AI-102 Implement knowledge mining and information extraction solutions Practice Test 6 — 15 Questions

Question 1

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?

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

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 2

Which TWO configurations are required to enable incremental enrichment in Azure AI Search?

Accepted Answer

Define a projection in the skillset to store enriched data.. Defining a projection in the skillset is required to store enriched data in a knowledge store, which is a prerequisite for incremental enrichment. Incremental enrichment relies on caching enriched content so that only changed documents are reprocessed, and projections define how that enriched data is stored in Azure Storage for later reuse.

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Configure the indexer to run in 'once' mode.

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Enable blob metadata extraction in the indexer.

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Add a custom skill that outputs a hash of the document content.

Question 3

Which TWO Azure AI services can be used together to build a knowledge mining solution that extracts text from handwritten notes and indexes them for search?

Accepted Answer

Azure AI Search. Azure AI Search (A) is the correct service because it provides the indexing and querying capabilities required for a knowledge mining solution. Azure AI Vision's Read API (C) is correct because it extracts printed and handwritten text from images, which is the first step in making handwritten notes searchable. Together, they form a pipeline where the Read API extracts text, and Azure AI Search indexes that extracted text for full-text search.

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Azure AI Document Intelligence

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Azure AI Language

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Azure AI Translator

Question 4

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.. A custom skill in Azure AI Search must be implemented as a web API that receives a JSON payload containing the input fields defined in the skill's context and returns a JSON response with the output fields. This API is called by the AI Search enrichment pipeline during indexing, allowing you to inject custom logic (e.g., entity extraction, classification) into the skillset execution.

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A database to store intermediate results.

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A Power Automate flow to orchestrate the skill.

Question 5

You have the above Azure AI Search skillset. The indexer fails with the error 'The skill 'sentiment-skill' cannot find the input '/document/pages/*' because the path does not exist.' What is the most likely cause?

Accepted Answer

The SplitSkill did not split the content because the document content is too short.. The error indicates that the input path '/document/pages/*' does not exist. This path is generated by the SplitSkill when it splits the document content into pages. If the SplitSkill does not produce any output, the path will not exist. The most likely cause is that the document content is too short to be split, so no pages are created. Therefore, option A is correct. Option B is incorrect because the SplitSkill is defined. Option C is incorrect because the SentimentSkill's input path is correct, but the source is missing. Option D is incorrect because the error is about a missing path, not a misspelled targetName.

Answer

The SplitSkill is not defined in the skillset.

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The SentimentSkill is missing a required input.

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The targetName in SplitSkill is misspelled.

Question 6

You have the above Azure AI Search indexer definition. The indexer runs successfully but the 'content' field in the index is empty for all documents. What is the likely cause?

Accepted Answer

The outputFieldMapping references '/document/content' which is not produced by the skillset.. The indexer runs successfully but the 'content' field is empty because the outputFieldMapping maps a path '/document/content' that does not exist in the enrichment tree produced by the skillset. The skillset must output a node at that path; if it doesn't, the mapping cannot populate the field. Option A correctly identifies this as the likely cause.

Answer

The indexer schedule is too frequent.

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The fieldMapping for 'metadata_storage_path' is incorrect.

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The batchSize is too large, causing some items to fail silently.

Question 7

You have the above data source definition for Azure AI Search. You want to index only PDF files from the 'documents' container. How should you modify the data source?

Accepted Answer

Set the 'query' field to a blob prefix that corresponds to the folder containing PDF files.. Azure AI Search data sources support a 'query' property that accepts a blob prefix or virtual directory path. By setting 'query' to the folder path containing PDFs (e.g., 'documents/pdfs/'), the indexer will only process blobs under that prefix, effectively filtering to PDF files without moving or renaming containers.

Answer

Change the container name to 'pdfs' and move all PDFs there.

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Add a 'fileExtension' property to the container object.

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Change the connection string to use a different storage account that contains only PDFs.

Question 8

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.

Answer

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 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.

Answer

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

You are building a knowledge mining solution using Azure AI Search and Azure AI Language. The solution must extract key phrases, entities, and sentiment from customer feedback documents. After processing, the enriched content should be stored in the search index for full-text search. You need to configure the enrichment pipeline. Which two Azure AI services should you integrate?

Accepted Answer

Azure AI Language and Azure AI Search. The solution requires key phrase extraction, entity recognition, and sentiment analysis, which are all capabilities of Azure AI Language. Azure AI Search is the other required service because it provides the search index and the enrichment pipeline (skillset) that invokes the AI Language skills to process documents and store the enriched content for full-text search.

Answer

Azure AI Language and a custom skill in Azure Functions

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Azure AI Translator and Azure AI Search

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Azure AI Document Intelligence and Azure AI Search

Question 11

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.

Answer

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 12

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 13

Your organization uses Microsoft Syntex to automatically classify and extract metadata from documents stored in SharePoint. You need to extend this capability to also extract entities such as invoice numbers and dates from PDF invoices that are uploaded to SharePoint. What should you do?

Accepted Answer

Create a document understanding model in Syntex that extracts entities from invoices.. Microsoft Syntex document understanding models can classify documents and extract entities such as invoice numbers and dates. Option A is incorrect because Syntex does not use AI Builder's out-of-the-box entity extraction; it uses its own model training. Option B is incorrect because Azure AI Search is a search service, not an entity extraction service integrated with Syntex. Option C is incorrect because Power Automate with AI Builder is a separate workflow automation approach, not part of Syntex. Therefore, option D is correct: create a document understanding model in Syntex that extracts entities from invoices.

Answer

Create a custom entity extraction model in Syntex using AI Builder.

Answer

Integrate Azure AI Search with SharePoint to extract entities.

Answer

Use Power Automate with AI Builder to extract entities from invoices.

Question 14

You are building a chatbot using Microsoft Copilot Studio that needs to answer questions based on content from a set of technical manuals stored as PDFs. The content must be indexed and made available to the chatbot. You need to configure the knowledge source for the chatbot. What should you do?

Accepted Answer

Create an Azure AI Search index from the PDFs and connect it to Copilot Studio as a knowledge source.. Azure AI Search provides a dedicated indexing service that can extract and store text from PDFs, enabling Copilot Studio to perform semantic search and retrieval-augmented generation (RAG). This allows the chatbot to answer questions based on the specific content of the technical manuals, rather than relying on external or unstructured data sources.

Answer

Upload the PDFs to a SharePoint library and use SharePoint as the knowledge source.

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Add a web search connector to the chatbot to search for information online.

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Store the content in Azure SQL Database and use it as a knowledge source.

Question 15

You are designing an Azure AI Search solution that indexes customer support tickets from a SQL database and also from a custom CRM system via a custom API. The index must support scoring profiles that boost tickets from VIP customers. The VIP status is stored in the CRM system. You need to ensure that the boost factor is applied correctly. What should you do?

Accepted Answer

Include the VIP status field in the index by pushing it from the CRM system via the push API, then use a scoring profile with a field boost.. The VIP status must be present in the index for a scoring profile to use it. By pushing the VIP status field from the CRM system via the Push API, you include it in the index. Then, a scoring profile with a field boost can apply a higher score to documents where the VIP field matches a specific value, ensuring the boost is applied at query time without external dependencies.

Answer

Use a scoring profile with a tag boost that references a field in the SQL database.

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Use a scoring profile with a function that queries the CRM system at query time.

Answer

Configure a custom analyzer that boosts VIP terms.