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CCNA Knowledge Mining Extraction Questions

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1
MCQmedium

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

A.Azure AI Document Intelligence
B.Azure AI Translator
C.Azure AI Language
D.Azure AI Search
AnswerC

Azure AI Language provides entity recognition, key phrase extraction and relation extraction natively, covering all three requirements in one service. Its custom NER and text analytics capabilities process the legal documents directly, satisfying the stem's demand to extract entities, key phrases and relationships without additional services.

Why this answer

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

Exam trap

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

How to eliminate wrong answers

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

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2
MCQmedium

You are building a knowledge mining solution for a legal firm to extract clauses from contracts. The contracts are stored as PDFs in Azure Blob Storage. You need to design the solution to minimize cost while ensuring high accuracy for clause extraction. Which approach should you use?

A.Use Azure AI Custom Vision to detect clause regions in scanned documents.
B.Use Azure OpenAI GPT-4 to process each PDF and extract clauses using prompts.
C.Use Azure AI Search with a blob indexer to extract clauses during indexing.
D.Use Azure AI Document Intelligence with a custom extraction model trained on contract clauses.
AnswerD

A custom Document Intelligence extraction model learns the specific clause layouts and terminology in the firm's contracts, delivering high accuracy. It is consumption-priced per page, avoiding the higher ongoing cost of building and hosting a bespoke machine learning pipeline, satisfying the minimise-cost constraint.

Why this answer

Azure AI Document Intelligence (formerly Form Recognizer) with a custom extraction model is designed to extract structured data from documents like contracts, and it supports training on your own labeled clauses for high accuracy. It is cost-effective for batch processing of PDFs and integrates with Azure AI Search for knowledge mining. This approach balances accuracy and cost better than the alternatives.

Exam trap

AI-102 often tests the service selection trap, where candidates choose Azure OpenAI or Custom Vision for document extraction, but the correct tool for structured clause extraction with cost efficiency is Document Intelligence custom models.

How to eliminate wrong answers

Option A is wrong because Custom Vision is an image classification/object detection service, not designed for text extraction from documents; it would require converting PDFs to images and would not accurately extract clause text. Option B is wrong because using GPT-4 for each PDF is expensive at scale and may not guarantee consistent, structured extraction; it is also overkill for clause extraction when a specialized model exists. Option C is wrong because Azure AI Search with a blob indexer extracts text content but does not perform clause-level extraction or custom entity recognition; it would index the full text, not isolate clauses.

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3
MCQeasy

You are creating an Azure AI Search index that will be populated from an enrichment pipeline. You need to ensure that the original content of each document is searchable. Which index field should you map the document content to?

A.A field with the key attribute set to true
B.A field with the retrievable attribute set to true
C.A field with the searchable attribute set to true
D.A field with the filterable attribute set to true
AnswerC

A searchable field is analyzed and included in the full-text search index. By mapping the document content to a searchable field, you enable users to query the content using keywords. This is the standard way to make document text searchable. The searchable attribute must be set to true for the field to be queried.

Why this answer

To make document content searchable, the index field must have the searchable attribute set to true. This enables full-text search capabilities, allowing users to query the content using keywords and phrases. Other attributes like filterable or retrievable serve different purposes and do not provide search functionality.

Therefore, mapping content to a searchable field is essential.

Exam trap

The trap here is confusing the retrievable attribute with searchability, assuming that if a field is returned in results it must also be searchable, which is not the case.

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4
MCQmedium

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

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

Document Intelligence applies prebuilt invoice models that extract invoice number, date, total amount, and vendor name from scanned PDFs, handling OCR and layout. It directly satisfies the requirement to pull those specific fields from image-based invoices.

Why this answer

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

Exam trap

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

How to eliminate wrong answers

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

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5
Multi-Selectmedium

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

Select 2 answers
A.Merge skill
B.LanguageDetection skill
C.OCR skill
D.EntityRecognition skill
E.KeyPhraseExtraction skill
AnswersC, D

The OCR skill performs optical character recognition on scanned document images, producing text that downstream skills can consume. It satisfies the constraint to extract text from scanned documents, since those files contain no embedded text layer for the indexer to read directly.

Why this answer

The OCR skill (option C) is correct because it is the built-in Azure AI Search cognitive skill that invokes Azure AI Document Intelligence's Read/OCR capability to extract text from scanned documents and images, which is exactly what the scenario requires for scanned input. The EntityRecognition skill (option D) is correct because it is the built-in skill that calls the Text Analytics entity recognition model to identify named entities (people, organizations, locations, dates, and so on) in the extracted text, satisfying the named-entity requirement. The Merge skill (option A) only combines text and offsets from multiple input fields and does not perform OCR or entity extraction, so it is not required here.

The LanguageDetection skill (option B) merely detects the language of the text and is not needed to extract text or identify entities. The KeyPhraseExtraction skill (option E) extracts key phrases rather than named entities, so it does not meet the entity-identification requirement.

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6
Multi-Selecthard

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

Select 3 answers
A.Run a full scan of the Blob Storage to discover and classify data.
B.Create custom classification rules for sensitive data types.
C.Apply sensitivity labels to the classified data.
D.Create a scan rule set that includes the desired classification rules.
E.Register the Azure Blob Storage account as a data source in Purview.
AnswersA, D, E

A full scan is what actually triggers Purview to crawl the registered Blob Storage, apply the chosen scan rule set, and populate the knowledge map with discovered assets and classifications. Without running the scan, no classification occurs, so this action satisfies the requirement for automatic discovery and classification.

Why this answer

Option E is correct because before Microsoft Purview can scan any asset, the Azure Blob Storage account must first be registered as a data source in the Purview governance portal, which establishes the connection and allows the account to be managed and scanned. Option D is correct because a scan rule set defines which file types and classification rules are applied during a scan; you must create or select a rule set that includes the desired system or custom classification rules so sensitive data types are detected. Option A is correct because after registering the source and configuring the rule set, you must run a scan (a full scan for initial discovery and classification) so Purview can crawl the Blob Storage, apply the rule set, and populate the knowledge map with classified assets.

Option B is not required because Purview provides built-in system classification rules for common sensitive data types, and custom rules are only needed for organization-specific patterns, which the scenario does not require. Option C is not part of the scanning and classification configuration; sensitivity labels are applied through Microsoft Purview Information Protection and are a separate labeling concern from the data map scanning process.

Exam trap

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

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7
MCQhard

You are implementing a knowledge mining solution with Azure AI Search that ingests data from Azure Blob Storage. The pipeline includes a custom skill that calls an external API for specialized entity extraction. The custom skill sometimes returns HTTP 429 (Too Many Requests). How should you handle this to ensure reliable indexing?

A.Reduce the batch size in the indexer
B.Increase the skill timeout
C.Configure a retry policy on the custom skill
D.Schedule the indexer to run less frequently
AnswerC

A retry policy on the custom skill handles transient HTTP 429 responses by reissuing the request after a delay, honouring Retry-After where supplied. This keeps indexing reliable without discarding enriched documents, directly addressing the throttling constraint described in the stem.

Why this answer

Configuring a retry policy on the custom skill is the correct way to handle HTTP 429 errors, as it allows the skill to retry the request after a delay, respecting the Retry-After header if provided. This ensures reliable indexing without overwhelming the external API.

Exam trap

AI-102 often tests the misconception that increasing timeout or reducing batch size solves rate limiting, when the correct approach is to implement a retry policy that respects the API's rate limits.

How to eliminate wrong answers

Option A is wrong because reducing batch size may lessen load but does not directly handle 429 errors; it's a mitigation, not a solution. Option B is wrong because increasing the skill timeout does not address rate limiting; the skill would still fail if the API returns 429. Option D is wrong because scheduling the indexer less frequently does not handle transient 429 errors during a run; it only reduces frequency, not the error handling.

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8
MCQmedium

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

A.OCR skill
B.Translation skill
C.Key phrase extraction skill
D.Entity recognition skill
AnswerA

The OCR skill extracts text from embedded images and scanned PDF content within the enrichment pipeline, feeding recognised text downstream for indexing. It directly satisfies the requirement to perform optical character recognition on images embedded in PDFs from SharePoint Online.

Why this answer

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

Exam trap

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

How to eliminate wrong answers

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

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9
MCQeasy

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

A.Custom extraction model
B.Layout model
C.Read model
D.Premade invoice model
AnswerD

The premade invoice model is trained on invoice layout and extracts key-value pairs such as vendor, dates and totals, while accepting PDF and TIFF inputs. Consistent invoice layout matches its domain, avoiding custom model training.

Why this answer

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

Exam trap

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

How to eliminate wrong answers

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

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10
MCQeasy

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

A.Computer Vision
B.Azure AI Language Service
C.Azure AI Document Intelligence
D.Azure AI Speech Service
AnswerB

Azure AI Language Service includes Named Entity Recognition, which identifies dates, locations and organisations in unstructured text out of the box. No training or custom model is required for these built-in entity categories, directly satisfying the stem's extraction requirement.

Why this answer

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

Exam trap

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

How to eliminate wrong answers

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

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11
MCQeasy

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

A.Azure AI Vision's image analysis
B.Azure AI Speech to text
C.Azure Bot Service
D.Azure AI Document Intelligence's OCR capability
AnswerD

Azure AI Document Intelligence's OCR capability is built for handwritten text extraction, using models trained on handwriting rather than printed typefaces. It returns structured fields from scanned images, satisfying the stem's requirement to extract structured data from a large volume of handwritten forms without custom model training.

Why this answer

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

Exam trap

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

How to eliminate wrong answers

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

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12
MCQeasy

A company uses Azure AI Search to index customer support tickets. They need to automatically extract key phrases from each ticket to improve search relevance. Which built-in skill should they add to the skillset?

A.Key Phrase Extraction
B.Entity Recognition
C.Sentiment Analysis
D.OCR
AnswerA

Key Phrase Extraction is a built-in cognitive skill that runs natural language processing over each ticket's text, returning salient terms directly into the index. It satisfies the requirement to extract key phrases automatically without custom code or model training, improving relevance through enriched searchable fields.

Why this answer

The Key Phrase Extraction skill in Azure AI Search is a built-in cognitive skill that uses natural language processing to extract key phrases from text. It is designed exactly for scenarios like extracting important terms from support tickets to improve search relevance and indexing.

Exam trap

AI-102 often tests the difference between similar cognitive skills; the trap is confusing Key Phrase Extraction with Entity Recognition or Sentiment Analysis when the requirement is specifically to extract key phrases.

How to eliminate wrong answers

Option B is wrong because Entity Recognition identifies entities such as people, places, organizations, and dates, not key phrases. Option C is wrong because Sentiment Analysis determines positive, negative, or neutral sentiment, which does not extract key phrases. Option D is wrong because OCR (Optical Character Recognition) extracts text from images, which is irrelevant to extracting key phrases from text tickets.

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13
MCQmedium

You are building an Azure AI Search knowledge mining pipeline that enriches PDF documents with key phrases. The enrichment must be applied after text extraction and before the data is written to the index. You need to ensure the enriched key phrases are available for downstream skills and are mapped to an index field. Which component of the skillset defines the output of the Key Phrase Extraction skill and its mapping to the index?

A.The input parameter of the indexer
B.The context property of the skill
C.The outputFieldMappings of the indexer
D.The outputs array of the skill definition
AnswerD

In an Azure AI Search skillset, each skill has an outputs array that names the output produced by the skill and specifies the target enriched document node. For the Key Phrase Extraction skill, the output is typically named keyPhrases and is mapped to a node like /document/keyPhrases. This definition makes the output available to subsequent skills and to the indexer's outputFieldMappings, which then maps it to an index field. This is the correct place to define the skill's output.

Why this answer

The outputs array within a skill definition is where you declare the name of the output and the enriched document node it targets. This makes the enriched data available to later skills and to the indexer's output field mappings. The indexer's outputFieldMappings then connects that enriched node to a specific index field.

The context property scopes the skill's input, but does not define its output.

Exam trap

The trap here is confusing the skill's output definition with the indexer's output field mappings, which serve different roles in the enrichment pipeline.

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14
MCQhard

Your company is building a knowledge base for customer support using Azure AI Search. You have a large dataset of customer emails stored in Azure Blob Storage. The solution must extract key phrases, detect sentiment, and identify customer intents (e.g., complaint, inquiry, feedback). You plan to use built-in AI skills for key phrase extraction and sentiment detection. For intent identification, you need a custom solution because the intents are specific to your business. You have trained a custom Language Understanding (LUIS) model and published it. How should you integrate the LUIS model into the Azure AI Search enrichment pipeline to extract intents?

A.Add a Document Intelligence skill to classify intents.
B.Configure the index to use a custom analyzer to parse intents.
C.Use the built-in Entity Recognition skill to extract intents.
D.Create a custom skill in the skillset that calls the LUIS endpoint and returns the top intent.
AnswerD

A custom skill in the skillset invokes the published LUIS endpoint, passing enriched text and mapping the returned top intent into the index. This satisfies the requirement for business-specific intent extraction that built-in skills cannot provide.

Why this answer

Azure AI Search enrichment pipelines support built-in skills (key phrase extraction, sentiment, entity recognition, OCR, etc.) and custom skills. A custom skill is a Web API endpoint that the skillset calls during enrichment, receiving a JSON payload and returning enriched fields. To integrate a published LUIS model, you create a custom skill (typically an Azure Function) that calls the LUIS prediction endpoint with the document text and returns the top intent and score, which the indexer then maps into the search index.

Exam trap

AI-102 often tests whether candidates know that built-in skills cover only generic NLP tasks — anything business-specific (custom intents, custom classification) requires a custom skill that wraps an external model endpoint.

How to eliminate wrong answers

Option A is wrong because Document Intelligence (formerly Form Recognizer) extracts structured data from documents (forms, invoices, receipts) — it does not perform intent classification. Option B is wrong because custom analyzers in Azure AI Search control tokenization, stemming, and stop words at query/index time; they have nothing to do with calling an ML model during enrichment. Option C is wrong because the built-in Entity Recognition skill extracts named entities (people, places, organizations) via Cognitive Services — it does not classify intents, which is a distinct NLU task.

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15
MCQmedium

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

A.Use Azure AI Document Intelligence to extract content from HTML, then use Azure AI Language to extract entities and sentiment. Index in Azure AI Search with semantic search.
B.Skillset with Entity Recognition skill, Sentiment skill, Key Phrase Extraction skill, and Text Translation skill. Enable semantic search.
C.Skillset with Entity Recognition skill, Sentiment skill, and Key Phrase Extraction skill. Use Azure OpenAI service to generate summaries via a custom skill that calls the GPT model. Enable semantic search.
D.Skillset with Entity Recognition skill, Sentiment skill, and Text Analytics for Health skill to extract medical terms. Use Azure OpenAI for summarization as a custom skill.
AnswerC

Entity Recognition extracts people and locations, Sentiment scores tone, and Key Phrase Extraction surfaces topics; a custom skill invoking Azure OpenAI generates article summaries. Semantic search then reranks results for relevance, meeting every stated requirement across HTML blob content.

Why this answer

The requirements are topic search, sentiment, key people, locations, and article summaries. Entity Recognition extracts people and locations, Sentiment provides sentiment, Key Phrase Extraction supports topic search, and a custom skill calling Azure OpenAI GPT generates summaries — all wired into an Azure AI Search skillset with semantic search for relevance. This combination directly maps to every requirement without extraneous skills.

Exam trap

AI-102 often tests whether candidates can map each stated requirement to a specific skill — the trap is picking an option with a plausible-sounding but irrelevant skill (Text Translation, Text Analytics for Health, Document Intelligence) while missing a required capability like summarization.

How to eliminate wrong answers

Option A is wrong because Azure AI Document Intelligence is designed for structured document extraction (forms, invoices, PDFs) and is overkill/inefficient for HTML articles; Azure AI Language can extract entities and sentiment but the option omits key phrase extraction and summarization, and Document Intelligence adds cost/latency without benefit for HTML. Option B is wrong because Text Translation is irrelevant — the articles are not stated to be in multiple languages — and the option omits summarization entirely, failing a stated requirement. Option D is wrong because Text Analytics for Health extracts medical terms, which is irrelevant to a news agency, and while it includes summarization, the health skill is a mismatch that wastes resources and could misclassify general news content.

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16
MCQmedium

You are using Azure AI Document Intelligence to process a large batch of PDF forms. The forms have varying layouts and handwriting. You need to extract text and key-value pairs. Which custom model type should you train?

A.Custom template model
B.Prebuilt-layout model
C.Custom neural model
D.Custom composed model
AnswerC

Custom neural models handle unstructured documents with varying layouts and mixed handwriting, generalising from labelled samples across diverse form structures. Custom template models require consistent visual layout, so they fail on the varying layouts described; the neural model extracts text and key-value pairs reliably.

Why this answer

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

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

Therefore, C is the best choice.

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17
MCQeasy

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

A.Review the search service metrics for high query latency and CPU usage.
B.Check the size of the index storage in the Azure portal.
C.Ensure the index schema does not have too many fields.
D.Verify that the skillset is not running during peak hours.
AnswerA

Reviewing service metrics exposes query latency and CPU saturation, the primary indicators of degraded Azure AI Search performance. High CPU usage or elevated query latency directly satisfies the stem's requirement to identify the cause first, since these metrics reveal whether throttling, expensive queries, or insufficient replicas are driving the slow response times.

Why this answer

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

Exam trap

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

How to eliminate wrong answers

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

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18
Multi-Selecteasy

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

Select 2 answers
A.Synonym maps
B.Semantic search
C.Suggesters
D.Scoring profiles
E.Filterable fields
AnswersB, D

Semantic search applies Microsoft's language understanding models to rerank results, promoting passages that are semantically relevant rather than only keyword-matched. This directly improves relevance ranking for users, satisfying the stem's requirement to enhance search result relevance.

Why this answer

Semantic search (B) is correct because it uses Microsoft's language understanding models to rerank results with semantic captions and answers, boosting relevance beyond keyword matching. Scoring profiles (D) are correct because they let you define custom ranking functions (e.g., boosting by freshness, magnitude, or tags) that directly influence the relevance score of returned documents. Synonym maps (A) expand queries with equivalent terms but only broaden matching, not rank relevance.

Suggesters (C) enable type-ahead autocomplete, which improves query input rather than result relevance. Filterable fields (E) restrict the result set with OData filters but do not affect relevance ranking.

Exam trap

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

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19
MCQmedium

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

A.Azure AI Document Intelligence
B.Azure AI Search with semantic search
C.Azure AI Language Service with custom question answering
D.Azure AI Computer Vision
AnswerB

Azure AI Search with semantic search reranks results using language understanding models, returning relevant passages from indexed legal documents. It satisfies the requirement to answer natural-language questions directly from documents while keeping data within Azure.

Why this answer

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

Exam trap

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

How to eliminate wrong answers

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

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20
Multi-Selecteasy

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

Select 3 answers
A.Azure AI Speech
B.Azure AI Search
C.Azure AI Document Intelligence layout model
D.Azure AI Vision OCR
E.Azure AI Language custom NER
AnswersB, C, D

Azure AI Search supports OCR enrichment through its built-in cognitive skills, extracting text from image content during indexing. This satisfies the requirement for a service that pulls text out of images, alongside Vision and Document Intelligence.

Why this answer

Option B (Azure AI Search) is correct because it includes AI enrichment with the OCR cognitive skill, which extracts text from image files (e.g., JPEG, PNG) during the indexing pipeline, making the text searchable. Option C (Azure AI Document Intelligence layout model) is correct because the layout model performs OCR on documents and images, extracting printed and handwritten text along with tables and structure from forms and files. Option D (Azure AI Vision OCR) is correct because the Read/OCR API in Azure AI Vision extracts printed and handwritten text from images and documents.

Option A (Azure AI Speech) is incorrect because it handles speech-to-text, text-to-speech, and translation of audio, not text extraction from images. Option E (Azure AI Language custom NER) is incorrect because it extracts named entities from existing text, not text from images.

Exam trap

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

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21
MCQmedium

You are building an Azure AI Search enrichment pipeline that must extract text and layout information from scanned PDFs stored in Azure Blob Storage. The extracted content must include bounding boxes for each text line so that a downstream custom skill can associate key-value pairs spatially. You need to add a built-in skill to the skillset to perform this extraction. Which skill should you add?

A.DocumentExtractionSkill
B.TextMergeSkill
C.OcrSkill
D.DocumentIntelligenceLayoutSkill
AnswerD

DocumentIntelligenceLayoutSkill is a built-in Azure AI Search skill that leverages Azure AI Document Intelligence to extract text, layout, and structure from documents, including scanned PDFs. It returns bounding boxes for text lines and other layout elements, enabling spatial association in downstream skills. This skill is specifically designed for document layout analysis, making it the correct choice to meet the requirement for bounding box coordinates.

Why this answer

The requirement is to extract text and layout information, including bounding boxes, from scanned PDFs. DocumentIntelligenceLayoutSkill is a built-in Azure AI Search skill that uses Azure AI Document Intelligence to analyze document structure and return bounding boxes for text lines. This enables downstream spatial association.

Other skills either do not provide layout data or are not designed for direct PDF analysis, so they fail to meet the spatial requirement.

Exam trap

The trap here is assuming that any text extraction skill returns layout coordinates, when in fact only DocumentIntelligenceLayoutSkill provides bounding boxes for scanned PDFs.

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22
MCQeasy

You are creating an Azure AI Search solution that must extract named entities such as people, organizations, and locations from text documents. You want to use a built-in cognitive skill to perform this extraction during indexing. Which skill should you add to the skillset?

A.KeyPhraseExtractionSkill
B.LanguageDetectionSkill
C.EntityRecognitionSkill
D.TextTranslationSkill
AnswerC

EntityRecognitionSkill is a built-in cognitive skill in Azure AI Search that extracts entities such as persons, organizations, and locations from text. It is specifically designed for this purpose and integrates directly into the enrichment pipeline. Adding it to the skillset will produce enriched output that can be mapped to index fields, meeting the requirement without custom code.

Why this answer

EntityRecognitionSkill is the built-in Azure AI Search skill that extracts named entities and classifies them into categories such as persons, organizations, and locations. It is part of the cognitive skills library and is designed to enrich documents during indexing. The other skills perform different natural language processing tasks such as key phrase extraction, language detection, or translation, none of which provide categorized entity extraction.

Exam trap

The trap here is confusing entity extraction with key phrase extraction, which also analyzes text but does not categorize entities into types like people or organizations.

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23
MCQmedium

You are building an Azure AI Search solution that enriches documents by detecting the language of each document and then routing content to language-specific analyzers. You add a LanguageDetectionSkill to the skillset and want the detected language code to be available to downstream skills and to be stored in the index. The detected language must be mapped to a field named 'languageCode' in the index. What should you do?

A.Configure the LanguageDetectionSkill with a 'defaultLanguageCode' parameter and set the index field 'languageCode' to use the 'fr.lucene' analyzer.
B.Create a custom skill that calls the Azure AI Language service and writes the detected language directly into the index using the Azure AI Search REST API.
C.Set the 'languageCode' field in the index to be retrievable and filterable, and rely on the indexer to automatically populate it from the skill output.
D.Add an outputFieldMapping in the indexer that maps the '/document/languageCode' enrichment node to the 'languageCode' index field.
AnswerD

Output field mappings in the indexer explicitly connect enriched document nodes to index fields. The LanguageDetectionSkill emits a 'languageCode' value under /document, and an outputFieldMapping with sourceFieldName '/document/languageCode' and targetFieldName 'languageCode' persists it. This is the supported mechanism for projecting skill output into the search index.

Why this answer

The LanguageDetectionSkill outputs a language code under the enriched document, but that value only reaches the index if the indexer maps it. Output field mappings declare which enrichment nodes become index field values. Configuring analyzers or field attributes changes how data is stored or queried, not whether it is stored.

A custom skill is unnecessary because the built-in skill already emits the required value.

Exam trap

The trap here is assuming that enabling a skill and adding a matching index field is enough, when the indexer still needs an explicit output field mapping to persist the enriched value.

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24
Multi-Selectmedium

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

Select 2 answers
A.Use the Push API to send data directly to the search index.
B.Use Azure Data Factory to copy data to Blob Storage, then index from Blob.
C.Use Azure AI Document Intelligence to extract data and push to index.
D.Use Azure Event Hubs to stream data into the search index.
E.Use the Azure AI Search SQL Server indexer.
AnswersA, E

The Push API lets your code serialise SQL rows into JSON documents and POST them to the index, giving full control over transformation and scheduling. It works without an indexer, satisfying the requirement for a valid SQL-to-search ingestion path.

Why this answer

Option A is correct because the Push API (the REST/SDK endpoint that accepts JSON documents directly into an index) lets an application read rows from Azure SQL Database and send them straight to Azure AI Search, bypassing any intermediate storage. Option E is correct because Azure AI Search provides a built-in Azure SQL indexer (created via the portal, REST, or the Azure.Search.Documents SDK) that connects to Azure SQL Database using a change-tracking column (or high-water mark) to pull and index rows on a schedule. Option B is not a direct indexing method for Azure SQL Database; it inserts an unnecessary Data Factory copy to Blob Storage and then relies on a Blob indexer, which is a different data source.

Option C is wrong because Azure AI Document Intelligence extracts text from documents (PDFs, images, forms), not from relational Azure SQL tables. Option D is wrong because Event Hubs is a streaming ingestion service and Azure AI Search has no Event Hubs indexer; streaming into the index would still require the Push API.

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25
MCQmedium

You are configuring an Azure AI Search indexer to process documents from Azure Blob Storage. The documents include PDFs and Microsoft Word files. You need to extract both text and metadata such as author and creation date. Which indexer configuration should you use?

A.Set the parsingMode to json
B.Set the parsingMode to default
C.Set the parsingMode to delimitedText
D.Set the parsingMode to text
AnswerB

The default parsingMode uses the built-in document cracking capabilities to extract text and metadata from various file formats, including PDF and Microsoft Office files. It automatically detects the file type and uses the appropriate extractor. This mode is designed for exactly this scenario, where you need to process multiple document types and extract both content and metadata fields like author and creation date.

Why this answer

The default parsingMode in Azure AI Search indexers is designed to handle a variety of document formats, including PDF and Microsoft Office files. It uses built-in document cracking to extract text and metadata, such as author and creation date, which are then available for mapping to index fields. Other parsing modes are specialized for JSON, delimited text, or plain text and do not support rich document extraction.

Exam trap

The trap here is assuming that a specific parsing mode like text is needed for text extraction, when the default mode already handles common document formats with metadata.

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26
MCQeasy

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

A.Use Azure AI Document Intelligence to extract QnA pairs.
B.Create a custom question answering project and import the Word document as a source.
C.Use the prebuilt question answering API to parse the document.
D.Use conversational language understanding (CLU) to extract intents and entities.
AnswerB

Custom question answering ingests FAQ documents directly, extracting question-and-answer pairs automatically, so the Word file becomes a project source without manual reformatting. This satisfies the requirement to import existing FAQs into the project rather than authoring them by hand.

Why this answer

Azure AI Language's custom question answering feature allows you to create a project and import a Word document directly as a source, automatically extracting question-and-answer pairs. This is the intended approach for ingesting FAQ documents into a knowledge base.

Exam trap

AI-102 often tests the confusion between Document Intelligence (for form and document extraction) and custom question answering (for FAQ knowledge bases), leading candidates to pick Document Intelligence for QnA import.

How to eliminate wrong answers

Option A is wrong because Azure AI Document Intelligence is for extracting text and structure from documents, not for directly creating QnA pairs in a question answering project. Option C is wrong because the prebuilt question answering API is for querying an existing knowledge base, not for parsing and importing documents. Option D is wrong because Conversational Language Understanding (CLU) is for intent and entity extraction in conversational apps, not for importing FAQ documents into a QnA project.

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27
MCQeasy

You are creating an Azure AI Search index that will store documents enriched with key phrases and sentiment scores. You need to define the index fields to store these enriched values. The key phrases should be searchable and retrievable, and the sentiment score should be filterable and sortable. Which field definitions should you use?

A.keyPhrases: Collection(Edm.String) with searchable=true, filterable=true; sentimentScore: Edm.Double with filterable=true, sortable=true.
B.keyPhrases: Collection(Edm.String) with searchable=true, retrievable=true; sentimentScore: Edm.Double with filterable=true, sortable=true, retrievable=true.
C.keyPhrases: Edm.String with searchable=true, retrievable=true; sentimentScore: Edm.Int32 with filterable=true, sortable=true, retrievable=true.
D.keyPhrases: Collection(Edm.String) with filterable=true, retrievable=true; sentimentScore: Edm.Double with searchable=true, retrievable=true.
AnswerB

Key phrases are typically a collection of strings, so Collection(Edm.String) is appropriate. Marking it searchable and retrievable allows full-text search and retrieval in results. Sentiment scores are numeric, so Edm.Double works, and marking it filterable and sortable enables filtering and sorting. This definition meets all requirements.

Why this answer

Key phrases are a collection of strings, so Collection(Edm.String) is correct. They must be searchable and retrievable to enable full-text search and to return them in results. Sentiment score is a numeric value, so Edm.Double is suitable.

It must be filterable and sortable, and retrievable to be included in results. The combination of these attributes satisfies the scenario's requirements.

Exam trap

The trap here is overlooking the retrievable attribute for key phrases or choosing an incorrect data type for sentiment score.

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28
MCQmedium

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

A.Azure AI Search with semantic ranking
B.Azure AI Translator with dictionary lookup
C.Azure AI Document Intelligence custom extraction model
D.Azure AI Language healthcare entity recognition and relation extraction
AnswerD

Healthcare entity recognition extracts drugs, diseases and genes as typed entities, and relation extraction links them, producing exactly the drug-disease-gene relationships the knowledge graph requires. General entity extraction cannot capture these biomedical relation types, so it fails the graph-querying requirement.

Why this answer

Azure AI Language's healthcare entity recognition and relation extraction is specifically designed to extract medical entities (drugs, diseases, genes) and their relationships from unstructured text, making it ideal for building a knowledge graph. This pre-built model uses deep learning trained on biomedical literature, directly supporting the required extraction without custom training.

Exam trap

The trap here is that candidates confuse general-purpose text extraction (Azure AI Document Intelligence) or search (Azure AI Search) with domain-specific biomedical entity and relation extraction, which requires a specialized healthcare NLP model like Azure AI Language's healthcare feature.

How to eliminate wrong answers

Option A is wrong because Azure AI Search with semantic ranking is a search and ranking service, not an extraction service; it cannot extract entities or relationships from text. Option B is wrong because Azure AI Translator with dictionary lookup performs language translation and word-level lookup, not structured entity or relation extraction from scientific articles. Option C is wrong because Azure AI Document Intelligence custom extraction model is designed for extracting fields from forms and documents (e.g., invoices, receipts), not for complex biomedical entity and relation extraction from unstructured narrative text.

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29
MCQhard

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

A.Use the push API to index all data from a custom application that polls all sources.
B.Create a single indexer that reads from all three sources using a data source definition.
C.Use only indexers for all sources by creating a custom indexer for the REST API.
D.Configure indexers for SQL and SharePoint, and use the push API for the REST API. Schedule indexers to run every 5 minutes.
AnswerD

Native indexers cover SQL Database and SharePoint Online, while the push API handles the custom REST source that has no indexer. Scheduling indexers every 5 minutes meets the freshness requirement, and index fields marked filterable and facetable support faceted navigation.

Why this answer

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

Exam trap

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

How to eliminate wrong answers

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

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30
Multi-Selecteasy

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

Select 3 answers
A.A database to store intermediate results.
B.A Power Automate flow to orchestrate the skill.
C.A web API endpoint that accepts JSON input and returns JSON output.
D.An HTTPS endpoint for the API.
E.A JSON schema defining inputs and outputs.
AnswersC, D, E

A custom skill is invoked by the enrichment pipeline as a web API call, so it must expose an endpoint accepting a JSON request body and returning a JSON response containing the enriched values the skillset consumes.

Why this answer

To build a custom skill for Azure AI Search enrichment, you must expose your logic through a web API endpoint that accepts JSON input and returns JSON output (C), because the skillset invokes the skill via an HTTP POST with a JSON payload and expects a JSON response. The endpoint must be secured with HTTPS (D), since Azure AI Search requires custom skill connections to use HTTPS for secure transport. You also need a JSON schema defining inputs and outputs (E), which describes the expected input fields and output fields so the skillset can map enriched document content correctly.

A database for intermediate results (A) is not required, as the skillset pipeline passes data between skills in memory, and a Power Automate flow (B) is not a supported orchestration mechanism for custom skills.

Exam trap

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

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31
Multi-Selectmedium

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

Select 2 answers
A.Entity Recognition skill
B.Image Analysis skill
C.OCR skill
D.Key Phrase Extraction skill
E.Text Translation skill
AnswersB, C

The Image Analysis skill extracts visual features and generates descriptions or tags from image content, and can also produce text via its OCR capability. It satisfies the requirement to extract information from images embedded within documents during AI enrichment.

Why this answer

The Image Analysis skill (B) is correct because it invokes the Computer Vision service to extract visual features from embedded images, such as descriptions, tags, and celebrity or landmark detection, which is exactly the kind of image-derived information a knowledge mining pipeline needs. The OCR skill (C) is also correct because it extracts printed and handwritten text from image files (including images embedded in documents), producing text that can be mapped into the search index. The Entity Recognition skill (A) operates on text to identify entities like people, places, and organizations, not on image content.

The Key Phrase Extraction skill (D) analyzes text to surface salient phrases and does not process images. The Text Translation skill (E) translates text between languages and likewise does not extract information from images.

Exam trap

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

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32
MCQmedium

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

A.Use Azure AI Language key phrase extraction, and enable vector search
B.Use Azure AI Document Intelligence to extract entities, and enable semantic ranking
C.Use Azure AI Language entity extraction as a custom skill, and enable fuzzy search in the index
D.Use Azure AI Vision OCR to extract text, and enable synonyms
AnswerC

Azure AI Language entity extraction runs as a custom skill during indexing, pulling colour, size and brand from product descriptions into filterable index fields. Enabling fuzzy search on those fields handles misspellings, directly satisfying the natural-language filtering and typo-tolerance requirements.

Why this answer

Azure AI Language entity extraction as a custom skill in an Azure AI Search skillset extracts entities such as color, size, and brand from product descriptions, which can then be mapped to filterable index fields. Enabling fuzzy search (via the Lucene query syntax or the built-in fuzzy matching in the query parser) supports misspelled queries. This combination directly satisfies the requirement to extract entities for filtering and to support fuzzy matching.

Exam trap

AI-102 often tests the confusion between key phrase extraction and entity extraction, and between semantic ranking and fuzzy matching, causing candidates to select key phrase extraction or semantic ranking when the requirement explicitly calls for typed entities and misspelling tolerance.

How to eliminate wrong answers

Option A is wrong because key phrase extraction returns key phrases, not typed entities like color, size, or brand, so it cannot populate filterable entity fields; vector search addresses semantic similarity, not entity extraction or fuzzy matching. Option B is wrong because Azure AI Document Intelligence is designed for OCR and form/document structure extraction, not for entity extraction from short product descriptions, and semantic ranking improves relevance ranking rather than enabling fuzzy matching. Option D is wrong because Azure AI Vision OCR extracts text from images, which is irrelevant to text-based product descriptions, and synonyms expand query terms but do not provide fuzzy matching for misspellings.

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33
MCQmedium

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

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

Azure AI Document Intelligence provides prebuilt and custom invoice models that extract line items such as product names, quantities and prices from varied layouts, then output structured JSON suitable for loading into Azure SQL Database.

Why this answer

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

Exam trap

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

How to eliminate wrong answers

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

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34
MCQhard

You are designing a knowledge mining solution that must extract tables from scanned invoices stored in Azure Blob Storage and make the table cells searchable. The invoices are in PDF and JPEG formats. Which Azure AI service should you use to extract the tables before loading the data into Azure AI Search?

A.Azure AI Language
B.Azure AI Search OCR skill
C.Azure AI Vision Image Analysis
D.Azure AI Document Intelligence
AnswerD

Azure AI Document Intelligence is designed to extract structured data from documents, including tables with row and column relationships, using prebuilt or custom models. It supports PDF and image inputs such as JPEG, making it suitable for scanned invoices. Its output can be transformed and loaded into Azure AI Search for searchable table content.

Why this answer

Azure AI Document Intelligence provides prebuilt and custom models that detect tables and return structured cell data with row and column indices. It accepts PDF and image inputs, which matches the scanned invoice formats. The extracted table structure can then be shaped and indexed into Azure AI Search so that table cells are searchable.

Exam trap

The trap here is assuming the Azure AI Search OCR skill can extract tables, when it only produces unstructured text and loses the row and column relationships.

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35
MCQeasy

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

A.Named Entity Recognition (NER)
B.Sentiment analysis with opinion mining
C.Entity linking
D.Key phrase extraction
AnswerB

Opinion mining extends sentiment analysis by associating each detected entity with its own sentiment and the specific words expressing it, rather than returning one document-level score. This directly satisfies the requirement to identify sentiment towards specific entities in the feedback.

Why this answer

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

Exam trap

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

How to eliminate wrong answers

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

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36
MCQmedium

You are building an Azure AI Search enrichment pipeline that extracts key phrases from documents stored in Azure Blob Storage. The documents are plain text files in English. You need to add a built-in skill that identifies the main concepts in each document without writing custom code. Which skill should you use?

A.Entity Recognition skill
B.Key Phrase Extraction skill
C.Language Detection skill
D.Text Translation skill
AnswerB

The Key Phrase Extraction skill is a built-in cognitive skill in Azure AI Search that uses natural language processing to identify the main points in text. It is designed for exactly this scenario: extracting key phrases from unstructured English text without custom code. It outputs a list of key phrases that can be mapped to an index field, enabling users to search or filter by important concepts.

Why this answer

The Key Phrase Extraction skill is the correct choice because it is a built-in Azure AI Search skill that analyzes text and returns a list of key phrases representing the main concepts. It requires no custom code and integrates directly into a skillset. The other skills serve different purposes: entity recognition focuses on named entities, language detection identifies language, and translation converts languages.

Exam trap

The trap here is confusing general key phrase extraction with named entity recognition, assuming that extracting entities will also capture the main concepts of a document.

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37
MCQhard

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

A.Create a tagging scoring profile that boosts by the title field with a weight of 10.
B.Create a freshness scoring profile with a boosting duration of 30 days.
C.Create a distance scoring profile with a reference point parameter.
D.Create a magnitude scoring profile with a boosting function of 'linear'.
AnswerA

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

Why this answer

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

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

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38
MCQeasy

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

A.Set the 'filterable' property to true on the category field
B.Set the 'facetable' property to true on the category field
C.Set the 'searchable' property to true on the category field
D.Set the 'sortable' property to true on the category field
AnswerA

Filtering in Azure AI Search requires the field to be marked filterable in the index definition. Setting filterable to true on the category field enables OData filter expressions alongside term searches, satisfying the stated requirement.

Why this answer

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

Exam trap

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

How to eliminate wrong answers

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

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39
MCQmedium

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

A.Run 'az search indexer run --name myindexer' to trigger a new run.
B.Run 'az search indexer show --name myindexer' and review the 'warnings' array in the output.
C.Ignore the warnings because they are not errors.
D.Run 'az search indexer reset --name myindexer' to reset the indexer.
AnswerB

Indexer list output omits warning detail. Running az search indexer show for that indexer returns the full execution history, including the warnings array, which names each affected document and the underlying skill or mapping issue.

Why this answer

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

Exam trap

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

How to eliminate wrong answers

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

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40
MCQmedium

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

A.Named Entity Recognition skill
B.Language Detection skill
C.Optical Character Recognition (OCR) skill
D.Key Phrase Extraction skill
AnswerA

The Named Entity Recognition skill calls Azure AI Language to extract persons, organisations and dates from document text, writing them into the index as enriched fields. This directly satisfies the requirement to index those entity types from PDFs.

Why this answer

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

Exam trap

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

How to eliminate wrong answers

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

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41
MCQmedium

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

A.Azure AI Document Intelligence's pre-built invoice model and Azure Bot Service
B.Azure AI Document Intelligence's custom extraction model and Azure AI Language's custom text classification
C.Azure AI Search's built-in OCR skill and a custom skill using Azure Functions
D.Azure AI Language's pre-built entity extraction and Azure AI Search
AnswerB

Document Intelligence's custom extraction model handles domain-specific fields such as authors, dates and references, while Azure AI Language's custom text classification assigns topics using labelled training data. Together they satisfy the requirement for custom models on domain-specific research papers.

Why this answer

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

Exam trap

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

How to eliminate wrong answers

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

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42
MCQhard

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

A.Custom Entity Lookup skill and Sentiment skill
B.PII detection skill
C.Text Translation skill and Entity Recognition skill
D.Entity Recognition skill and Key Phrase Extraction skill
AnswerB

The PII detection skill calls Azure AI Language's entity recognition to identify and mask personal data such as names, addresses, and identifiers during skillset enrichment. Running it before indexing redacts sensitive patient information, satisfying the de-identification requirement.

Why this answer

The PII detection skill in Azure AI Services is purpose-built to identify and redact personal information such as names, addresses, phone numbers, and medical record identifiers. For de-identifying medical research papers before indexing in Azure AI Search, this skill directly satisfies the requirement by detecting and masking PII during the enrichment pipeline. The other skill combinations address sentiment, translation, entity recognition, or key phrases, none of which perform de-identification.

Exam trap

AI-102 often tests whether candidates confuse entity recognition (which extracts but does not redact) with PII detection (which detects and can mask) — the trap is picking Entity Recognition because it sounds like it handles personal data.

How to eliminate wrong answers

Option A is wrong because Custom Entity Lookup requires you to define entities manually and Sentiment analyzes tone — neither detects or redacts PII automatically. Option C is wrong because Text Translation converts languages and Entity Recognition extracts named entities (people, places, organizations) but does not redact PII. Option D is wrong because Entity Recognition plus Key Phrase Extraction identifies entities and topics but leaves sensitive data intact in the index.

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43
Multi-Selecthard

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

Select 3 answers
A.The API endpoint must be reachable from the search service
B.The skill can only accept one input and produce one output
C.The skill must be written in Python
D.The skill must handle payloads up to 16 MB
E.The skill must complete within 230 seconds
AnswersA, D, E

The search service invokes the custom skill over HTTP during enrichment, so the external API endpoint must be reachable from the service's network path. This satisfies the stem's design consideration, since an unreachable endpoint causes skill execution failures and incomplete indexing.

Why this answer

Option A is correct because a custom skill in Azure AI Search is essentially a Web API skill that the indexer invokes over HTTPS, so the external API endpoint must be network-reachable from the search service (including any required private endpoint or firewall rules) or enrichment will fail. Option D is correct because the Web API skill has a documented maximum request payload size of 16 MB; larger payloads will be rejected, so the skill design must keep the serialized input within that limit. Option E is correct because the Web API skill enforces a maximum execution time of 230 seconds per call, after which the request times out, so long-running extraction logic must be optimized or split.

Option B is wrong because a custom skill can accept multiple inputs and produce multiple outputs via the inputs and outputs mappings. Option C is wrong because the skill can be implemented in any language or framework (for example C#, Node.js, or Java) as long as it exposes a compatible HTTP endpoint.

Exam trap

AI-102 often tests the specific numeric limits (16 MB, 230 seconds) and the misconception that custom skills are restricted to a single input/output or a specific programming language.

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44
MCQmedium

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

A.The first document has a higher value in a scoring profile field
B.The first document is more similar to the query in vector space
C.The first document was boosted by a semantic ranking function
D.The first document has a higher term frequency and better term proximity for the query terms
AnswerD

Azure AI Search's BM25 scoring rewards documents where query terms appear more often and closer together. The first document contains 'brown' and 'fox' repeatedly and adjacent, so its term frequency and proximity boosts outweigh the second document's weaker matches.

Why this answer

The first document is scored higher because it has a higher term frequency and better term proximity for the query terms 'brown fox'. In Azure AI Search's default scoring profile, documents that contain the query terms more frequently and closer together receive higher relevance scores.

Exam trap

AI-102 often tests the difference between default scoring and optional features like semantic ranking or vector search — candidates may assume advanced features are always active, but the default behavior is based on term frequency and proximity.

How to eliminate wrong answers

Option A is wrong because a scoring profile field boost would only apply if a custom scoring profile was used and the field was boosted; the question does not indicate that. Option B is wrong because vector similarity applies to vector search, not the default keyword search. Option C is wrong because semantic ranking is an optional feature that re-ranks results using semantic understanding, but the question does not specify it was enabled.

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45
MCQeasy

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

A.Azure Bot Service
B.Azure AI Document Intelligence
C.Azure AI Search with built-in skills
D.Azure AI Foundry model catalog
AnswerB

Azure AI Document Intelligence provides prebuilt invoice models that extract invoice numbers, dates and totals from scanned documents, and its read model handles OCR. It supports scheduled, unattended runs via the API, satisfying the no-manual-intervention constraint.

Why this answer

Azure AI Document Intelligence (formerly Form Recognizer) provides prebuilt and custom models specifically designed to extract structured fields like invoice numbers, dates, and totals from scanned documents, including the prebuilt invoice model. It can be invoked programmatically on a schedule via Azure Functions, Logic Apps, or Data Factory, satisfying the no-manual-intervention requirement. This is the canonical Azure service for OCR-plus-field-extraction from forms and invoices.

Exam trap

AI-102 often tests the distinction between Document Intelligence (structured field extraction from forms/invoices) and Azure AI Search with skills (indexing/search enrichment) — candidates who see 'search through a collection' may incorrectly pick AI Search, missing that the requirement is field extraction, not retrieval.

How to eliminate wrong answers

Option A is wrong because Azure Bot Service is for building conversational AI interfaces (chatbots), not for document field extraction or scheduled batch processing of invoices. Option C is wrong because Azure AI Search with built-in skills is for indexing and searching content (including OCR enrichment via cognitive skills), but it does not natively extract structured invoice fields like invoice number, date, and total with the accuracy of a purpose-built document model — it is a search/indexing layer, not a document understanding service. Option D is wrong because Azure AI Foundry model catalog is a marketplace of foundation models (LLMs, embeddings, etc.) for generative and predictive workloads; it does not provide invoice-specific field extraction out of the box and would require significant custom prompt engineering without the structured accuracy of Document Intelligence.

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46
MCQmedium

You have an Azure AI Search indexer that enriches documents with a custom skill that calls an external API. The custom skill returns a JSON object containing a list of product codes. You need to store these product codes in a collection field named 'productCodes' in the index, and you want the field to be searchable and filterable. What should you do?

A.In the index definition, set the 'productCodes' field to type Edm.String and mark it as searchable and filterable. Then in the indexer, map the custom skill output to this field using an output field mapping.
B.In the index definition, set the 'productCodes' field to type Collection(Edm.String) and mark it as retrievable only. Then in the indexer, map the custom skill output using a field mapping.
C.In the index definition, set the 'productCodes' field to type Collection(Edm.String) and mark it as searchable and filterable. Then in the indexer, map the custom skill output to this field using an output field mapping.
D.In the index definition, set the 'productCodes' field to type Collection(Edm.String) and mark it as filterable and facetable. Then in the indexer, use a field mapping to map the custom skill output.
AnswerC

Collection(Edm.String) is the correct field type for storing multiple string values. Marking it searchable and filterable enables full-text search and filtering on individual codes. An output field mapping connects the custom skill's JSON array to the index field. This combination correctly stores and exposes the product codes for search and filter operations.

Why this answer

To store multiple product codes from a custom skill, the index field must be a collection of strings. Collection(Edm.String) supports multiple values and can be marked searchable and filterable to meet query requirements. The custom skill's output is part of the enriched document, so an output field mapping is the correct mechanism to project it into the index field.

Using a single string field or omitting searchable would not satisfy the scenario.

Exam trap

The trap here is using a field mapping instead of an output field mapping for enriched skill output, and forgetting that collection types are needed for multiple values.

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47
MCQmedium

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

A.Prebuilt Named Entity Recognition (NER)
B.Custom Named Entity Recognition (NER)
C.PII detection
D.Entity linking
AnswerB

Custom NER trains on your labelled medical examples to extract domain-specific entities such as dosage, side effects and contraindications. The prebuilt healthcare model covers general entities, but custom NER is required for these bespoke categories.

Why this answer

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

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

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48
MCQeasy

Refer to the exhibit. You have this Azure AI Search indexer configuration. The indexer is failing after processing 6 documents that contain errors. What should you do to ensure the indexer continues processing even if some documents fail?

A.Decrease the batch size to 5
B.Increase batch size to 20
C.Increase maxFailedItems to a higher value, such as 100
D.Remove the schedule to run the indexer on demand
AnswerC

Raising maxFailedItems lets the indexer tolerate more per-document failures before halting, directly satisfying the stem's requirement to continue despite errors. The default of 0 stops execution after the first failure; setting 100 allows the remaining documents to be processed while failed ones are skipped.

Why this answer

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

Exam trap

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

How to eliminate wrong answers

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

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49
MCQmedium

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

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

Azure AI Document Intelligence applies prebuilt and custom extraction models to scanned PDFs, returning structured fields such as parties, dates and clauses. Its OCR plus layout-aware contract model satisfies the requirement to mine thousands of scanned contracts, which generic vision or language services cannot parse reliably.

Why this answer

Azure AI Document Intelligence (formerly Form Recognizer) is purpose-built for extracting structured data — key-value pairs, entities, tables, and clauses — from scanned documents and PDFs. Its prebuilt and custom models (including the prebuilt contract model) can identify parties, dates, and clauses, making it the correct primary component for contract clause extraction.

Exam trap

AI-102 often tests the confusion between Document Intelligence (document extraction) and Azure AI Language (text analytics) — candidates pick Language because it sounds like it handles 'clauses,' but it cannot parse scanned document layout.

How to eliminate wrong answers

Option B is wrong because Azure AI Vision handles image analysis (OCR, object detection, facial recognition) but does not extract semantic contract clauses or structured fields from documents. Option C is wrong because Azure AI Language handles text analytics (sentiment, NER, key phrases) on already-extracted text — it lacks document layout understanding and OCR for scanned PDFs. Option D is wrong because Azure AI Search is the indexing/query layer that consumes enriched content; it is not the extraction engine itself, though it is often paired with Document Intelligence in a knowledge mining pipeline.

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50
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

Document Intelligence's built-in skill only supports its documented formats, so EPUB is skipped. A custom skill converts EPUB to plain text before enrichment, letting the remaining skillset extract authors, dates and chapter titles from every manuscript format stored in Blob Storage.

Why this answer

Azure AI Document Intelligence does not natively support EPUB files, so the built-in Document Intelligence skill cannot process them. To include EPUB files in the enrichment pipeline, you must create a custom skill that converts EPUB to plain text (or another supported format) and then add it to the skillset. This allows the pipeline to handle EPUB content alongside PDFs and Word documents.

Exam trap

AI-102 often tests the assumption that Document Intelligence supports all document formats, but it does not support EPUB, leading candidates to incorrectly choose option A or C.

How to eliminate wrong answers

Option A is wrong because Azure AI Document Intelligence does not support EPUB as an input format; it supports PDF, images, Office, and HTML, but not EPUB. Option C is wrong because you cannot modify the Document Intelligence skill to accept EPUB; the skill's capabilities are fixed by the service. Option D is wrong because Azure AI Search does not allow registering new data source types; data sources are limited to supported types like Azure Blob Storage, and the issue is not the data source but the file format processing.

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51
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

The Read API performs OCR and extracts printed and handwritten text, including French, from scanned documents without needing language-specific training. It satisfies the stem's requirement for extracting text from scanned French documents, unlike custom or prebuilt models that target structured fields.

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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52
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

The Key Phrase Extraction skill invokes the Key Phrase Extraction cognitive service during skillset execution, returning a list of key phrases per document into the enrichment tree. This satisfies the stem's requirement to extract key phrases automatically at index time, without custom code or separate processing pipelines.

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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53
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 combines OCR with prebuilt invoice models that extract structured fields such as vendor, total and due date, and its read model handles handwritten text, satisfying the mixed printed and handwritten PDF requirement in one service.

Why this answer

Azure AI Document Intelligence (formerly Form Recognizer) is purpose-built for extracting structured fields from documents like invoices, receipts, and IDs. Its prebuilt invoice model returns vendor, invoice number, line items, totals, and dates, and its custom/neural models handle both printed and handwritten text. It also natively ingests PDFs, making it the correct choice for scanned invoices with mixed print/handwriting.

Exam trap

AI-102 often tests whether candidates confuse Computer Vision's generic OCR with Document Intelligence's field-aware extraction — the trap is picking Computer Vision because it 'reads documents,' missing that invoices need structured field models.

How to eliminate wrong answers

Option A is wrong because the Language Service handles text analytics (sentiment, entities, key phrases, translation) on already-extracted text — it does not read PDFs or perform OCR/layout extraction. Option B is wrong because Speech Service is for audio transcription and text-to-speech, unrelated to document images. Option C is wrong because Computer Vision offers OCR (Read API) but does not provide invoice-specific field extraction, layout-aware key-value pairing, or prebuilt invoice models — it returns raw text, not structured invoice fields.

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54
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

Azure AI Search indexes and retrieves relevant manual passages, while Azure OpenAI generates grounded natural-language answers from those retrieved chunks. This combination satisfies the requirement to answer questions about PDF manual content without training a custom model.

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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55
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 outputs structured Markdown and JSON that preserves table rows, columns and headings, unlike plain OCR which flattens them. Wrapping it as a custom skill lets the indexer enrich each PDF, Word or HTML document, satisfying the requirement to retain table structure in the searchable index.

Why this answer

The Azure AI Document Intelligence layout model is purpose-built to extract text, tables, and structure from documents in PDF, Word, and HTML formats, and it preserves table structure and reading order. When integrated as a custom skill in an Azure AI Search skillset, it enriches the pipeline with structured content that can be indexed and searched. This directly satisfies the requirement to handle tables while preserving their structure.

Exam trap

AI-102 often tests the distinction between OCR (text extraction from images) and document layout analysis (structure extraction), causing candidates to pick the Vision OCR skill when tables and structure are required.

How to eliminate wrong answers

Option A is wrong because uploading documents directly to Azure AI Search only performs basic text extraction and does not parse complex tables or preserve their structure. Option B is wrong because Azure AI Language custom entity extraction is for identifying entities in text, not for extracting text and images from documents. Option D is wrong because the Azure AI Vision OCR skill extracts text from images but does not handle document layout, tables, or multi-format documents natively.

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56
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

A tenant ID field with query filters enforces logical isolation within one index, satisfying the single-service constraint. Filters apply at query time, so each tenant retrieves only its own documents. This is the documented approach for multi-tenancy when index-per-tenant isolation is unnecessary, though filter discipline must be enforced consistently.

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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57
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

degreeOfParallelism of 3 lets the indexer dispatch three concurrent calls per batch, so bursts exceed the function's 10 documents per second limit. The function throttles with 429 responses; lowering parallelism to 1 aligns throughput with capacity.

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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58
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

Creating a data source establishes the connection between the search indexer and Azure Blob Storage, which is the prerequisite for pulling PDF content. The indexer cannot traverse or extract text from the container until this data source object exists, so it must precede defining the index, skillset, or indexer itself.

Why this answer

In Azure Cognitive Search, the pipeline always begins with a data source that defines the connection to the underlying content store. Before you can create an indexer, skillset, or index, you must register the Azure Blob Storage container as a data source so the service knows where to pull the PDFs from. Only after the data source exists can an indexer crawl it and push content through the enrichment pipeline.

Exam trap

AI-102 often tests the ordering of the Cognitive Search pipeline, and candidates mistakenly jump to the indexer or skillset because those feel like the 'real work' — but the data source is always the mandatory first object.

How to eliminate wrong answers

Option A is wrong because an index defines the searchable schema, but it cannot be created meaningfully until the source content and its fields are known — and it is not the first step in the ingestion pipeline. Option B is wrong because a skillset is an optional enrichment layer (OCR, entity extraction, etc.) that runs after an indexer has already pulled documents from a data source. Option D is wrong because an indexer requires a data source to exist first; scheduling it daily is a later configuration step, not the initial one.

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59
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

A custom skill in the skillset invokes your deployed Azure AI Language endpoint over HTTP, letting the enrichment pipeline attach the extracted chemical compounds and diseases as indexable fields. This satisfies the requirement to use your existing custom entity extraction model rather than a built-in skill.

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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60
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 search ranks results using a semantic configuration that designates title and content fields for caption and answer generation. Without that configuration, semantic ranking cannot use the fields, so relevance and contextual captions remain poor.

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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61
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

Entity recognition splits input into 5,000-character chunks, so 12,000 characters yields three executions: two full chunks plus a 2,000-character remainder. This satisfies the stem's chunking constraint, where each skill invocation processes at most 5,000 characters, making three the accurate count.

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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62
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

Azure AI Language provides the sentiment analysis and key phrase extraction capabilities the skillset requires, satisfying the stem's demand for both enrichments from one service. Its built-in Text Analytics skills integrate directly into Azure AI Search indexers, so each ticket is scored and mined for phrases during indexing without custom model code.

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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63
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

Azure AI Document Intelligence extracts text and layout from PDFs and Word files, while Azure AI Language performs named entity recognition for parties, judges and case numbers. A custom skill redacts sensitive content before indexing, satisfying the pre-indexing redaction constraint, and Azure AI Search delivers the required full-text search index.

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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64
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 custom extraction models learn from as few as five labelled samples, satisfying the minimise-labelling constraint. Deploying it as a custom skill lets Azure AI Search invoke the model during indexing, so handwritten name, ID, date of birth and degree fields are extracted and mapped into searchable index fields.

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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65
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

Marking the description field searchable enables full-text tokenisation, which underpins fuzzy matching, while a suggester builds the dedicated autocomplete and suggestions structures. Both are required because fuzzy search and autocomplete read from different index constructs, and the stem demands support for both.

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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66
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

The prebuilt-invoice model is trained to extract invoice-specific fields including line items, invoice number, date and total amount. Custom or general models lack this schema, so the prebuilt invoice model directly addresses the failing line-item extraction described in the scenario.

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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67
Multi-Selectmedium

You are building an Azure AI Search enrichment pipeline that processes PDF documents from Azure Blob Storage. The documents contain both text and images. You need to extract text from the images and also detect the language of the extracted text to route documents to language-specific processing. Which two built-in skills should you include in the skillset? (Choose two.)

Select 2 answers
A.Text Translation skill
B.OCR skill
C.Language Detection skill
D.Entity Recognition skill
E.Key Phrase Extraction skill
AnswersB, C

The OCR skill is a built-in cognitive skill that extracts text from images. It is essential for this scenario because the PDFs contain images with embedded text. The OCR skill can process image files or images extracted from PDFs (when using the Document Extraction skill). It outputs text that can then be used by other skills, such as language detection. Without OCR, the text in images would not be available for indexing or further enrichment.

Why this answer

The OCR skill is required to extract text from images within the PDFs. The Language Detection skill is required to identify the language of the extracted text for routing. Together, they enable the pipeline to process image-based text and then determine its language.

The other skills do not provide these capabilities: key phrase extraction, entity recognition, and translation serve different purposes.

Exam trap

The trap here is assuming that language detection can work directly on images or that OCR also detects language, when in fact they are separate steps that must be chained.

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68
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-specific analyzers apply per-field lexical processing, so each language's tokens are stemmed and indexed correctly. This satisfies the requirement that users search and retrieve results in their native language, since queries analysed with the matching language analyzer return same-language documents rather than mistranslated or poorly tokenised matches.

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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69
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

A custom classification model trains on labelled samples to categorise documents into your own classes, such as travel or office supplies, before extraction. This satisfies the requirement to classify expense report images into categories prior to extracting fields.

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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70
MCQhard

You are designing an Azure AI Search enrichment pipeline that extracts entities from text using the Entity Recognition skill. You need to ensure that the extracted entities are stored as a collection in the index so that users can filter and facet on them. Which index field type should you use?

A.Edm.ComplexType
B.Edm.String
C.Edm.Int32
D.Collection(Edm.String)
AnswerD

Collection(Edm.String) allows storing multiple string values in a single field. This is ideal for entities extracted by the Entity Recognition skill, which outputs a list of entities. With this type, you can apply filters and facets on each entity value. It preserves the collection nature of the output and enables advanced query scenarios.

Why this answer

The Entity Recognition skill outputs a list of strings representing entities. To store this list and enable filtering and faceting, the index field must be a collection of strings. Collection(Edm.String) is the correct type because it accommodates multiple values and supports the necessary query capabilities.

Other types either cannot hold multiple values or are not designed for text data.

Exam trap

The trap here is assuming that a complex type is needed to store structured entity data, when the skill actually outputs a simple string collection that can be directly mapped.

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71
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 scans enriched content and identifies entities such as credit card numbers, then allows masking or redaction before the data is written to the index. This satisfies the requirement that sensitive information is automatically removed from indexed content during skillset execution.

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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72
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

A custom skill runs PII detection and redaction during enrichment, so sensitive values are removed before documents reach the index. Because the index never stores the PII, search results cannot return it, satisfying the requirement.

Why this answer

A custom skill in the Azure AI Search skillset lets you invoke a custom function (e.g., Azure Function calling Azure AI Language's PII detection or a regex-based redactor) during the enrichment pipeline, before documents are indexed. This ensures PII is detected and redacted/removed at ingestion time, so it never appears in the search index or query results.

Exam trap

AI-102 often tests the confusion between access control (RBAC) and data minimization (redaction) — candidates pick RBAC thinking 'restricting access' satisfies 'not returned in results,' but the requirement is about the data itself, not who can see it.

How to eliminate wrong answers

Option A is wrong because encryption at rest protects data from unauthorized access to storage but does not prevent PII from being returned in search results — the data is still indexed and queryable. Option B is wrong because RBAC controls who can query the index, but authorized users would still see PII; the requirement is to prevent PII from being returned at all, not just to restrict access. Option D is wrong because field mappings only map source fields to index fields — they do not detect or redact PII within content; excluding a field by name assumes PII is isolated in known fields, which is not reliable for free-text documents.

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73
Multi-Selectmedium

Which TWO Azure AI Search features should you enable to improve the relevance of search results for a knowledge mining solution that supports natural language queries?

Select 2 answers
A.Synonyms
B.Semantic ranking
C.Search mode 'all'
D.Scoring profiles
E.Filters
AnswersB, D

Semantic ranking applies Microsoft's language models to re-rank the initial result set, so natural language queries return contextually relevant matches rather than keyword-only hits. It satisfies the requirement to improve relevance for conversational queries in the knowledge mining solution.

Why this answer

Semantic ranking (B) is correct because it uses Microsoft's language understanding models to re-rank the top results from the initial BM25 retrieval, promoting results that are semantically relevant to natural language queries rather than just keyword matches. Scoring profiles (D) are correct because they let you boost or demote documents based on weighted fields, functions (e.g., magnitude, freshness, distance), and parameters, directly tuning relevance for the knowledge mining scenario. Synonyms (A) only expand query terms with equivalent expressions and do not provide semantic re-ranking or weighted relevance boosting.

Search mode 'all' (C) merely requires all query terms to match, which is a stricter boolean behavior that can reduce recall rather than improve relevance. Filters (E) restrict the result set by criteria such as OData expressions but do not affect ranking or relevance scoring.

Exam trap

AI-102 often tests the distinction between recall-improving features (synonyms, search mode) and relevance-ranking features (semantic ranking, scoring profiles), where candidates incorrectly select synonyms or filters for relevance improvement.

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74
MCQeasy

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

A.Enable OCR on the documents and use regular expressions to extract fields.
B.Convert the invoices to a standard format before processing.
C.Train a custom model using labeled samples of the vendor's invoices.
D.Use the prebuilt invoice model with confidence threshold adjustment.
AnswerC

A custom model trained on labelled samples of that vendor's invoices learns its non-standard layout and field positions, which the prebuilt invoice model cannot capture. This directly targets the extraction accuracy problem for that specific vendor.

Why this answer

Training a custom model using labeled samples of the vendor's invoices allows Document Intelligence to learn the non-standard layout, improving extraction accuracy. Option A is incorrect because while OCR and regex can extract text, they are not effective for structured data extraction from variable layouts. Option B is incorrect because converting invoices to a standard format is time-consuming and may lose important data; it's better to train a model on the actual invoices.

Option D is incorrect because the prebuilt invoice model is designed for standard invoice layouts; adjusting confidence thresholds won't fix extraction accuracy for non-standard formats.

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75
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

Setting context to `/document` makes the skill execute once per document rather than per page or chunk, while the input source `/document/content` passes the full extracted text of that document into the Azure Function. This satisfies the requirement that the custom skill receives the entire document content.

Why this answer

To pass the entire document content to a custom skill, the skill's context must be set to '/document' (the root of each document in the enrichment tree) and the input source must be '/document/content'. Setting context to '/document' ensures the skill executes once per document, and mapping the input to '/document/content' delivers the full text content to the Azure Function. This is the correct configuration for document-level enrichment.

Exam trap

AI-102 often tests the distinction between skill context (execution granularity) and input source (data passed to the skill), tricking candidates who set context to '/document/content' instead of '/document' or who confuse content with normalized_images.

How to eliminate wrong answers

Option A is wrong because while context '/document/content' would scope the skill to the content node, the input source '/document/metadata' would pass metadata rather than the full content, and the context is not the correct document-level scope for whole-document enrichment. Option B is wrong because setting context to '/document/content' scopes the skill to the content field itself rather than the document root, which can cause issues when the skill needs document-level context and multiple inputs. Option C is wrong because context '/document' is correct, but the input source '/document/normalized_images/*' passes normalized images (used for OCR/image skills), not the document's text content.

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