AI-102 Implement knowledge mining and information extraction solutions Practice Test 8 — 15 Questions

Question 1

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?

Accepted Answer

Azure AI Document Intelligence. 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.

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Azure AI Language

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Azure AI Search

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Azure AI Vision

Question 2

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

Accepted Answer

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

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The indexer skips the failed documents and continues with the same batch.

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The indexer stops completely because 3 documents failed.

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The indexer retries the failed documents.

Question 3

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?

Accepted Answer

Key Phrase Extraction skill. 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.

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Sentiment skill

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Language Detection skill

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Entity Recognition skill

Question 4

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

Accepted Answer

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

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Use Azure AI Document Intelligence's pre-built model to analyze documents and store results in a database, then use a SQL indexer to index the database.

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Use the OCR skill to extract text and then use regular expressions to find PO number and total.

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Manually enter the extracted data into the search index.

Question 5

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?

Accepted Answer

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

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Language Service

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Speech Service

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Computer Vision

Question 6

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

Accepted Answer

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

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Apply a filter

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Configure a scoring profile

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Enable semantic search

Question 7

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

Accepted Answer

Azure AI Language Service. 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.

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Computer Vision

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Azure AI Document Intelligence

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Azure AI Speech Service

Question 8

Your knowledge mining solution uses Azure AI Search with cognitive skills. During testing, you notice that some documents are not being enriched because the skillset execution fails. Which diagnostic step should you take first?

Accepted Answer

Enable debug mode on the skillset. When a skillset execution fails during enrichment, the first diagnostic step is to enable debug mode on the skillset. Debug mode allows you to step through each skill execution, inspect inputs and outputs, and identify exactly where the failure occurs. This targeted approach is more efficient than reviewing logs or re-running the indexer, because it provides granular, per-document debugging without waiting for a full indexer run.

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Review the indexer execution history in the portal

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Re-run the indexer with a fresh document

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Check the indexer logs in Azure Monitor

Question 9

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

Accepted Answer

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

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Azure AI Language Service - Custom Question Answering

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Azure AI Computer Vision

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Azure AI Document Intelligence

Question 10

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?

Accepted Answer

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

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Use Azure AI Search with encryption at rest

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Implement role-based access control on the search index

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Configure field mappings to exclude PII fields

Question 11

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

Accepted Answer

Read API. 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.

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Custom model

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Layout model

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Prebuilt invoice model

Question 12

Which TWO services can be used to enrich an Azure AI Search index with knowledge mining skills? (Choose two.)

Accepted Answer

Azure AI Computer Vision. Azure AI Search indexes can be enriched using built-in AI skills that call Azure AI services. Azure AI Computer Vision provides image analysis skills (e.g., OCR, description, tag extraction) that can be attached to an indexer pipeline. Azure AI Language Service provides text analytics skills (e.g., entity recognition, key phrase extraction, sentiment analysis) that enrich document fields during indexing.

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Azure AI Video Indexer

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Azure AI Speech Service

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Azure AI Translator

Question 13

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

Accepted Answer

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

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Ensure the skill executes within 2 minutes

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Write the skill in a language supported by Azure AI Search

Question 14

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

Accepted Answer

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

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Filters

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Indexers

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Synonym maps

Question 15

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

Accepted Answer

The input source path in the entity recognition skill should be relative to the context, not absolute. The entity recognition skill has its context set to '/document/pages/*', which means it iterates over each page in the split output. However, its input source is '/document/pages/*' (absolute path), which points to the entire page array element rather than the text content within each page. The input source should be relative to the context, e.g., 'text' if the page had a property, or simply the context itself if the page is a string. Using an absolute path that matches the context can cause the skill to fail because it expects a scalar input, but the context iteration provides the array element. Therefore, the correct fix is to make the input source relative, such as using 'text' or '/document/pages/*/text' appropriately. Option D correctly identifies this issue.

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The entity recognition skill requires a language code that is missing

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The split skill is not producing pages because the content is too short

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The entity recognition skill is not registered in the skillset