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

Question 1

You are designing a knowledge mining solution using Azure AI Search. The solution must process large volumes of PDFs daily. You need to minimize the cost of cognitive skills execution while ensuring the pipeline can handle transient failures. Which approach should you recommend?

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

Accepted Answer

Enable incremental enrichment on the indexer. Enabling incremental enrichment caches intermediate results and recovers from failures without re-processing unchanged documents, reducing cost. Option B is incorrect because disabling field mappings would break the pipeline. Option C is incorrect because increasing the number of replicas improves query performance, not indexing. Option D is incorrect because using a free tier is not feasible for large volumes.

Answer

Disable field mappings

Answer

Increase the number of replicas

Answer

Use the free tier for the indexer

Question 2

Your team is building a custom ChatGPT-like copilot using Azure AI Foundry that answers questions based on internal HR policies stored in SharePoint. The solution must retrieve only the most relevant documents to minimize token usage. Which Azure AI Search feature should you configure?

Accepted Answer

Semantic ranking. Semantic ranking is the correct feature because it uses deep neural networks to re-rank search results based on semantic relevance to the query, ensuring only the most contextually appropriate documents are returned. This directly minimizes token usage by reducing the number of irrelevant documents passed to the copilot, which is critical for cost and performance in a ChatGPT-like system.

Answer

Synonyms

Answer

Scoring profiles

Answer

Filters

Question 3

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. Azure AI Search indexers can be configured with a retry policy for custom skills. When a custom skill returns HTTP 429 (Too Many Requests), a retry policy will automatically retry the skill execution after a backoff delay, helping to handle rate limiting from the external API. Option A is incorrect: reducing the batch size may reduce the number of requests per batch, but if the API rate limit is per request, it may not resolve the 429 error. Option B is incorrect: increasing the skill timeout does not address rate limiting; it only allows more time for a single request. Option D is incorrect: scheduling the indexer less frequently only reduces the frequency of index runs, not the rate of requests during a run.

Answer

Reduce the batch size in the indexer

Answer

Increase the skill timeout

Answer

Schedule the indexer to run less frequently

Question 4

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. Azure AI Health Insights (formerly Text Analytics for Health) is a specialized service designed to extract healthcare-related entities such as diseases, medications, and treatment procedures from clinical text. Azure AI Language (option D) offers general entity extraction but lacks domain-specific models for healthcare. Azure AI Document Intelligence (option B) is optimized for extracting information from structured documents like forms and invoices, not clinical narratives. Azure AI Search (option C) is the indexing and querying service, not an extraction service. Therefore, Azure AI Health Insights is the appropriate choice to integrate with Azure AI Search for entity extraction from clinical notes.

Answer

Azure AI Document Intelligence

Answer

Azure AI Search

Answer

Azure AI Language

Question 5

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. Azure AI Search cannot index encrypted documents; the solution is to remove encryption before indexing. Option A is incorrect: the indexer does not require a specific PDF parser; it can index PDFs by default with the appropriate skillset. Option B is incorrect: unsupported file formats would generate a different error, and encryption is not a format issue. Option C is incorrect: file size limits exist (up to 16 MB for PDFs), but the error specifically mentions encryption, not size.

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 6

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. Azure AI Document Intelligence Layout model extracts text, tables, and their structure from documents, making it suitable for preserving table structure and building a searchable knowledge base. It can be used as a custom skill in an Azure AI Search enrichment pipeline. Option A is incorrect because uploading documents directly to Azure AI Search only indexes raw text without extraction or table structure preservation. Option B is incorrect because Custom Entity Extraction in Azure AI Language focuses on identifying entities, not extracting tables or preserving structure. Option D is incorrect because Azure AI Vision OCR extracts text but not table structure, and is not designed for document layout analysis.

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 7

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. The EntityRecognitionSkill outputs entities into a structured format (e.g., '/document/entities'). To populate the 'organizations' field in the index, you need to add a field mapping in the indexer that maps the skill output path (e.g., '/document/entities/organizations') to the index field. Without this mapping, the 'organizations' field remains empty. Option A is incorrect because the skill output path is typically correct; the issue is the missing field mapping. Option C is incorrect because the 'Organization' category is correctly spelled in the skill configuration. Option D is incorrect because the order of skills does not affect the output; entity recognition and key phrase extraction are independent.

Answer

The skill output path is incorrect

Answer

The 'Organization' category is misspelled

Answer

The skills must be in reverse order

Question 8

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. Correct options are A, D, and E. Custom skills must have an API endpoint reachable from the search service (public or via private endpoint) [A]. They must handle payloads up to 16 MB due to data size limits [D]. They must complete within 230 seconds (the default timeout) [E]. Option B is incorrect because custom skills can accept multiple inputs and produce multiple outputs. Option C is incorrect because custom skills can be written in any language that supports JSON.

Answer

The skill can only accept one input and produce one output

Answer

The skill must be written in Python

Question 9

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

Accepted Answer

PII Detection. The two built-in cognitive skills in Azure AI Search that extract entities from text are Entity Recognition and PII Detection. Entity Recognition extracts named entities such as people, organizations, and locations. PII Detection extracts personally identifiable information like phone numbers, email addresses, and social security numbers. Custom Entity Lookup is a custom skill, not built-in. Sentiment Analysis determines sentiment, and Language Detection identifies language; neither extracts entities.

Answer

Custom Entity Lookup

Answer

Sentiment Analysis

Answer

Language Detection

Question 10

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. 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 11

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 12

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. Using Azure AI Language entity extraction as a custom skill extracts attributes like color, size, and brand from product descriptions, and enabling fuzzy search in the index handles misspellings. Option A is incorrect because key phrase extraction identifies topics, not specific entities like color or brand, and vector search is for similarity matching, not fuzzy matching for typos. Option B is incorrect because Azure AI Document Intelligence is designed to extract text from documents (e.g., PDFs, images), not to extract named entities from text already stored in a database; semantic ranking improves relevance but does not perform entity extraction. Option D is incorrect because Azure AI Vision OCR extracts text from images, not from product descriptions in Cosmos DB, and synonyms expand queries but do not extract entities needed for filtering.

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 13

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 improves relevance by understanding natural language queries and providing answer-style results. Synonym maps (B) help with query expansion but not natural language understanding. Cognitive skills (C) are used for enrichment during indexing, not query-time interpretation. Knowledge mining (D) is a broader process that encompasses multiple services, not a specific feature of Azure AI Search.

Answer

Synonym maps

Answer

Cognitive skills

Answer

Knowledge mining

Question 14

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. The PII detection skill in Azure AI Search uses Azure AI Services to identify and redact personally identifiable information from text, which is exactly what is needed to de-identify sensitive patient data before indexing. The other options are incorrect: A) Custom Entity Lookup skill requires a predefined list and does not perform redaction, and Sentiment skill is irrelevant; C) Text Translation skill translates text, and Entity Recognition skill identifies entities but does not redact them; D) Entity Recognition skill identifies entities but does not redact, and Key Phrase Extraction skill extracts key phrases, neither of which de-identify data.

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 15

You are designing a knowledge mining solution using Azure AI Search. The solution must process large volumes of PDFs daily. You need to minimize the cost of cognitive skills execution while ensuring the pipeline can handle transient failures. Which approach should you recommend?

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

Accepted Answer

Use the Text Translation skill to translate all content to English during indexing.. 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.