AI-102 Implement knowledge mining and information extraction solutions Practice Test 12 — 15 Questions

Question 1

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?

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

Accepted Answer

PII detection skill. 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.

Answer

Custom Entity Lookup skill and Sentiment skill

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Text Translation skill and Entity Recognition skill

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Entity Recognition skill and Key Phrase Extraction skill

Question 2

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

Accepted Answer

Use the Text Translation skill to translate all content to English during indexing.. Azure AI Search's Text Translation skill (backed by Azure AI Translator) translates non-English content into a target language during the enrichment pipeline, so all indexed content is normalized to English and searchable in English only. This directly satisfies the requirement to make multilingual manuals searchable in English. The skill runs at indexing time, so the index contains only English text.

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Use the Entity Recognition skill to extract entities and then index entities only.

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Use the Language Detection skill to identify language and then index all content as-is.

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Use the Key Phrase Extraction skill to extract key phrases and then index them.

Question 3

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

Accepted Answer

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

Answer

Add a custom scoring profile based on freshness.

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

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Enable semantic search on the 'content' field.

Question 4

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

Accepted Answer

Azure AI Document Intelligence. Azure AI Document Intelligence (option A) is correct because it provides prebuilt and custom models that perform OCR and extract text, key-value pairs, and tables from documents, including support for handwritten text via its Read and Layout models. Azure AI Vision (option C) is correct because its Image Analysis and OCR capabilities, including the Read API, are specifically designed to extract printed and handwritten text from images. Azure AI Speech (option B) is incorrect because it handles speech-to-text and text-to-speech, not text extraction from images. Azure AI Search (option D) is incorrect because it is a search indexing and query service, not an OCR or handwriting recognition service. Azure AI Language (option E) is incorrect because it focuses on natural language processing tasks such as sentiment analysis, entity recognition, and translation, not image text extraction.

Answer

Azure AI Speech

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Azure AI Search

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Azure AI Language

Question 5

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

Accepted Answer

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

Answer

Use Azure AI Custom Vision to train a model to detect handwriting regions, then use Azure AI Vision OCR to read text.

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Use Azure AI Search with a blob indexer and a skillset that includes OCR skill and Entity Recognition skill.

Answer

Use Azure AI Vision OCR to extract text from images, then use Azure AI Language to extract entities like name, date, and degree.

Question 6

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

Accepted Answer

Skillset with Entity Recognition skill, Sentiment skill, and Key Phrase Extraction skill. Use Azure OpenAI service to generate summaries via a custom skill that calls the GPT model. Enable semantic search.. 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.

Answer

Use Azure AI Document Intelligence to extract content from HTML, then use Azure AI Language to extract entities and sentiment. Index in Azure AI Search with semantic search.

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

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Skillset with Entity Recognition skill, Sentiment skill, and Text Analytics for Health skill to extract medical terms. Use Azure OpenAI for summarization as a custom skill.

Question 7

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

Accepted Answer

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

Answer

Add a Document Intelligence skill to classify intents.

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

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

Question 8

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

Accepted Answer

Develop a custom skill that converts EPUB files to plain text and add it to the skillset.. Azure AI 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.

Answer

Use Azure AI Document Intelligence to extract text from EPUB files directly.

Answer

Modify the Document Intelligence skill to accept EPUB files.

Answer

Register a new data source type for EPUB in Azure AI Search.

Question 9

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

Accepted Answer

Create a custom skill that calls the custom NER endpoint and map the output to the index fields.. To integrate a custom NER model into an Azure AI Search enrichment pipeline, you must create a custom skill that calls the custom NER endpoint. The custom skill is a web API that the skillset invokes, and it can map the JSON output to index fields. This allows the enrichment pipeline to use the custom model's predictions.

Answer

Use a Language Understanding (LUIS) app to extract entities and call it from a custom skill.

Answer

Use the built-in Entity Recognition skill and configure it with your custom model's endpoint.

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Configure the indexer to call the custom NER endpoint directly during indexing.

Question 10

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?

Accepted Answer

DocumentIntelligenceLayoutSkill. 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.

Answer

DocumentExtractionSkill

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TextMergeSkill

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OcrSkill

Question 11

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?

Accepted Answer

Key Phrase Extraction skill. 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.

Answer

Entity Recognition skill

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Language Detection skill

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Text Translation skill

Question 12

You are implementing an Azure AI Search enrichment pipeline that extracts text from PDF documents stored in Azure Blob Storage. The PDFs are scanned images with no embedded text layer. You need to ensure the extracted text is available for downstream skills. Which skill should you add to the skillset?

Accepted Answer

Microsoft.Skills.Vision.OcrSkill. The OCR skill is specifically designed to extract text from images, including scanned PDFs, within an Azure AI Search enrichment pipeline. It leverages the Computer Vision Read API to recognize text and output it for further processing. Other skills like Split, Merge, or custom Web API do not provide built-in OCR capabilities. Therefore, adding the OCR skill is the correct approach to make the scanned PDF content searchable.

Answer

Microsoft.Skills.Text.SplitSkill

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Microsoft.Skills.Text.MergeSkill

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Microsoft.Skills.Custom.WebApiSkill

Question 13

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?

Accepted Answer

Collection(Edm.String). 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.

Answer

Edm.ComplexType

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Edm.String

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Edm.Int32

Question 14

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?

Accepted Answer

The outputs array of the skill definition. 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.

Answer

The input parameter of the indexer

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The context property of the skill

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The outputFieldMappings of the indexer

Question 15

You are building an Azure AI Search enrichment pipeline that enriches documents with key phrases detected by Azure AI Language. You need the key phrases to be returned as a structured collection that can be mapped to a Collection(Edm.String) field in the search index, and you must avoid storing the enriched text in the enrichment cache. Which output mapping configuration should you use in the indexer?

Accepted Answer

Define an outputFieldMapping with a source of /document/keyPhrases and a targetField of keyPhrases.. The Key Phrases skill writes its output to the enrichment tree at /document/keyPhrases as an array of strings. To persist that array into a Collection(Edm.String) field, the indexer needs an outputFieldMapping whose source is that exact path and whose target is the index field. Cache behavior is configured separately on the indexer and is not part of output mapping.

Answer

Define an outputFieldMapping with a source of /document/pages/*/keyPhrases and a targetField of keyPhrases.

Answer

Define an outputFieldMapping with a source of /document/keyPhrases and a targetField of keyPhrases, and set the indexer cache to disabled.

Answer

Define an outputFieldMapping with a source of /document/keyPhrases and a targetField of keyPhrases, and mark the field as retrievable and filterable.