AI-102 Practice Test 49 — 15 Questions

Question 1

Your knowledge mining solution uses Azure AI Search with cognitive skills. During testing, you notice that some documents are not being enriched because the skillset execution fails. Which diagnostic step should you take first?

Accepted Answer

Enable debug mode on the skillset. Option B is correct because enabling debug mode creates a debug session that captures detailed execution logs for each skill. Option A is wrong because checking the indexer status shows success/failure but not detailed skill errors. Option C is wrong because resubmitting might repeat the same error without insight. Option D is wrong because the portal shows indexer execution history but not per-document skill details.

Answer

Review the indexer execution history in the portal

Answer

Re-run the indexer with a fresh document

Answer

Check the indexer logs in Azure Monitor

Question 2

Which THREE actions should you take when designing a custom skill for an Azure AI Search enrichment pipeline? (Choose three.)

Accepted Answer

Define input and output parameters in the skillset definition. Options A, C, and D are correct. The skill must be deployed as a web API that accepts JSON input and returns JSON output. It should handle errors gracefully. It must be registered in the skillset. Option B is wrong because the skill should not be long-running; indexer timeouts apply. Option E is wrong because the skill can be in any language as long as it exposes a REST API.

Answer

Ensure the skill executes within 2 minutes

Answer

Write the skill in a language supported by Azure AI Search

Question 3

Your team is building a custom ChatGPT-like copilot using Microsoft Foundry that answers questions based on internal HR policies stored in SharePoint. The solution must retrieve only the most relevant documents to minimize token usage. Which Azure AI Search feature should you configure?

Accepted Answer

Semantic ranking. Option C is correct because semantic ranking improves relevance by using deep learning models to re-rank search results. Option A is incorrect because synonyms expand queries but don't necessarily improve relevance ranking. Option B is incorrect because scoring profiles are simpler and less effective for deep relevance. Option D is incorrect because filters reduce the result set but don't improve ranking.

Answer

Synonyms

Answer

Scoring profiles

Answer

Filters

Question 4

You are designing a knowledge mining solution to extract information from scanned invoices stored as multi-page TIFF images. Which two Azure AI services should you combine to extract text and structure the data?

Accepted Answer

Azure AI Document Intelligence and Azure AI Vision. Azure AI Document Intelligence (formerly Form Recognizer) can extract structured data from invoices, and Azure AI Vision OCR can extract text from images. Option A is incorrect because Azure AI Language doesn't process images. Option B is incorrect because Azure AI Search is for indexing, not extraction. Option D is incorrect because Azure AI Translator is for translation.

Answer

Azure AI Language and Azure AI Search

Answer

Azure AI Search and Azure AI Vision

Answer

Azure AI Translator and Azure AI Document Intelligence

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 SplitSkill outputs 'pages' but the SentimentSkill input expects '/document/pages/*'. However, the SplitSkill's targetName 'pages' is correct, but note that the SplitSkill context is '/document', so the output path is '/document/pages'. The input source uses '/document/pages/*', which is correct. The error suggests the SplitSkill did not produce any pages. Option B is correct: the SplitSkill might not have split the content, possibly because the text length is too short. Option A is incorrect because the skill is defined. Option C is incorrect because targetName is correct. Option D is incorrect because the error is about missing path, not missing skill.

Answer

The SplitSkill is not defined in the skillset.

Answer

The SentimentSkill is missing a required input.

Answer

The targetName in SplitSkill is misspelled.

Question 6

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 can use a document understanding model to classify documents and extract entities. Option A is wrong because Syntex does not use the out-of-the-box entity extraction from AI Builder directly; it uses its own models. Option B is wrong because Power Automate with AI Builder is a separate approach, not integrated with Syntex. Option D is wrong because Azure AI Search is not needed for this scenario.

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 7

Your knowledge mining solution uses Azure AI Search with a custom skill that calls an Azure Function to perform complex data validation. The custom skill returns an error for some documents, but the indexer continues without raising an error. What is the most likely cause?

Accepted Answer

The indexer is configured with 'allowSkillsetToExecuteIfError' set to true.. By default, indexers continue on error. To stop on skill errors, set 'allowSkillsetToExecuteIfError' to false. The indexer treats skill errors as warnings by default.

Answer

The indexer is configured with 'allowSkillsetToExecuteIfError' set to false.

Answer

The Azure Function returns HTTP 200 with an error message in the body.

Answer

The custom skill's output field mapping is incorrect.

Question 8

You are building a solution to extract key information from scanned invoices. The invoices are in PDF format and contain both printed and handwritten fields. Which Azure AI service should you use?

Accepted Answer

Azure AI Document Intelligence (formerly Form Recognizer). Option C is correct because Form Recognizer (now AI Document Intelligence) can extract text, key-value pairs, and tables from documents, including handwriting. Option A is wrong because Computer Vision primarily analyzes images for objects and tags, not structured extraction. Option B is wrong because Language Service focuses on text analytics. Option D is wrong because Speech Service handles audio.

Answer

Language Service

Answer

Speech Service

Answer

Computer Vision

Question 9

You are designing a knowledge mining solution that must handle sensitive customer data. The solution must ensure that personally identifiable information (PII) is not returned in search results. What should you do?

Accepted Answer

Use a custom skill in the skillset to detect and redact PII before indexing. Option B is correct because enabling PII detection in the enrichment pipeline and using a custom skill to remove or mask PII fields before indexing prevents PII from being stored in the index. Option A is wrong because encryption does not prevent PII from being returned in results. Option C is wrong because field mappings control how fields are imported, not content removal. Option D is wrong because access control restricts who can search but does not remove PII from results.

Answer

Use Azure AI Search with encryption at rest

Answer

Implement role-based access control on the search index

Answer

Configure field mappings to exclude PII fields

Question 10

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, set context to '/document' and inputs to reference '/document/content'. Option A passes only the content. Option B passes only metadata. Option D uses wrong path separators.

Answer

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

Answer

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

Answer

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

Question 11

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 outputFieldMapping maps '/document/content' to 'content'. However, the skillset (not shown) might not produce this output, or the skill output path is different. But the exhibit shows that the indexer expects '/document/content' to be available from the skillset. If the skillset does not output '/document/content', the field remains empty. Option C is correct: the outputFieldMapping references a path that does not exist in the enrichment tree. Option A is incorrect because the field mapping for 'path' is fine. Option B is incorrect because the indexer runs successfully. Option D is incorrect because batch size does not affect field mapping.

Answer

The indexer schedule is too frequent.

Answer

The fieldMapping for 'metadata_storage_path' is incorrect.

Answer

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

Question 12

You are designing a knowledge mining solution for customer support emails. The solution must extract the customer's name, issue category, and sentiment from each email. Which two Azure AI services should you combine?

Accepted Answer

Azure AI Language and Azure AI Search. Azure AI Language provides entity extraction and sentiment analysis. Azure AI Search indexes the extracted data for search. The other services are not directly needed for extraction and indexing.

Answer

Azure AI Bot Service and Azure AI Language

Answer

Azure AI Document Intelligence and Azure AI Search

Answer

Azure AI Translator and Azure AI Language

Question 13

An organization uses Azure AI Search to power an internal knowledge base. They notice that search results are returning irrelevant documents. The index includes a 'content' field with full text and a 'tags' field with metadata. Users often search for specific terms that appear in the 'tags' field. How should you configure the search index to improve relevance?

Accepted Answer

Configure a scoring profile with a higher weight for the 'tags' field.. Option B is correct because by assigning a higher weight to the 'tags' field, search results that match tags will rank higher. Option A changes analyzers but doesn't address field weighting. Option C only affects the 'content' field. Option D is about scoring profiles but not specifically about field weighting.

Answer

Add a custom scoring profile based on freshness.

Answer

Set the 'tags' field to use the 'keyword' analyzer.

Answer

Enable semantic search on the 'content' field.

Question 14

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.. Custom models in Document Intelligence can be trained on a set of sample documents to learn the specific layout of a vendor's invoices, improving extraction accuracy for non-standard formats. Option A is wrong because using a prebuilt model for invoices is designed for standard layouts. Option B is wrong because Azure AI Form Recognizer (now part of Document Intelligence) supports custom models. Option D is wrong because OCR provides text but not structured extraction.

Answer

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

Answer

Convert the invoices to a standard format before processing.

Answer

Use the prebuilt invoice model with confidence threshold adjustment.

Question 15

Which TWO options are valid ways to index content from Azure SQL Database into Azure AI Search? (Select TWO.)

Accepted Answer

Use the Push API to send data directly to the search index.. Azure AI Search supports indexing from SQL Database using either a SQL Server indexer or a push API. Data Factory and Event Hubs are not direct indexers for SQL.

Answer

Use Azure Data Factory to copy data to Blob Storage, then index from Blob.

Answer

Use Azure AI Document Intelligence to extract data and push to index.

Answer

Use Azure Event Hubs to stream data into the search index.