AI-102 Implement knowledge mining and information extraction solutions Practice Test 3 — 15 Questions

Question 1

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?

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

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 2

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 3

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 4

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 5

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

Accepted Answer

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

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Add a separate field for each word in the document

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Change the analyzer to a custom analyzer that splits on spaces only

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Set the 'content' field to a higher boosting value

Question 6

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 7

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 8

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

Accepted Answer

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

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Enable incremental enrichment on the skillset

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Use an indexer with a schedule set to run every 5 minutes

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Enable semantic search to speed up indexing

Question 9

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.

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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

Question 10

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?

Accepted Answer

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

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Entity Recognition skill

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Key Phrase Extraction skill

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Text Translation skill

Question 11

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

Accepted Answer

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

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Train the classification model with one sample per type

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Use the prebuilt invoice and purchase order models for classification

Question 12

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

Accepted Answer

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

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Synonym maps

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Suggesters

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Filterable fields

Question 13

You are building a solution to extract customer feedback from PDF documents stored in Azure Blob Storage. The solution must extract key phrases and sentiment scores, but you cannot use any pre-built models from Azure AI Language. What should you use?

Accepted Answer

Train custom models in Azure AI Language for key phrase extraction and sentiment analysis. Azure AI Language supports training custom models for named entity recognition (NER) to extract key phrases and custom text classification to predict sentiment labels. You can first extract text from PDFs using Azure AI Document Intelligence or other OCR services, then pass the text to the custom Language models. This approach meets the requirement without using any pre-built Azure AI Language models. Options A and C rely on pre-built APIs and are disallowed. Option D only provides OCR and cannot perform semantic analysis.

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Use the sentiment analysis capability in Azure AI Language

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

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

Question 14

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?

Accepted Answer

Azure AI Document Intelligence. Azure AI Document Intelligence (formerly Form Recognizer) is specifically designed to extract structured data like invoice numbers, dates, and total amounts from scanned documents. It can be scheduled to run automatically via APIs or pipelines. Azure Bot Service is for conversational AI, not document extraction. Azure AI Search with built-in skills can index and search documents but is not optimized for extracting specific fields from invoices. Azure AI Foundry model catalog is for discovering and deploying AI models, not for direct document extraction.

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Azure Bot Service

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Azure AI Search with built-in skills

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Azure AI Foundry model catalog

Question 15

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

Accepted Answer

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

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Use a multi-model ensemble by training separate models per template

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Retrain the model from scratch each month using all historical data

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Use Azure Machine Learning pipelines to automate retraining and deploy a new endpoint