where extracted data (key phrases and language) must be stored for querying. Without an index, the extracted information has no structured location to be persisted and made searchable. The index schema must include fields specifically mapped to the output of the skillset's Key Phrase Extraction and Language Detection skills.

Exam trap

The trap here is that candidates often assume a custom skill is needed to merge multiple skill outputs, when in fact outputFieldMappings in the indexer configuration handle the routing of each skill's output to separate index fields without custom code.

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145
MCQeasy

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

A.Semantic search
B.Synonym maps
C.Cognitive skills
D.Knowledge mining
AnswerA

Semantic search improves relevance by understanding natural language queries and providing answer-style results.

Why this answer

Semantic search improves relevance by understanding natural language queries and providing answer-style results. Synonym maps (B) help with query expansion but not natural language understanding. Cognitive skills (C) are used for enrichment during indexing, not query-time interpretation.

Knowledge mining (D) is a broader process that encompasses multiple services, not a specific feature of Azure AI Search.

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146
MCQeasy

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

A.Azure AI Health Insights
B.Azure AI Document Intelligence
C.Azure AI Search
D.Azure AI Language
AnswerA

Azure AI Health Insights extracts diseases, medications, and treatments from clinical text.

Why this answer

Azure AI Health Insights (formerly Text Analytics for Health) is a specialized service designed to extract healthcare-related entities such as diseases, medications, and treatment procedures from clinical text. Azure AI Language (option D) offers general entity extraction but lacks domain-specific models for healthcare. Azure AI Document Intelligence (option B) is optimized for extracting information from structured documents like forms and invoices, not clinical narratives.

Azure AI Search (option C) is the indexing and querying service, not an extraction service. Therefore, Azure AI Health Insights is the appropriate choice to integrate with Azure AI Search for entity extraction from clinical notes.

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147
MCQhard

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

A.The indexer skips the failed documents and continues with the same batch.
B.The indexer stops completely because 3 documents failed.
C.The indexer retries the failed documents.
D.The indexer fails the entire batch but continues with the next batch.
AnswerD

maxFailedItemsPerBatch=2 causes the batch to abort; overall limit of 5 allows subsequent batches.

Why this answer

Azure AI Search indexers use a batch-level failure policy: if the number of failed documents in a batch exceeds the configured 'maxFailedItems' threshold (default 0), the entire batch is marked as failed and skipped, but the indexer continues processing subsequent batches. This behavior is controlled by the indexer's 'maxFailedItems' and 'maxFailedItemsPerBatch' properties, which default to 0, meaning any failure in a batch causes the batch to be skipped while the indexer moves on.

Exam trap

The trap here is that candidates assume individual document failures are silently skipped (Option A) or that any failure stops the entire indexer (Option B), but the actual behavior depends on the configurable 'maxFailedItems' and 'maxFailedItemsPerBatch' thresholds, which cause the batch to fail but allow the indexer to continue if cumulative failures are within limits.

How to eliminate wrong answers

Option A is wrong because the indexer does not skip individual failed documents within a batch when the failure count exceeds the threshold; instead, it fails the entire batch. Option B is wrong because the indexer does not stop completely unless the cumulative failures across all batches exceed the 'maxFailedItems' threshold (default 0), which would stop the entire indexer run, but here only 3 documents fail in one batch, not exceeding the cumulative limit. Option C is wrong because the indexer does not automatically retry failed documents; retry behavior is not part of the default indexer failure handling—failed documents are simply skipped at the batch level.

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148
MCQeasy

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

A.The language detection model misidentified the language
B.You need to set a confidence threshold to exclude negative phrases
C.Key phrase extraction does not consider negation
D.The service is not trained on your specific domain
AnswerC

Key phrase extraction extracts noun phrases without considering negation modifiers.

Why this answer

Key phrase extraction in Azure AI Language Service uses a statistical model that identifies significant terms based on frequency and context, but it does not inherently understand negation. When the phrase 'not good' appears, the model may still extract 'good' as a key phrase because it recognizes 'good' as a high-value term, ignoring the negation. This is a known limitation of the feature, as it focuses on noun phrases and important terms rather than sentiment or negated constructs.

Exam trap

The trap here is that candidates often assume Azure AI Language Service handles negation across all features, but key phrase extraction explicitly does not consider negation, unlike sentiment analysis which does.

How to eliminate wrong answers

Option A is wrong because language detection is a separate step that identifies the language of the text; misidentification would cause incorrect processing but would not specifically cause 'good' to be extracted from 'not good'. Option B is wrong because confidence thresholds filter out low-confidence phrases, not negative phrases; the service does not have a built-in mechanism to exclude negated terms via threshold settings. Option D is wrong because while domain-specific training can improve accuracy, the core issue here is a fundamental limitation of the key phrase extraction model's handling of negation, not a lack of domain adaptation.

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149
MCQmedium

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

A.The output field mapping for 'sentiment' is missing or incorrectly defined in the indexer.
B.The Sentiment skill is not correctly configured in the skillset.
C.The indexer is in a failed state and not processing documents.
D.The sentiment field in the index is of type 'Collection(Edm.String)' but the skill outputs a double.
AnswerA

Without mapping, skill output is not written to index.

Why this answer

The Sentiment skill outputs a 'double' value for sentiment score, but the indexer requires an explicit output field mapping to write that value into the index's 'sentiment' field. Even when a skillset runs successfully, without a correct output field mapping in the indexer definition, the skill's output is not transferred to the index. The indexer's field mappings control how enriched data flows from the skillset's output nodes to the index fields.

Exam trap

The trap here is that candidates assume a successful skillset execution guarantees data is written to the index, but Azure AI Search requires explicit output field mappings in the indexer to bridge skill outputs to index fields, and this step is often overlooked.

How to eliminate wrong answers

Option B is wrong because the question states the skillset runs successfully, meaning the Sentiment skill itself is correctly configured and executed without errors. Option C is wrong because the indexer is explicitly described as running successfully, not in a failed state, so it is processing documents. Option D is wrong because the Sentiment skill outputs a double (a numeric score between 0 and 1), and if the index field were of type 'Collection(Edm.String)', the mismatch would cause an indexer error or warning, but the question says the skillset runs successfully — the issue is the missing mapping, not a type conflict.

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150
MCQhard

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

A.Enable incremental enrichment on the skillset
B.Use the push API to send updates as soon as data changes
C.Use an indexer with a schedule set to run every 5 minutes
D.Enable semantic search to speed up indexing
AnswerB

The push API allows you to add or update documents in the index in real-time.

Why this answer

The push API (Azure Cognitive Search REST API or SDK) allows you to directly upload documents to the index as soon as data changes occur, bypassing the indexer's polling cycle. This provides sub-minute latency, meeting the 15-minute near-real-time requirement. Indexers with schedules or enrichment pipelines introduce inherent delays and are not designed for sub-minute updates.

Exam trap

Microsoft often tests the misconception that indexer schedules can achieve near-real-time indexing, but the trap is that indexers have inherent polling intervals and processing overhead that prevent sub-minute latency, making the push API the only viable option for true near-real-time updates.

How to eliminate wrong answers

Option A is wrong because incremental enrichment only optimizes reprocessing of existing documents in a skillset when a skill changes, not the speed of indexing new or updated data from source changes. Option C is wrong because an indexer scheduled every 5 minutes introduces a minimum 5-minute delay plus processing time, which cannot guarantee updates within 15 minutes if the change occurs just after a run. Option D is wrong because semantic search is a query-time feature that improves relevance ranking, not indexing speed or latency.

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