AI-102 Practice Test 42 — 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 large set of PDF invoices stored in Azure Blob Storage. You need to extract line-item details (product names, quantities, prices) and store them in Azure SQL Database for downstream reporting. The invoices have varied layouts. Which Azure AI service should you use?

Accepted Answer

Azure AI Document Intelligence. Option B is correct because Document Intelligence (formerly Form Recognizer) is designed to extract structured data from documents with varied layouts using prebuilt or custom models. Option A is wrong because Computer Vision can extract text but not structured line items. Option C is wrong because Language Service is for NLP tasks, not document extraction. Option D is wrong because Cognitive Search is for indexing and search, not extraction from documents.

Answer

Azure AI Computer Vision

Answer

Azure AI Language Service

Answer

Azure AI Search

Question 9

You are designing a knowledge mining solution for a large legal firm. The solution must extract key clauses, parties, and dates from thousands of PDF contracts. You need to minimize manual labeling effort while achieving high extraction accuracy. Which Azure AI service should you use?

Accepted Answer

Azure AI Document Intelligence custom extraction model. Option B is correct because Custom Document Extraction in Azure AI Document Intelligence (formerly Form Recognizer) uses custom models trained with a small set of labeled documents to extract specific fields. Option A is wrong because Azure AI Search is for indexing and search, not extraction. Option C is wrong because Azure OpenAI Service with GPT-4 can extract but requires more prompt engineering and may not be as cost-effective for structured field extraction. Option D is wrong because Azure AI Language's custom NER is more focused on entities in text, not document layout.

Answer

Azure OpenAI Service with GPT-4 prompt engineering

Answer

Azure AI Search with built-in blob indexing

Answer

Azure AI Language custom named entity recognition

Question 10

You need to extract product codes (e.g., 'PRD-12345') from scanned invoices using Azure AI Document Intelligence. The product codes always follow a pattern of three uppercase letters, a hyphen, and five digits. Which approach should you use?

Accepted Answer

Use the pre-built invoice model in Azure AI Document Intelligence with a regex field extraction. Option C is correct because Azure AI Document Intelligence's pre-built invoice model can be extended with custom fields using regex to extract product codes. Option A is wrong because Azure AI Language's custom NER does not handle document layouts. Option B is wrong because Azure AI Search's custom skills require additional development. Option D is wrong because Azure OpenAI GPT-4 is overkill and less reliable for structured extraction.

Answer

Build a custom skill in Azure AI Search using a Python regex

Answer

Train a custom NER model in Azure AI Language

Answer

Use Azure OpenAI GPT-4 with document vision to extract the codes

Question 11

Your Azure AI Search indexer is failing to index a large number of PDFs from Azure Blob Storage. The error log shows 'Document extraction timeout' for many documents. You need to resolve this issue without losing data. What should you do?

Accepted Answer

Increase the indexer execution timeout in the indexer definition. Option A is correct because increasing the indexer execution timeout allows more time for large document extraction. Option B is wrong because enabling incremental enrichment does not affect extraction timeout. Option C is wrong because splitting PDFs into smaller files would require re-uploading. Option D is wrong because changing the parsing mode to text does not apply to PDFs; PDFs are already parsed as text or image.

Answer

Change the parsing mode of the indexer to 'text'

Answer

Split large PDFs into smaller files before uploading

Answer

Enable incremental enrichment on the skillset

Question 12

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. Option B is correct because the prebuilt invoice model extracts tables and line items. Option A is wrong because the general document model extracts text and layout but not invoice-specific fields. Option C is wrong because the custom model requires training data. Option D is wrong because the layout model only extracts text and tables without field mapping.

Answer

Prebuilt document model

Answer

Layout model

Answer

Custom model

Question 13

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 14

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 15

Your organization has a large corpus of legal documents stored in Azure Blob Storage. You need to build a solution that allows lawyers to ask natural language questions and get answers directly from the documents, without moving data out of Azure. Which service should you use?

Accepted Answer

Azure AI Search with semantic search. Option C is correct because Azure AI Search with semantic search can index documents and return answers using natural language queries. Option A is wrong because Document Intelligence extracts structured data but does not provide Q&A. Option B is wrong because Language Service provides Q&A but requires a knowledge base. Option D is wrong because Computer Vision is for image analysis.

Answer

Azure AI Document Intelligence

Answer

Azure AI Language Service with custom question answering

Answer

Azure AI Computer Vision