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

Question 1

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. The Image Analysis skill (B) is correct because it invokes the Computer Vision service to extract visual features from embedded images, such as descriptions, tags, and celebrity or landmark detection, which is exactly the kind of image-derived information a knowledge mining pipeline needs. The OCR skill (C) is also correct because it extracts printed and handwritten text from image files (including images embedded in documents), producing text that can be mapped into the search index. The Entity Recognition skill (A) operates on text to identify entities like people, places, and organizations, not on image content. The Key Phrase Extraction skill (D) analyzes text to surface salient phrases and does not process images. The Text Translation skill (E) translates text between languages and likewise does not extract information from images.

Answer

Entity Recognition skill

Answer

Key Phrase Extraction skill

Answer

Text Translation skill

Question 2

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. Option B is correct because a custom classification model in Azure AI Document Intelligence is the required resource for identifying document type before extraction, since prebuilt models extract fields but do not perform custom document classification. Option C is correct because training a custom classification model requires a minimum of 5 labeled samples per document type, which provides the model with enough examples to distinguish invoices from purchase orders. Option D is correct because after classification, you must chain the classification model with the appropriate extraction models so each document is routed to the correct extraction model for field retrieval. Option A is incorrect because one sample per type is insufficient; the minimum is 5 labeled samples per document type. Option E is incorrect because prebuilt invoice and purchase order models are extraction models, not classification models, and they do not classify documents before extraction.

Answer

Train the classification model with one sample per type

Answer

Use the prebuilt invoice and purchase order models for classification

Question 3

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) is correct because it uses Microsoft's language understanding models to rerank results with semantic captions and answers, boosting relevance beyond keyword matching. Scoring profiles (D) are correct because they let you define custom ranking functions (e.g., boosting by freshness, magnitude, or tags) that directly influence the relevance score of returned documents. Synonym maps (A) expand queries with equivalent terms but only broaden matching, not rank relevance. Suggesters (C) enable type-ahead autocomplete, which improves query input rather than result relevance. Filterable fields (E) restrict the result set with OData filters but do not affect relevance ranking.

Answer

Synonym maps

Answer

Suggesters

Answer

Filterable fields

Question 4

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) provides prebuilt and custom models specifically designed to extract structured fields like invoice numbers, dates, and totals from scanned documents, including the prebuilt invoice model. It can be invoked programmatically on a schedule via Azure Functions, Logic Apps, or Data Factory, satisfying the no-manual-intervention requirement. This is the canonical Azure service for OCR-plus-field-extraction from forms and invoices.

Answer

Azure Bot Service

Answer

Azure AI Search with built-in skills

Answer

Azure AI Foundry model catalog

Question 5

You are designing a knowledge mining solution that ingests documents from SharePoint Online and makes them searchable using Azure AI Search. The solution must extract text from images and perform optical character recognition (OCR) on embedded images within PDFs. Which built-in skill should you include in the skillset?

Accepted Answer

OCR skill. The OCR skill (Optical Character Recognition) is the correct built-in skill for extracting text from images and performing OCR on embedded images within PDFs in Azure AI Search. It specifically handles image files (e.g., JPEG, PNG) and embedded images in PDFs, outputting text that can be indexed and searched. Other skills like translation, key phrase extraction, or entity recognition do not perform text extraction from images.

Answer

Translation skill

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Key phrase extraction skill

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Entity recognition skill

Question 6

Your organization has a large repository of technical manuals in PDF format. You need to build a chatbot that can answer questions about the content of these manuals. Which combination of Azure services should you use?

Accepted Answer

Azure AI Search and Azure OpenAI. Azure AI Search provides the indexing and retrieval capabilities needed to search through the PDF content, while Azure OpenAI (specifically GPT models) can generate natural language answers based on the retrieved passages. This combination enables a RAG (Retrieval-Augmented Generation) pattern where the search engine finds relevant text chunks from the manuals and the language model formulates a coherent answer.

Answer

Azure AI Speech and Azure OpenAI

Answer

Azure AI Language and Azure AI Document Intelligence

Answer

Azure AI Document Intelligence and Azure Bot Service

Question 7

You are implementing a knowledge mining solution using Azure AI Search. The data source is a large Azure Cosmos DB collection containing customer support tickets. Each ticket has fields: ticket_id, description, category, and resolution. You need to ensure that the search index can support fuzzy search and autocomplete suggestions. What should you configure in the index definition?

Accepted Answer

Set the 'searchable' attribute on the description field and define a suggester. Fuzzy search requires the 'searchable' attribute on fields to enable full-text search, and autocomplete suggestions require a 'suggester' configured on the index. The suggester defines which fields are used to generate suggestion candidates, and the 'searchable' attribute allows the description field to be tokenized and matched against partial or misspelled queries.

Answer

Set the 'filterable' attribute on the description field

Answer

Set the 'sortable' attribute on the ticket_id field

Answer

Set the 'facetable' attribute on the category field

Question 8

Your team is building a knowledge mining solution for research papers. You need to automatically categorize papers into topics and extract author names, publication dates, and references. The solution must use custom models because the papers are domain-specific. Which combination of Azure services should you use?

Accepted Answer

Azure AI Document Intelligence's custom extraction model and Azure AI Language's custom text classification. It combines Azure AI Document Intelligence's custom extraction model to extract domain-specific fields like author names, publication dates, and references, with Azure AI Language's custom text classification to categorize research papers into topics. This pairing directly addresses the need for custom models tailored to the specialized domain, unlike pre-built or generic solutions.

Answer

Azure AI Document Intelligence's pre-built invoice model and Azure Bot Service

Answer

Azure AI Search's built-in OCR skill and a custom skill using Azure Functions

Answer

Azure AI Language's pre-built entity extraction and Azure AI Search

Question 9

You are designing a solution to extract structured data from a large number of handwritten forms. The forms are scanned and stored as images. Which Azure AI feature should you use?

Accepted Answer

Azure AI Document Intelligence's OCR capability. Azure AI Document Intelligence's OCR capability is specifically designed to extract structured data from scanned documents, including handwritten forms. It uses advanced optical character recognition (OCR) and layout analysis to identify text, tables, and key-value pairs, making it the correct choice for this scenario.

Answer

Azure AI Vision's image analysis

Answer

Azure AI Speech to text

Answer

Azure Bot Service

Question 10

Which TWO actions should you take to optimize the performance of an Azure AI Search solution that indexes large volumes of data?

Accepted Answer

Use the appropriate search tier (S1, S2, etc.) based on document size. Option A is correct because Azure AI Search tiers (Basic, S1, S2, S3, etc.) differ in storage size, document limits, partitions, and indexer throughput, so selecting a tier that matches your document size and volume is essential for indexing performance. Option D is correct because the Azure AI Search indexing API supports batching up to 1000 documents (or about 16 MB of payload) per index operation, which amortizes network and service overhead and dramatically improves indexing throughput. Option B is wrong because the Free tier is for evaluation only, with strict limits on storage, indexes, and indexers, and is not suitable for production workloads. Option C is wrong because replicas provide high availability and increased query throughput, not indexing throughput; indexing scale is driven by partitions. Option E is wrong because scoring profiles affect query-time ranking, not indexing speed, and disabling them would not optimize indexing performance.

Answer

Use the free tier for production workloads

Answer

Increase the replica count for better indexing throughput

Answer

Disable scoring profiles to speed up indexing

Question 11

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?

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

Accepted Answer

Use language-specific analyzers in the Azure AI Search index for each language. Azure AI Search supports language-specific analyzers (e.g., 'de.microsoft' for German, 'fr.microsoft' for French) that apply linguistic rules such as stemming, lemmatization, and stop-word removal tailored to each language. This ensures that queries and documents are processed in the same language, enabling users to search and retrieve results in their native language without translation loss.

Answer

Detect the language of the query using Azure AI Language and then use a generic analyzer

Answer

Translate all queries to English using Azure AI Translator before searching

Answer

Use a single non-language-specific analyzer like 'standard.lucene' for all documents

Question 12

You are designing a knowledge mining solution for a medical research organization. The solution must extract relationships between drugs, diseases, and genes from scientific articles. The data will be stored in a knowledge graph for querying. Which Azure AI service should you use for the extraction?

Accepted Answer

Azure AI Language healthcare entity recognition and relation extraction. Azure AI Language's healthcare entity recognition and relation extraction is specifically designed to extract medical entities (drugs, diseases, genes) and their relationships from unstructured text, making it ideal for building a knowledge graph. This pre-built model uses deep learning trained on biomedical literature, directly supporting the required extraction without custom training.

Answer

Azure AI Search with semantic ranking

Answer

Azure AI Translator with dictionary lookup

Answer

Azure AI Document Intelligence custom extraction model

Question 13

Which THREE factors should you consider when designing a knowledge mining solution that uses Azure AI Search and custom skills to extract insights from large volumes of documents?

Accepted Answer

The maximum execution time of the custom skill must fit within the indexer timeout. Option B is correct because custom skills run inside the indexer execution pipeline, and each skill invocation must complete within the indexer's timeout limits (for example, the default HTTP timeout of 3 minutes 30 seconds for a WebApiSkill); a long-running custom skill will cause the indexer to fail or time out. Option C is correct because enabling incremental enrichment (by setting the indexer's data source change detection and using a high-water mark) lets Azure AI Search skip documents whose content has not changed, avoiding unnecessary re-invocation of expensive custom skills and reducing cost and processing time. Option E is correct because the indexer can invoke skills concurrently across documents, so a custom skill must not depend on shared mutable state or on a specific call order; making it stateless and idempotent ensures consistent, repeatable enrichment results under parallel execution. Option A is not a primary design factor here because knowledge store projections are an output concern and do not fundamentally constrain the design of the custom-skill enrichment pipeline in the way timeout, incremental enrichment, and statelessness do. Option D is incorrect because semantic ranking is configured on the search index/query side (semantic configuration), not as a required element of the skillset, so it is not a factor in designing the custom-skill extraction pipeline.

Answer

The number of knowledge store projections affects indexing speed

Answer

Semantic ranking configuration must be included in the skillset

Question 14

Which TWO configurations are required to enable Azure AI Search to index content from an Azure SQL database?

Accepted Answer

Enable change tracking on the Azure SQL table. Option C is correct because Azure AI Search's SQL indexer relies on change tracking (or a rowversion/timestamp column) to detect which rows have been inserted, updated, or deleted since the last indexing run, so enabling change tracking on the Azure SQL table is required for incremental indexing. Option D is correct because the indexer must be given a data source object that specifies the connection string, table or view, and change-tracking policy for the Azure SQL database, which is the mandatory link between the search service and the SQL data. Option A is not required because a skillset is only needed for AI enrichment (for example OCR, key phrase extraction, or embedding generation), not for basic SQL-to-index ingestion. Option B is not required because semantic ranking is an optional query-time feature that improves relevance; it does not affect whether content can be indexed. Option E is not required because high availability is a resilience/uptime configuration for the SQL database and is unrelated to the indexer's ability to read and index data.

Answer

Create a custom skillset for data enrichment

Answer

Configure semantic ranking on the index

Answer

Enable high availability on the Azure SQL database

Question 15

Which THREE Azure AI services can be used to extract text from images?

Accepted Answer

Azure AI Search. Option B (Azure AI Search) is correct because it includes AI enrichment with the OCR cognitive skill, which extracts text from image files (e.g., JPEG, PNG) during the indexing pipeline, making the text searchable. Option C (Azure AI Document Intelligence layout model) is correct because the layout model performs OCR on documents and images, extracting printed and handwritten text along with tables and structure from forms and files. Option D (Azure AI Vision OCR) is correct because the Read/OCR API in Azure AI Vision extracts printed and handwritten text from images and documents. Option A (Azure AI Speech) is incorrect because it handles speech-to-text, text-to-speech, and translation of audio, not text extraction from images. Option E (Azure AI Language custom NER) is incorrect because it extracts named entities from existing text, not text from images.

Answer

Azure AI Speech

Answer

Azure AI Language custom NER