AI-102 Implement knowledge mining and information extraction solutions — 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?

Accepted Answer

Azure AI Document Intelligence. Option C is correct because Document Intelligence (formerly Form Recognizer) is specialized in extracting structured fields from documents. Option A is wrong because Azure AI Search is for indexing and searching, not extraction. Option B is wrong because Azure OpenAI can extract entities but is not the most cost-effective for this specific scenario. Option D is wrong because AI Language is for text analytics but not optimized for document layout analysis.

Answer

Azure AI Language (custom NER)

Answer

Azure AI Search with integrated vectorization

Answer

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. Option B is correct because if the indexer fails, documents are not indexed. Option A is wrong because throttling would affect all documents, not a subset. Option C is wrong because the search service would report failures for other reasons. Option D is wrong because missing semantic configuration affects ranking, not indexing.

Answer

The index does not have a semantic configuration

Answer

The search service has insufficient replicas

Answer

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. Option A is correct because Document Intelligence can extract handwriting and layout, and AI Language can post-process entities. Option B is wrong because Computer Vision OCR is for printed text only. Option C is wrong because Cognitive Search is not an extraction service. Option D is wrong because AI Document Intelligence already includes OCR; adding Computer Vision is redundant.

Answer

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

Answer

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

Answer

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. Option D is correct because semantic ranking uses captions and answers; without them, it is less effective. Option A is wrong because simple scoring profiles do not use AI. Option B is wrong because more replicas improve throughput, not relevance. Option C is wrong because increasing partition count improves indexing speed.

Answer

Increase the number of partitions to handle more data

Answer

Configure a scoring profile with boosting based on metadata

Answer

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. Option B is correct because the premade invoice model is designed for common invoice layouts. Option A is wrong because the layout model extracts text and tables, not key-value pairs. Option C is wrong because custom extraction requires training data. Option D is wrong because the read model only extracts text.

Answer

Custom extraction model

Answer

Layout model

Answer

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. Option C is correct because index-level security with filters is the recommended pattern for multi-tenancy. Option A is wrong because multiple services are costly and unnecessary. Option B is wrong because data sources are not security boundaries. Option D is wrong because skillsets are stateless and do not provide isolation.

Answer

Use separate skillsets for each tenant

Answer

Create a separate search service for each tenant

Answer

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. Option A is correct because Azure AI Language provides sentiment analysis and key phrase extraction as built-in skills. Option B is wrong because Document Intelligence is for document extraction. Option C is wrong because Translator is for translation. Option D is wrong because Computer Vision is for images.

Answer

Azure AI Document Intelligence

Answer

Azure AI Computer Vision

Answer

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. Option B is correct because custom question answering (from Azure AI Language) is designed for FAQ-like Q&A over documents. Option A is wrong because Azure AI Search alone does not provide Q&A. Option C is wrong because Document Intelligence only extracts text. Option D is wrong because Azure OpenAI can do Q&A but requires careful grounding; custom question answering is more straightforward for this use case.

Answer

Azure AI Search with integrated vectorization and semantic search

Answer

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

Answer

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. Option A is correct because increasing the function timeout within limits can handle larger documents. Option B is wrong because moving to Azure AI Language does not solve the timeout. Option C is wrong because Cognitive Services support for custom skills is limited. Option D is wrong because the 24-hour timeout is the maximum; but increasing to 10 minutes is practical.

Answer

Use Azure AI Document Intelligence instead of a custom skill

Answer

Set the function timeout to the maximum of 24 hours

Answer

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. Option A is correct because using managed identity eliminates the need for keys. Option C is correct because allowing the search service's IP in the firewall ensures access. Option B is wrong because shared access keys are less secure. Option D is wrong because disabling firewall is insecure. Option E is wrong because the search service connects to Azure Storage, not the other way.

Answer

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

Answer

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

Answer

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

Index. Option A is correct because a data source defines where to pull data. Option C is correct because an index stores the enriched content. Option E is correct because a skillset defines the enrichment pipeline. Option B is wrong because an indexer is required, but a datasource and index are separate components; an indexer connects them. Option D is wrong because a semantic configuration is optional.

Answer

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

Answer

Azure AI Video Indexer

Answer

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. Option B is correct because the SplitSkill splits the document into pages, and the SentimentSkill runs per page. If the split output is not properly mapped, sentiment only runs on the first page. Option A is wrong because the skill order is correct (split before sentiment). Option C is wrong because the skill version is V3, which is valid. Option D is wrong because the context is correct.

Answer

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

Answer

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. Option C is correct because the suggester's sourceFields only include "content", not the "people" or "organizations" fields. Users likely want suggestions from those entity fields. Option A is wrong because the key field is required and correct. Option B is wrong because the suggester mode is valid. Option D is wrong because the fields are correctly defined as collections.

Answer

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

Answer

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

Answer

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. Option A is correct because the indexer does not specify a skillset or a field mapping to extract content; without a skillset, the blob content is not automatically mapped to the 'content' field unless default mappings exist. But here, the default content field mapping is not explicitly set, and the index expects a 'content' field but the blob's content is not automatically mapped if the indexer is not using a parsing mode. Option B is wrong because the connection string is valid. Option C is wrong because the container name is correct. Option D is wrong because maxFailedItems = -1 allows all failures.

Answer

The connection string in the data source is invalid

Answer

The container name is incorrect

Answer

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