AI-102 Implement knowledge mining and information extraction solutions Practice Test 10 — 15 Questions

Question 1

A healthcare organization needs to mine clinical notes to find mentions of diseases, medications, and treatment procedures. The data is stored in Azure SQL Database. Which Azure AI service should they integrate with Azure AI Search to extract these entities?

Accepted Answer

Azure AI Health Insights. Option B is correct because Azure AI Health Insights (formerly Text Analytics for Health) is specifically designed to extract medical entities from clinical text. Option A is incorrect because Azure AI Language provides general entity extraction but not healthcare-specific. Option C is incorrect because Azure AI Document Intelligence is for document extraction, not clinical notes. Option D is incorrect because Azure AI Search is the indexing service, not the extraction service.

Answer

Azure AI Document Intelligence

Answer

Azure AI Search

Answer

Azure AI Language

Question 2

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. Option C is correct because Azure AI Search cannot index encrypted documents; the solution is to decrypt the PDF before uploading or use a decryption step. Option A is incorrect because indexers can handle large files (up to 16 MB). Option B is incorrect because unsupported file types would give a different error. Option D is incorrect because the indexer does not need a specific parser for encrypted files.

Answer

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

Answer

The file format is unsupported; convert to PDF/A

Answer

The file is too large; split it into smaller parts

Question 3

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. Option D is correct because Azure AI Document Intelligence with layout model extracts text, tables, and structure from documents. Option A is incorrect because Azure AI Vision OCR extracts text but not table structure. Option B is incorrect because Azure AI Language extracts entities but not tables. Option C is incorrect because Azure AI Search alone cannot extract content.

Answer

Upload documents directly to Azure AI Search

Answer

Use Azure AI Language custom entity extraction

Answer

Use Azure AI Vision OCR skill in the skillset

Question 4

Refer to the exhibit. You are configuring an Azure AI Search skillset. The skillset includes an EntityRecognitionSkill and a KeyPhraseExtractionSkill. After running the indexer, you notice that the 'organizations' field is empty in the index. What is the most likely cause?

Accepted Answer

The output field mapping for organizations is missing. Option B is correct because the entity recognition skill outputs entities into a specific structure; to get organizations, you need to map the '/document/organizations' path to the index field. Option A is incorrect because the skill is correctly configured to extract organizations. Option C is incorrect because the order of skills doesn't affect entity recognition. Option D is incorrect because the skill output path is standard.

Answer

The skill output path is incorrect

Answer

The 'Organization' category is misspelled

Answer

The skills must be in reverse order

Question 5

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. Options A and D are correct. Semantic ranking re-orders results using deep learning models to match query intent, and scoring profiles allow customizing relevance based on field weights, boosting, and freshness. Option B is incorrect because search mode 'all' returns documents containing all terms, which may not improve relevance. Option C is incorrect because synonyms expand queries but do not re-rank. Option E is incorrect because filters narrow results but do not improve ranking.

Answer

Synonyms

Answer

Search mode 'all'

Answer

Filters

Question 6

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. Options B, C, and D are correct. Custom skills must have a timeout of 230 seconds (default), the API must be accessible from the search service (public or via private endpoint), and data size limits apply (payload up to 16 MB). Option A is incorrect because custom skills can be written in any language that supports JSON. Option E is incorrect because the skill can have multiple inputs and outputs.

Answer

The skill can only accept one input and produce one output

Answer

The skill must be written in Python

Question 7

Which TWO built-in cognitive skills in Azure AI Search can be used to extract entities from text?

Accepted Answer

PII Detection. Options A and C are correct. Entity Recognition extracts named entities (Person, Organization, Location). PII Detection extracts personally identifiable information entities (e.g., phone numbers, emails). Option B is incorrect because Sentiment Analysis determines sentiment. Option D is incorrect because Language Detection detects language. Option E is incorrect because Custom Entity Lookup matches against a custom list.

Answer

Custom Entity Lookup

Answer

Sentiment Analysis

Answer

Language Detection

Question 8

You are a data engineer at a multinational corporation. The company has thousands of research reports in PDF format stored in Azure Blob Storage. The reports contain text, tables, charts, and handwritten annotations. Your team needs to build a knowledge mining solution using Azure AI Search that allows researchers to query the reports using natural language. The solution must extract text, table structures, and handwritten annotations. Additionally, the solution must handle multiple languages (English, Spanish, and French) and ensure that the index is updated daily as new reports are added. The search should prioritize the most recent reports. You have an Azure AI Search service in the S2 tier. Which combination of actions should you take to meet these requirements?

Accepted Answer

Use Azure AI Document Intelligence layout model with OCR, add a custom translation skill, and configure a scoring profile with freshness boosting. Option B is correct. Using Azure AI Document Intelligence's layout and OCR capabilities extracts text, tables, and handwriting. The enrichment pipeline with a custom skill using the translation service handles multilingual content, and a scoring profile with freshness boosting prioritizes recent reports. Option A is incorrect because Azure AI Vision OCR alone does not extract table structure. Option C is incorrect because the Language service does not handle document layout. Option D is incorrect because scheduling the indexer once a week does not meet the daily update requirement.

Answer

Use Azure AI Vision OCR skill for text extraction, add a translation skill, and use a simple search query

Answer

Use Azure AI Document Intelligence prebuilt-read model, add a custom skill for language detection, and schedule the indexer weekly

Answer

Use Azure AI Language text extraction, a custom entity recognition skill, and enable semantic ranking

Question 9

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. Option A is correct. Using Azure AI Document Intelligence to chunk documents into meaningful sections preserves context, and generating embeddings with Azure OpenAI allows vector search for RAG. Option B is incorrect because splitting by page may break tables and footnotes. Option C is incorrect because keyword search alone does not support semantic understanding. Option D is incorrect because Azure AI Language does not handle PDF extraction or vector generation.

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 10

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. Option C is correct. Using Azure AI Language for entity extraction extracts attributes like color and brand, and enabling fuzzy search in the index handles misspellings. Option A is incorrect because Azure AI Document Intelligence is for document extraction, not text from a database. Option B is incorrect because OCR is for images. Option D is incorrect because the Language service does not perform vector search.

Answer

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

Answer

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 11

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.. Option B is correct because Azure AI Document Intelligence (formerly Form Recognizer) offers custom extraction models with high accuracy and is optimized for document understanding. Azure AI Search with blob indexers is for indexing, not extraction. Azure OpenAI GPT-4 can be costlier and may not be as optimized for structured extraction from PDFs. Custom Vision is for images, not documents.

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 12

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. Option A is correct because semantic search improves relevance by understanding natural language queries and providing answer-style results. Synonyms (B) help with query expansion but not natural language understanding. Knowledge mining (C) is a broader process. Cognitive skills (D) are for enrichment, not query-time interpretation.

Answer

Synonym maps

Answer

Cognitive skills

Answer

Knowledge mining

Question 13

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?

Accepted Answer

PII detection skill. Option D is correct because the PII detection skill identifies and redacts personal information from text. The Text Translation skill is not relevant. The Entity Recognition skill does not redact. The Custom Entity Lookup skill requires a predefined list and does not redact.

Answer

Custom Entity Lookup skill and Sentiment skill

Answer

Text Translation skill and Entity Recognition skill

Answer

Entity Recognition skill and Key Phrase Extraction skill

Question 14

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.. Option C is correct because you can use the Text Translation skill to translate content to English during indexing, and then index only the translated text. Option A would not translate. Option B only detects language. Option D uses two skills unnecessarily.

Answer

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

Answer

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

Answer

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

Question 15

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.. Option B is correct because by assigning a higher weight to the 'tags' field, search results that match tags will rank higher. Option A changes analyzers but doesn't address field weighting. Option C only affects the 'content' field. Option D is about scoring profiles but not specifically about field weighting.

Answer

Add a custom scoring profile based on freshness.

Answer

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

Answer

Enable semantic search on the 'content' field.