AI-102 Implement knowledge mining and information extraction solutions Practice Test 11 — 15 Questions

Question 1

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?

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

Accepted Answer

Create a data source that connects to Azure Blob Storage.. 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.

Answer

Create an index with a field for each metadata property.

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Create a skillset to extract text from PDFs.

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Create an indexer that runs daily.

Question 2

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?

Accepted Answer

Custom Named Entity Recognition (NER). 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.

Answer

Prebuilt Named Entity Recognition (NER)

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PII detection

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Entity linking

Question 3

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?

Accepted Answer

Create a custom question answering project and import the Word document as a source.. 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.

Answer

Use Azure AI Document Intelligence to extract QnA pairs.

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Use the prebuilt question answering API to parse the document.

Answer

Use conversational language understanding (CLU) to extract intents and entities.

Question 4

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?

Accepted Answer

Create a custom skill in the skillset that calls the custom entity extraction endpoint via HTTP.. 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.

Answer

Deploy the custom model to Azure AI Document Intelligence and use a Document Intelligence skill.

Answer

Add the custom entity extraction as a field mapping in the indexer.

Answer

Use the built-in Entity Recognition skill and configure it to use your custom model endpoint.

Question 5

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?

Accepted Answer

Azure AI Document Intelligence. 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.

Answer

Azure AI Vision

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Azure AI Language

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Azure AI Search

Question 6

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?

Accepted Answer

Key Phrase Extraction. 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.

Answer

Entity Recognition

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Sentiment Analysis

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OCR

Question 7

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?

Accepted Answer

Configure a retry policy on the custom skill. 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.

Answer

Reduce the batch size in the indexer

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Increase the skill timeout

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Schedule the indexer to run less frequently

Question 8

You are troubleshooting an Azure AI Search indexer that fails to index a PDF file stored in Azure Blob Storage. The error message indicates that the document is encrypted. What is the most likely cause and solution?

Accepted Answer

The PDF is encrypted; remove encryption before indexing. The most likely cause is that the PDF is encrypted, and the solution is to remove encryption before indexing. Azure AI Search indexers cannot decrypt password-protected or encrypted PDFs; they require unencrypted content to extract text. Therefore, the file must be decrypted prior to indexing.

Answer

The indexer is not configured with the PDF parser; set the parsing mode

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The file format is unsupported; convert to PDF/A

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The file is too large; split it into smaller parts

Question 9

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?

Accepted Answer

Use Azure AI Document Intelligence layout model as a custom skill. 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.

Answer

Upload documents directly to Azure AI Search

Answer

Use Azure AI Language custom entity extraction

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Use Azure AI Vision OCR skill in the skillset

Question 10

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?

Accepted Answer

Semantic ranking. 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.

Answer

Synonyms

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Search mode 'all'

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Filters

Question 11

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

Accepted Answer

The API endpoint must be reachable from the search service. 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.

Answer

The skill can only accept one input and produce one output

Answer

The skill must be written in Python

Question 12

You are a solution architect at a legal firm. The firm wants to build a copilot using Microsoft Foundry that answers questions about case law documents stored in Azure Blob Storage. The copilot should use the Retrieval Augmented Generation (RAG) pattern with Azure AI Search as the vector store. The documents are in PDF format and include complex tables and footnotes. The solution must ensure that the answers are grounded in the documents and that the copilot can handle follow-up questions. You need to design the ingestion pipeline. Which approach should you take?

Accepted Answer

Use Azure AI Document Intelligence to extract content, then chunk by headings and paragraphs, generate embeddings using Azure OpenAI, and index in Azure AI Search with vector search. It uses Azure AI Document Intelligence to accurately extract content from PDFs (including complex tables and footnotes), then chunks by headings and paragraphs to preserve document structure, generates embeddings via Azure OpenAI for semantic understanding, and indexes in Azure AI Search with vector search to enable RAG-based, grounded answers with follow-up support.

Answer

Use Azure AI Vision OCR to extract text, split by page, and use Azure AI Search keyword search

Answer

Use Azure AI Document Intelligence prebuilt-read model, chunk by character count, and use Azure AI Search with semantic ranking

Answer

Use Azure AI Language to extract key phrases, create a non-vector index, and use simple search

Question 13

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?

Accepted Answer

Use Azure AI Language entity extraction as a custom skill, and enable fuzzy search in the index. 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.

Answer

Use Azure AI Language key phrase extraction, and enable vector search

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Use Azure AI Document Intelligence to extract entities, and enable semantic ranking

Answer

Use Azure AI Vision OCR to extract text, and enable synonyms

Question 14

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?

Accepted Answer

Use Azure AI Document Intelligence with a custom extraction model trained on contract clauses.. 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.

Answer

Use Azure AI Custom Vision to detect clause regions in scanned documents.

Answer

Use Azure OpenAI GPT-4 to process each PDF and extract clauses using prompts.

Answer

Use Azure AI Search with a blob indexer to extract clauses during indexing.

Question 15

A company uses Azure AI Search to index customer support transcripts. They want to enable users to find relevant answers by asking natural language questions. Which feature should they enable in the search service?

Accepted Answer

Semantic search. Semantic search in Azure AI Search enhances the ranking of results by using language understanding models to re-rank matches based on semantic relevance to the query, enabling users to ask natural language questions and get more relevant answers. It is the feature designed to improve relevance for natural language queries.

Answer

Synonym maps

Answer

Cognitive skills

Answer

Knowledge mining