AI-102 Implement knowledge mining and information extraction solutions Practice Test 7 — 15 Questions

Question 1

You are building an Azure AI Search enrichment pipeline that must extract text and structure from scanned PDF documents stored in Azure Blob Storage. The documents contain both printed text and handwritten notes in the margins. You need to extract all text, including handwriting, and also identify the layout of tables within the documents. Which two actions should you perform? (Choose two.)

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

Accepted Answer

Create a custom skill that calls the Azure AI Document Intelligence Read model to extract printed and handwritten text.. To extract both printed and handwritten text, the Azure AI Document Intelligence Read model is appropriate and can be integrated via a custom skill. To identify table layouts, the Layout model provides structural extraction of tables and text. These two custom skills together meet the requirements. Built-in OcrSkill does not handle handwriting, DocumentExtractionSkill is not a built-in skill, and MergeSkill only combines already-extracted text.

Answer

Add a DocumentExtractionSkill to the skillset to extract text and layout from the PDFs.

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Add a MergeSkill to the skillset to combine text from multiple pages into a single field.

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Add an OcrSkill to the skillset and set the 'detectOrientation' parameter to true.

Question 2

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

Accepted Answer

The indexer encountered errors and marked the documents as failed. When an indexer runs, it processes each document and can encounter errors such as unsupported file formats, corrupt content, or permission issues. If a document fails during indexing, the indexer records the error and does not add that document to the index, making it invisible to search queries. This is the most direct cause of missing documents after ingestion. Other options affect search behavior but not whether documents are indexed in the first place.

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The index does not have a semantic configuration

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The search service has insufficient replicas

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The search service is throttled due to high query volume

Question 3

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?

Accepted Answer

Ensure the index includes a semantic configuration with title and content fields. 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.

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Increase the number of partitions to handle more data

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Configure a scoring profile with boosting based on metadata

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Increase the number of replicas to improve query performance

Question 4

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?

Accepted Answer

Premade invoice model. 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.

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Custom extraction model

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Layout model

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Read model

Question 5

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?

Accepted Answer

Use a single index with a tenant ID field and filter queries by that field. 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.

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Use separate skillsets for each tenant

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Create a separate search service for each tenant

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Use separate data sources within the same index

Question 6

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?

Accepted Answer

Azure AI Language. 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.

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Azure AI Document Intelligence

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Azure AI Computer Vision

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Azure AI Translator

Question 7

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

Accepted Answer

Azure AI Document Intelligence. Azure AI Document Intelligence (option B) is correct because its Read and Layout models perform OCR on documents and images, extracting printed and handwritten text along with structure for downstream knowledge mining enrichment. Azure AI Computer Vision (option C) is correct because its Read OCR feature (Image Analysis / Read API) extracts printed and handwritten text from images, a core skill in Azure AI Search cognitive skillsets. Azure AI Language (option A) is wrong because it handles text analytics such as entity recognition, sentiment, and key phrase extraction, not image OCR. Azure AI Video Indexer (option D) is wrong because it targets video and audio insights like transcription and face tracking, not still-image text extraction. Azure AI Custom Vision (option E) is wrong because it trains image classification and object detection models, not text recognition.

Answer

Azure AI Language

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Azure AI Video Indexer

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Azure AI Custom Vision

Question 8

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

Accepted Answer

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

Answer

The people and organizations fields should be Edm.String instead of Collection(Edm.String)

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The id field is not defined as a key in the index

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The suggester searchMode should be 'analyzingInfixMatching' which is incorrect

Question 9

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?

Accepted Answer

Azure AI Document Intelligence. 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.

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Azure AI Computer Vision

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Azure AI Language Service

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Azure AI Search

Question 10

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

Accepted Answer

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

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Azure AI Document Intelligence with prebuilt invoice and receipt models

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Azure AI Language Service with custom Named Entity Recognition (NER)

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Azure AI Computer Vision with OCR and Azure AI Search

Question 11

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?

Accepted Answer

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

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

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

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

Question 12

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?

Accepted Answer

Azure AI Search with semantic search. 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.

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Azure AI Document Intelligence

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Azure AI Language Service with custom question answering

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Azure AI Computer Vision

Question 13

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?

Accepted Answer

PII Detection skill. 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.

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Entity Recognition skill

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Conditional skill

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Text Translation skill

Question 14

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?

Accepted Answer

Custom classification model. 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.

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OCR capability

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Layout extraction

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Prebuilt expense report model

Question 15

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

Accepted Answer

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

Answer

The language detection model misidentified the language

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You need to set a confidence threshold to exclude negative phrases

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The service is not trained on your specific domain