AI-102 Implement knowledge mining and information extraction solutions — Timed 10 Questions

Question 1

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?

10 minute time limit — choose an answer to begin.

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.

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Azure AI Search with semantic ranking

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Azure AI Translator with dictionary lookup

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Azure AI Document Intelligence custom extraction model

Question 2

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. Azure AI Search imposes a timeout on indexer execution, and any custom skill invoked during the enrichment pipeline must complete within that timeout. If the custom skill exceeds the maximum execution time (e.g., 3 minutes for built-in skills or a configurable limit for custom skills), the indexer will fail, causing the entire indexing run to abort. This is a critical design constraint when processing large volumes of documents.

Answer

The number of knowledge store projections affects indexing speed

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Semantic ranking configuration must be included in the skillset

Question 3

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. Azure AI Search requires change tracking to be enabled on the Azure SQL table to support incremental indexing. Without change tracking, the indexer would have to re-read the entire table on each run, which is inefficient for large datasets. Change tracking allows the indexer to detect only new, updated, or deleted rows since the last indexing cycle.

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Create a custom skillset for data enrichment

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Configure semantic ranking on the index

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Enable high availability on the Azure SQL database

Question 4

You have an Azure AI Search solution that indexes customer support tickets. The index includes a 'category' field that should be automatically populated using a custom skill that calls an Azure Machine Learning model. However, the skill fails intermittently with HTTP 429 errors. What is the most likely cause and the best fix?

Accepted Answer

The skill execution batch size is too large, causing high call volume; reduce batch size in the skillset definition.. HTTP 429 errors indicate rate limiting, which occurs when the custom skill sends too many requests to the Azure Machine Learning endpoint in a short period. The skill execution batch size controls how many documents are processed per skill invocation; a large batch size increases call volume and triggers throttling. Reducing the batch size in the skillset definition lowers the request rate, preventing 429 errors.

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The skill has a timeout set too short; increase the timeout.

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The indexer runs too frequently; increase the indexer schedule interval.

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The skill output field mapping is incorrect; fix the mapping.

Question 5

Your organization uses Microsoft Purview to catalog data assets. You need to enable knowledge mining on these assets to allow users to search across structured and unstructured data. Which integration should you use to connect Microsoft Purview with Azure AI Search?

Accepted Answer

Create an Azure AI Search indexer that connects to Microsoft Purview's data source using the Purview REST API.. Azure AI Search can use an indexer to connect to Microsoft Purview's Atlas REST API, which allows it to crawl and ingest metadata (e.g., table schemas, file types, classifications) from Purview's data catalog. This integration enables knowledge mining by making both structured and unstructured data assets searchable through a unified Azure AI Search index, without requiring manual metadata push or intermediate tools.

Answer

Configure Microsoft Purview to push metadata directly to an Azure AI Search index.

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Use Microsoft Foundry's built-in connector to import Purview metadata.

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Use Power BI to export Purview metadata to Azure AI Search.

Question 6

Your company deploys an Azure AI Document Intelligence solution to extract data from invoices. During testing, you notice that some fields are not being extracted correctly, especially for invoices from a specific vendor with a non-standard layout. You need to improve extraction accuracy for this vendor's invoices. What should you do?

Accepted Answer

Train a custom model using labeled samples of the vendor's invoices.. Training a custom model using labeled samples of the vendor's invoices allows Document Intelligence to learn the non-standard layout, improving extraction accuracy. Option A is incorrect because while OCR and regex can extract text, they are not effective for structured data extraction from variable layouts. Option B is incorrect because converting invoices to a standard format is time-consuming and may lose important data; it's better to train a model on the actual invoices. Option D is incorrect because the prebuilt invoice model is designed for standard invoice layouts; adjusting confidence thresholds won't fix extraction accuracy for non-standard formats.

Answer

Enable OCR on the documents and use regular expressions to extract fields.

Answer

Convert the invoices to a standard format before processing.

Answer

Use the prebuilt invoice model with confidence threshold adjustment.

Question 7

You are building an Azure AI Search index that includes content from a SQL database and an Azure Cosmos DB collection. The index must support autocomplete suggestions as users type. Which TWO configurations are required to enable autocomplete?

Accepted Answer

Define a suggester in the index definition.. A suggester must be defined in the index schema to enable autocomplete. The suggester specifies which fields are used to generate suggestions and the suggestion mode (e.g., 'analyzingInfixMatching' for autocomplete). Without a suggester, the autocomplete API has no source of candidate terms to match against user input.

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Enable CORS on the search service.

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Configure a custom analyzer for the fields used in autocomplete.

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Create a scoring profile to boost relevant suggestions.

Question 8

Which TWO Azure AI Search features are used to map skill outputs to search index fields? (Select TWO.)

Accepted Answer

outputFieldMappings. In Azure AI Search, the two features used to map skill outputs to search index fields are outputFieldMappings and skillset outputs. outputFieldMappings explicitly define how outputs from cognitive skills are mapped to fields in the search index. Skillset outputs are the definitions within the skillset that specify which outputs each skill produces; these outputs are then referenced by outputFieldMappings. Field mappings (fieldMappings) are used to map source data fields, not skill outputs. Indexer parameters control indexer behavior but do not map skill outputs. Index fields are the destination fields in the index, not a mapping feature.

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indexer parameters

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fieldMappings

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index fields

Question 9

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?

10 minute time limit — choose an answer to begin.

Accepted Answer

Use Azure AI Search with a custom skill to extract the governing law field. Azure AI Search custom skills allow you to invoke external processing (e.g., a custom model or logic) to extract specific fields like 'governing law' from PDF content. Since the contracts are stored as PDFs in Blob Storage, you would first use OCR or text extraction (via built-in skills) to get raw text, then a custom skill to parse and extract the structured field. This enables precise queries such as 'find all contracts where the governing law is New York' using a filter on that extracted field.

Answer

Use Azure AI Search with vector search

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Use Azure AI Search with a built-in skillset for OCR

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Use Azure AI Search with full-text search on the extracted text

Question 10

You are using Azure AI Document Intelligence to extract data from purchase orders. The purchase orders have a table of line items. Which prebuilt model should you use?

Accepted Answer

Prebuilt invoice model. The prebuilt invoice model is optimized for extracting line-item tables from financial documents such as purchase orders. The prebuilt document model extracts general key-value pairs and tables but lacks semantic understanding of line items. The custom model is not a prebuilt model. The layout model extracts only structural elements like tables and text, not semantic data.

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Prebuilt document model

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Layout model

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Custom model