AI-102 Implement knowledge mining and information extraction solutions Practice Test 2 — 15 Questions

Question 1

You are building a knowledge mining solution to extract insights from a large set of PDF contracts. The solution must identify parties, dates, and monetary amounts. Which Azure AI service should you use as the primary extraction engine?

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

Accepted Answer

Azure AI Document Intelligence. Azure AI Document Intelligence (formerly Form Recognizer) is the correct choice because it is specifically designed for document analysis and extraction of structured data such as parties, dates, and monetary amounts from PDFs and images. Its prebuilt models (e.g., the 'prebuilt-invoice' or 'prebuilt-contract' model) use optical character recognition (OCR) and deep learning to extract key-value pairs and entities from contracts without requiring custom training.

Answer

Azure AI Language (custom NER)

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Azure AI Search with integrated vectorization

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Azure OpenAI Service with GPT-4o

Question 2

Your team has built a knowledge mining pipeline using Azure AI Search and Document Intelligence. After ingestion, you notice that some documents are not appearing in search results. What is the most likely cause?

Accepted Answer

The indexer encountered errors and marked the documents as failed. If the indexer encounters errors while processing specific documents (e.g., unsupported formats, parsing failures, or skill errors), those documents are marked as failed and not indexed. This explains why only some documents are missing from search results. Option B is incorrect because semantic configuration affects ranking and relevance features, not whether documents are indexed. Option C is incorrect because insufficient replicas affect query performance and availability, not document ingestion. Option D is incorrect because throttling during high query volume would impact all search queries, not the indexing of specific documents.

Answer

The index does not have a semantic configuration

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The search service has insufficient replicas

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The search service is throttled due to high query volume

Question 3

You are designing a knowledge mining solution that must extract entities from scanned handwritten forms. The forms contain signatures and checkboxes. Which combination of Azure AI services should you recommend?

Accepted Answer

Azure AI Document Intelligence with a custom neural model and Azure AI Language for entity linking. The scenario requires extracting entities from scanned handwritten forms, which demands a custom neural model in Azure AI Document Intelligence to handle the variability of handwriting, signatures, and checkboxes. Azure AI Language's entity linking then enriches the extracted entities by disambiguating and linking them to a knowledge base, providing structured, meaningful output. The premade models in Document Intelligence are designed for printed text and common layouts, not handwritten content, making a custom neural model essential.

Answer

Azure AI Document Intelligence with a premade model and Azure AI Computer Vision

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Azure AI Computer Vision (OCR) and Azure AI Search with integrated vectorization

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Azure Cognitive Search and Azure AI Document Intelligence with a premade model

Question 4

Your knowledge mining solution uses Azure AI Search. Users complain that search results are not relevant. You have enabled semantic search but results still lack context. What should you do to improve relevance?

Accepted Answer

Ensure the index includes a semantic configuration with title and content fields. Semantic search in Azure AI Search requires a semantic configuration that explicitly maps the title and content fields to be used for semantic ranking. Without this configuration, the search engine cannot apply the deep neural network models that understand context and intent, so results remain based on keyword matching even when the semantic search feature is enabled.

Answer

Increase the number of partitions to handle more data

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Configure a scoring profile with boosting based on metadata

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Increase the number of replicas to improve query performance

Question 5

You need to extract key-value pairs from a large set of invoices. The invoices have a consistent layout but vary in format (PDF, TIFF). Which Document Intelligence model should you use?

Accepted Answer

Premade invoice model. The premade invoice model (D) is specifically designed to extract key-value pairs from invoices, including fields like invoice date, total amount, and vendor details, even when the invoices vary in format (PDF, TIFF). It leverages pre-trained deep learning models optimized for invoice layouts, making it the most efficient choice for this task without requiring custom training.

Answer

Custom extraction model

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Layout model

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Read model

Question 6

Your knowledge mining solution ingests documents from multiple tenants. Each tenant's data must be isolated and searchable only by that tenant. You have a single Azure AI Search service. How should you implement multi-tenancy?

Accepted Answer

Use a single index with a tenant ID field and filter queries by that field. Azure AI Search supports multi-tenancy within a single service by using a shared index with a tenant ID field. Each document is tagged with a tenant identifier, and queries are scoped using OData `$filter` expressions (e.g., `$filter=tenantId eq 'tenant123'`). This ensures data isolation while keeping costs low and management simple, as only one search service and one index are needed.

Answer

Use separate skillsets for each tenant

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Create a separate search service for each tenant

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Use separate data sources within the same index

Question 7

You are using Azure AI Search to index customer support tickets. You want to automatically extract the customer's sentiment and key phrases from each ticket. Which Azure AI service should you integrate as a skillset?

Accepted Answer

Azure AI Language. Azure AI Language provides pre-built capabilities for sentiment analysis and key phrase extraction, which are exactly the skills needed to process customer support ticket text. When integrated as a skillset in Azure AI Search, it enriches the indexing pipeline by automatically extracting these insights from each document. The other services focus on different modalities (vision, translation, document structure) and do not offer native sentiment or key phrase extraction.

Answer

Azure AI Document Intelligence

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Azure AI Computer Vision

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Azure AI Translator

Question 8

Your organization has a knowledge base of technical manuals in PDF format. You need to enable users to ask natural language questions and get answers from the manuals. Which solution should you build?

Accepted Answer

Azure AI Language custom question answering with the documents as sources. Azure AI Language custom question answering is specifically designed to ingest documents (like PDFs) and provide a natural language Q&A interface over them. It uses a built-in extractive reader to find answer spans directly from the source text, making it the most straightforward solution for answering questions from a static knowledge base of technical manuals without requiring additional search or vectorization infrastructure.

Answer

Azure AI Search with integrated vectorization and semantic search

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Azure OpenAI Service with GPT-4o and Azure AI Search as a data source

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Azure AI Document Intelligence to extract text and then use Azure AI Search

Question 9

Your knowledge mining pipeline uses Azure AI Search with a custom skillset that calls an Azure Function. The function sometimes times out for large documents. What is the best way to handle this?

Accepted Answer

Increase the function timeout and ensure the function is in the same region as the search service. Increasing the function timeout (up to the Azure Functions maximum of 10 minutes for the Consumption plan or 30 minutes for the Premium plan) and ensuring the function is in the same region as the search service reduces latency and network overhead, directly addressing timeout issues for large documents. Azure AI Search custom skills must complete within the function's configured timeout, and regional colocation minimizes cross-region data transfer delays.

Answer

Use Azure AI Document Intelligence instead of a custom skill

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Set the function timeout to the maximum of 24 hours

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Move the custom skill to Azure AI Language custom entity recognition

Question 10

Which TWO actions should you take to ensure that an Azure AI Search indexer can access data from an Azure Storage account that contains sensitive data?

Accepted Answer

Configure the search service to use a system-assigned managed identity. To ensure that an Azure AI Search indexer can access data from an Azure Storage account containing sensitive data, you should use a system-assigned managed identity for the search service (Option C) and allow the search service's IP address in the storage account firewall (Option D). Using a managed identity eliminates the need for keys and provides secure authentication. Allowing the search service's IP ensures that the indexer can connect through the firewall. Option A is incorrect because a private endpoint is for accessing the storage from a virtual network, not for indexer access. Option B is incorrect because shared access keys are less secure and expose the storage account. Option E is incorrect because disabling the firewall entirely is insecure.

Answer

Create a private endpoint in the storage account for the search service

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Use a shared access key (SAS) in the data source definition

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Disable the storage account firewall entirely

Question 11

Which THREE components are required to build a knowledge mining solution using Azure AI Search that extracts and enriches content from PDF files?

Accepted Answer

Skillset. To build a knowledge mining solution that extracts and enriches content from PDF files using Azure AI Search, three components are required: a data source (to connect to the PDFs), a skillset (to apply AI enrichment like OCR and entity extraction), and an indexer (to orchestrate the pipeline and ingest the enriched content into the index). While an index is also necessary to store the results, it is not considered a pipeline component in the same context, and the question asks for the three components of the extraction and enrichment pipeline.

Answer

Index

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Semantic configuration

Question 12

Which TWO Azure AI services can be used to extract text from images as part of a knowledge mining pipeline?

Accepted Answer

Azure AI Document Intelligence. Azure AI Document Intelligence (formerly Form Recognizer) is correct because it is specifically designed to extract text, tables, and key-value pairs from scanned documents and images using optical character recognition (OCR) and deep learning models. It is a core service for knowledge mining pipelines that require structured data extraction from unstructured documents.

Answer

Azure AI Language

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Azure AI Video Indexer

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Azure AI Custom Vision

Question 13

You are reviewing a skillset definition for an Azure AI Search indexer. The indexer is configured to index 1000 PDF documents. After running the indexer, you notice that only 500 documents have sentiment scores. What is the most likely cause?

Accepted Answer

The SplitSkill outputs are not correctly mapped to the SentimentSkill input; the skill runs only on the first page of each document. The SplitSkill divides each PDF into pages, and by default the SentimentSkill runs in the context of each page (e.g., /document/pages/*). If the SplitSkill outputs are not correctly mapped to the SentimentSkill input, the sentiment skill may only process the first page or fail to receive the split content, resulting in sentiment scores for only a subset of documents. Correct mapping ensures that each page's content is passed to the SentimentSkill for analysis.

Answer

The skills are defined in the wrong order; sentiment should run before split

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The SentimentSkill is not supported in this region

Answer

The context of the SentimentSkill should be "/document" instead of "/document/pages/*"

Question 14

You are reviewing an index definition created with PowerShell. The index is used for a knowledge mining solution that extracts people and organizations from documents. Users report that when they type partial names in the search bar, the suggester does not return suggestions. What is the most likely reason?

Accepted Answer

The suggester sourceFields do not include people or organizations. A suggester in Azure Cognitive Search only returns suggestions for fields explicitly listed in its `sourceFields` property. If the `people` and `organizations` fields are not included in `sourceFields`, the suggester cannot match partial names typed in the search bar, even if those fields are indexed and searchable. The suggester relies on prefix matching against the specified source fields to generate suggestions.

Answer

The people and organizations fields should be Edm.String instead of Collection(Edm.String)

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The id field is not defined as a key in the index

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The suggester searchMode should be 'analyzingInfixMatching' which is incorrect

Question 15

You review the configuration for an Azure AI Search indexer. The indexer runs successfully but no documents are indexed. What is the most likely cause?

Accepted Answer

The indexer does not have a skillset or field mappings to populate the 'content' field from the blob. When an Azure AI Search indexer runs successfully but indexes zero documents, the most common cause is that the indexer lacks a skillset or field mappings to extract and map the blob content (e.g., the 'content' field) into the search index. Without explicit field mappings or a skillset that defines how to populate the index fields from the blob's default properties, the indexer may complete execution without transferring any data into the index fields.

Answer

The connection string in the data source is invalid

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The container name is incorrect

Answer

The maxFailedItems parameter is set to -1, which causes the indexer to skip all documents