AI-102 Implement knowledge mining and information extraction solutions Practice Test 9 — 15 Questions

Question 1

You have defined the custom WebApiSkill shown in the exhibit. The skill calls an Azure Function that can process up to 10 documents per second. However, you notice that the skill is failing with 429 errors. What is the most likely cause?

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

Accepted Answer

The degreeOfParallelism of 3 causes too many concurrent requests, exceeding the function's capacity. The `degreeOfParallelism` of 3 causes the AI Search enrichment pipeline to invoke the Azure Function with up to 3 concurrent batches, each of size 5, resulting in up to 15 documents per second. Since the function can only handle 10 documents per second, this exceeds its capacity and triggers HTTP 429 (Too Many Requests) errors.

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The timeout of 30 seconds is too short for the function to respond

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The batch size of 5 is too large, causing the function to receive too many documents at once

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The context '/document' is incorrect, causing all documents to be processed as one

Question 2

You are building a knowledge mining solution for legal documents using Azure AI Search. The solution must extract entities like dates, organizations, and persons from PDF files and index them. Which built-in skill should you add to the skillset to perform this extraction?

Accepted Answer

Named Entity Recognition skill. The Named Entity Recognition (NER) skill in Azure AI Search is specifically designed to extract entities such as dates, organizations, and persons from text. When added to a skillset, it processes the content extracted from PDF files and outputs structured entity information that can be indexed and queried. This directly matches the requirement to extract and index named entities from legal documents.

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Language Detection skill

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Optical Character Recognition (OCR) skill

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Key Phrase Extraction skill

Question 3

You are implementing a knowledge mining solution using Azure AI Search with a custom skillset. The custom skill is an Azure Function that enriches documents with additional metadata. You need to ensure that the custom skill receives the entire document content as input. How should you configure the skill's context and inputs?

Accepted Answer

Set context to '/document' and input source to '/document/content'.. To pass the entire document content to a custom skill, the skill's context must be set to '/document' (the root of each document in the enrichment tree) and the input source must be '/document/content'. Setting context to '/document' ensures the skill executes once per document, and mapping the input to '/document/content' delivers the full text content to the Azure Function. This is the correct configuration for document-level enrichment.

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Set context to '/document/content' and input source to '/document/metadata'.

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Set context to '/document/content' and input source to '/document/content'.

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Set context to '/document' and input source to '/document/normalized_images/*'.

Question 4

You need to extract key-value pairs from scanned forms as part of a knowledge mining solution. Which Azure AI service should you use?

Accepted Answer

Azure AI Document Intelligence. Azure AI Document Intelligence (formerly Form Recognizer) is the correct service because it is specifically designed to extract key-value pairs, tables, and structured data from scanned forms and documents using prebuilt and custom models. This aligns directly with the requirement for knowledge mining from scanned forms.

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Azure AI Vision

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Azure AI Language

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Azure AI Search

Question 5

You are using Azure AI Search to build a knowledge base for a customer support portal. The index includes a 'sentiment' field that should be populated using the Sentiment skill. However, the sentiment scores are not being written to the index. The skillset runs successfully. What is the most likely cause?

Accepted Answer

The output field mapping for 'sentiment' is missing or incorrectly defined in the indexer.. The Sentiment skill outputs a 'double' value for sentiment score, but the indexer requires an explicit output field mapping to write that value into the index's 'sentiment' field. Even when a skillset runs successfully, without a correct output field mapping in the indexer definition, the skill's output is not transferred to the index. The indexer's field mappings control how enriched data flows from the skillset's output nodes to the index fields.

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The Sentiment skill is not correctly configured in the skillset.

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The indexer is in a failed state and not processing documents.

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The sentiment field in the index is of type 'Collection(Edm.String)' but the skill outputs a double.

Question 6

Which THREE components are required to build a custom skill for Azure AI Search enrichment?

Accepted Answer

A web API endpoint that accepts JSON input and returns JSON output.. To build a custom skill for Azure AI Search enrichment, you must expose your logic through a web API endpoint that accepts JSON input and returns JSON output (C), because the skillset invokes the skill via an HTTP POST with a JSON payload and expects a JSON response. The endpoint must be secured with HTTPS (D), since Azure AI Search requires custom skill connections to use HTTPS for secure transport. You also need a JSON schema defining inputs and outputs (E), which describes the expected input fields and output fields so the skillset can map enriched document content correctly. A database for intermediate results (A) is not required, as the skillset pipeline passes data between skills in memory, and a Power Automate flow (B) is not a supported orchestration mechanism for custom skills.

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A database to store intermediate results.

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A Power Automate flow to orchestrate the skill.

Question 7

Your organization is using Azure AI Search to index a large collection of PDF documents stored in Azure Blob Storage. The index currently returns search results, but users complain that the results are not relevant when they search using natural language phrases. You need to improve the relevance of search results without rewriting the application. What should you do?

Accepted Answer

Enable semantic search on the index and configure a semantic configuration.. Semantic search in Azure AI Search uses advanced language models to understand the intent behind natural language queries, re-ranking results based on semantic relevance rather than just keyword matching. Enabling semantic search and configuring a semantic configuration directly addresses the user complaint about poor relevance for natural language phrases without requiring application changes.

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Increase the number of replicas for the search service to improve query performance.

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Create a new index with a blob indexer that uses the 'content' field only.

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Configure a custom analyzer on the index to handle stop words and synonyms.

Question 8

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.

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

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Convert the invoices to a standard format before processing.

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Use the prebuilt invoice model with confidence threshold adjustment.

Question 9

You are designing an enterprise search solution using Azure AI Search. The solution must index data from multiple sources: SQL Database, SharePoint Online, and custom REST APIs. The search index must support faceted navigation and filtering by metadata such as department and document type. You also need to ensure that updates to source data are reflected in the index within 5 minutes. Which approach should you use?

Accepted Answer

Configure indexers for SQL and SharePoint, and use the push API for the REST API. Schedule indexers to run every 5 minutes.. It combines the strengths of indexers (for SQL Database and SharePoint Online, which have native connectors) with the push API for custom REST APIs, which lack a built-in indexer. Scheduling the indexers to run every 5 minutes ensures that updates are reflected within the required latency window, while the push API can be triggered on demand or via a polling mechanism to meet the same 5-minute SLA.

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Use the push API to index all data from a custom application that polls all sources.

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Create a single indexer that reads from all three sources using a data source definition.

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Use only indexers for all sources by creating a custom indexer for the REST API.

Question 10

Your company uses Azure AI Search to power a customer support portal. The search index includes product documentation and known issues. Recently, the portal's search performance has degraded, and users report slow response times. You need to identify the cause of the performance issue. What should you check first?

Accepted Answer

Review the search service metrics for high query latency and CPU usage.. High query latency and CPU usage are direct indicators of performance bottlenecks in Azure AI Search. The search service metrics in the Azure portal provide real-time data on query execution time and resource consumption, which are the first signals to investigate when users report slow response times. Checking these metrics helps identify whether the issue stems from excessive query load, insufficient replicas, or inefficient query execution.

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Check the size of the index storage in the Azure portal.

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Ensure the index schema does not have too many fields.

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Verify that the skillset is not running during peak hours.

Question 11

You are implementing a knowledge mining solution for a legal firm. The solution must ingest large volumes of legal documents (PDFs and Word files) stored in Azure Blob Storage. You need to extract text, recognize named entities (e.g., parties, judges, case numbers), and index the content for full-text search. The solution should also support redaction of sensitive information before indexing. Which combination of Azure AI services should you use?

Accepted Answer

Azure AI Document Intelligence, Azure AI Language, custom skill for redaction, and Azure AI Search. It combines Azure AI Document Intelligence for OCR and text extraction from PDFs and Word files, Azure AI Language for named entity recognition (e.g., parties, judges, case numbers), a custom skill for redaction (to remove sensitive information before indexing), and Azure AI Search to index the cleaned content for full-text search. This stack directly addresses all requirements: ingestion, entity extraction, redaction, and search indexing.

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Azure AI Document Intelligence, Azure AI Translator, and Azure AI Search

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Azure AI Document Intelligence, Azure AI Video Indexer, and Azure AI Search

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Azure AI Document Intelligence, Azure AI Content Safety, and Azure AI Search

Question 12

You are deploying a knowledge mining solution using Azure AI Search and Azure AI Document Intelligence. The solution must extract text from scanned documents, identify named entities, and index the content. You need to configure the skillset. Which TWO built-in skills should you include in the skillset?

Accepted Answer

OCR skill. The OCR skill (option C) is correct because it is the built-in Azure AI Search cognitive skill that invokes Azure AI Document Intelligence's Read/OCR capability to extract text from scanned documents and images, which is exactly what the scenario requires for scanned input. The EntityRecognition skill (option D) is correct because it is the built-in skill that calls the Text Analytics entity recognition model to identify named entities (people, organizations, locations, dates, and so on) in the extracted text, satisfying the named-entity requirement. The Merge skill (option A) only combines text and offsets from multiple input fields and does not perform OCR or entity extraction, so it is not required here. The LanguageDetection skill (option B) merely detects the language of the text and is not needed to extract text or identify entities. The KeyPhraseExtraction skill (option E) extracts key phrases rather than named entities, so it does not meet the entity-identification requirement.

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Merge skill

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LanguageDetection skill

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KeyPhraseExtraction skill

Question 13

You are using Microsoft Purview to create a knowledge map of your organization's data assets. The solution must automatically scan and classify sensitive data in Azure Blob Storage. You need to configure the scanning and classification. Which THREE actions should you perform?

Accepted Answer

Run a full scan of the Blob Storage to discover and classify data.. Option E is correct because before Microsoft Purview can scan any asset, the Azure Blob Storage account must first be registered as a data source in the Purview governance portal, which establishes the connection and allows the account to be managed and scanned. Option D is correct because a scan rule set defines which file types and classification rules are applied during a scan; you must create or select a rule set that includes the desired system or custom classification rules so sensitive data types are detected. Option A is correct because after registering the source and configuring the rule set, you must run a scan (a full scan for initial discovery and classification) so Purview can crawl the Blob Storage, apply the rule set, and populate the knowledge map with classified assets. Option B is not required because Purview provides built-in system classification rules for common sensitive data types, and custom rules are only needed for organization-specific patterns, which the scenario does not require. Option C is not part of the scanning and classification configuration; sensitivity labels are applied through Microsoft Purview Information Protection and are a separate labeling concern from the data map scanning process.

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Create custom classification rules for sensitive data types.

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Apply sensitivity labels to the classified data.

Question 14

You are building a knowledge mining solution for legal documents stored in Azure Blob Storage. The solution must extract entities, key phrases, and relationships from the documents. Which Azure AI service should you use?

Accepted Answer

Azure AI Language. Azure AI Language provides pre-built capabilities for entity extraction, key phrase extraction, and relationship extraction from text. This service includes features like Named Entity Recognition (NER), key phrase extraction, and document analysis that directly meet the requirements for extracting entities, key phrases, and relationships from legal documents. The other options lack one or more of these specific text analytics capabilities.

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Azure AI Document Intelligence

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Azure AI Translator

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Azure AI Search

Question 15

You have the above skillset in Azure AI Search. The indexer processes a document with 12,000 characters of content. How many entity recognition skill executions occur?

Accepted Answer

3. The Azure AI Search entity recognition skill has a maximum text length per execution of 5,000 characters. A document with 12,000 characters is split into chunks of up to 5,000 characters, resulting in three chunks (5,000 + 5,000 + 2,000). Each chunk triggers one skill execution, so three executions occur.

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4

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2

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1