AI-102 Implement knowledge mining and information extraction solutions Practice Test 3 — 15 Questions

Question 1

You are designing a knowledge mining solution that ingests documents from SharePoint Online and makes them searchable using Azure AI Search. The solution must extract text from images and perform optical character recognition (OCR) on embedded images within PDFs. Which built-in skill should you include in the skillset?

Accepted Answer

OCR skill. Option B is correct because the OCR skill extracts text from images. Option A is for text translation. Option C is for key phrases. Option D is for entity recognition.

Answer

Translation skill

Answer

Key phrase extraction skill

Answer

Entity recognition skill

Question 2

Your organization has a large repository of technical manuals in PDF format. You need to build a chatbot that can answer questions about the content of these manuals. Which combination of Azure services should you use?

Accepted Answer

Azure AI Search and Azure OpenAI. Option A is correct because Azure AI Search provides the indexing and retrieval capabilities needed to search through the PDF content, while Azure OpenAI (specifically GPT models) can generate natural language answers based on the retrieved passages. This combination enables a RAG (Retrieval-Augmented Generation) pattern where the search engine finds relevant text chunks from the manuals and the language model formulates a coherent answer.

Answer

Azure AI Speech and Azure OpenAI

Answer

Azure AI Language and Azure AI Document Intelligence

Answer

Azure AI Document Intelligence and Azure Bot Service

Question 3

You are implementing a knowledge mining solution using Azure AI Search. The data source is a large Azure Cosmos DB collection containing customer support tickets. Each ticket has fields: ticket_id, description, category, and resolution. You need to ensure that the search index can support fuzzy search and autocomplete suggestions. What should you configure in the index definition?

Accepted Answer

Set the 'searchable' attribute on the description field and define a suggester. Option B is correct because fuzzy search requires 'searchable' and 'analyzer' fields; autocomplete requires 'suggestions' configured on a field. Options A, C, D are not correct for these features.

Answer

Set the 'filterable' attribute on the description field

Answer

Set the 'sortable' attribute on the ticket_id field

Answer

Set the 'facetable' attribute on the category field

Question 4

You need to extract personally identifiable information (PII) from a set of text documents before indexing them in Azure AI Search. The PII must be redacted. Which Azure AI service and configuration should you use?

Accepted Answer

Use the built-in PII detection skill in Azure AI Search with redaction mode enabled. Option A is correct because Azure AI Language's PII detection skill can redact PII. Options B and D do not redact. Option C is for form extraction.

Answer

Use the entity recognition skill in Azure AI Search and map to a target field

Answer

Use Azure AI Document Intelligence with a custom model to identify PII fields

Answer

Use Azure AI Language's key phrase extraction to find PII

Question 5

Your team is building a knowledge mining solution for research papers. You need to automatically categorize papers into topics and extract author names, publication dates, and references. The solution must use custom models because the papers are domain-specific. Which combination of Azure services should you use?

Accepted Answer

Azure AI Document Intelligence's custom extraction model and Azure AI Language's custom text classification. Option C is correct because Azure AI Document Intelligence can extract custom fields, and Azure AI Language can be used for custom classification. Option A lacks custom extraction; Option B lacks classification; Option D uses OCR skill which is not for custom models.

Answer

Azure AI Document Intelligence's pre-built invoice model and Azure Bot Service

Answer

Azure AI Search's built-in OCR skill and a custom skill using Azure Functions

Answer

Azure AI Language's pre-built entity extraction and Azure AI Search

Question 6

You are designing a solution to extract structured data from a large number of handwritten forms. The forms are scanned and stored as images. Which Azure AI feature should you use?

Accepted Answer

Azure AI Document Intelligence's OCR capability. Option D is correct because the OCR skill in Azure AI Document Intelligence (or Azure AI Search) can extract text from handwritten images. Option A is for speech. Option B is for image analysis, not text extraction from forms. Option C is for conversational AI.

Answer

Azure AI Vision's image analysis

Answer

Azure AI Speech to text

Answer

Azure Bot Service

Question 7

Which TWO actions should you take to optimize the performance of an Azure AI Search solution that indexes large volumes of data?

Accepted Answer

Use the appropriate search tier (S1, S2, etc.) based on document size. Option A is correct because choosing the appropriate search tier (S1, S2, etc.) ensures that the service has sufficient resources (CPU, memory, disk I/O) to handle the indexing load and document size. Higher tiers provide better indexing throughput and storage capacity, which is critical for large volumes of data. Using an undersized tier can lead to throttling, timeouts, or failed indexing operations.

Answer

Use the free tier for production workloads

Answer

Increase the replica count for better indexing throughput

Answer

Disable scoring profiles to speed up indexing

Question 8

Which THREE components are essential when building a custom skill for Azure AI Search?

Accepted Answer

A Web API endpoint that processes documents. A, B, and D are correct. A: The Web API is the endpoint. B: JSON input/output format is required. D: Field mappings define inputs and outputs in the skillset. C is not required; E is optional.

Answer

A machine learning model trained in Azure Machine Learning

Answer

An Azure Function to trigger the skill on a schedule

Question 9

Which TWO Azure AI services can be used to extract text from images as part of an Azure AI Search enrichment pipeline?

Accepted Answer

Azure AI Document Intelligence's read model. B and D are correct. B: The OCR skill in Azure AI Search uses Azure AI Vision. D: Azure AI Document Intelligence uses OCR. A is for translation; C is for speech; E is for conversational AI.

Answer

Azure Bot Service

Answer

Azure AI Speech to text

Answer

Azure AI Language translation

Question 10

You are reviewing the skillset definition for an Azure AI Search indexer. The SplitSkill splits the document content into pages of 5000 characters. The SentimentSkill is set to run on each page. However, the sentiment analysis is not producing correct results. What is the most likely cause?

Accepted Answer

The input source for SentimentSkill should be '/document/pages/*' but the SplitSkill output is named 'pages', so the input should be '/document/pages'. Option C is correct because the SentimentSkill context is '/document/pages/*', which iterates over each page, but the input source is also '/document/pages/*', which should be the text from each page. However, the issue is that the SentimentSkill expects a single string, but the SplitSkill outputs an array. The input should be '/document/pages/*' but that is an array; the correct mapping would be to use the split output correctly. Actually, the error is that the input source should be '/document/pages/*' which is the array item, but the sentiment skill might be receiving an array instead of a string if not properly configured. Option C identifies that the input source should be the textItems from the split output. Option A is wrong because the skill version is fine. Option B is wrong because context can be arrays. Option D is wrong because the maximumPageLength is valid.

Answer

The maximumPageLength of 5000 is too high for sentiment analysis

Answer

The context of the SentimentSkill is set to an array, which is not supported

Answer

The SentimentSkill uses an incorrect @odata.type version

Question 11

You have an Azure AI Search indexer that is configured to index PDF files from Azure Blob Storage. The indexer is not extracting any text from the PDFs, and no errors are reported. You review the indexer definition as shown. What is the most likely cause?

Accepted Answer

The parsingMode is set to 'json' instead of 'default' or 'text'. Option B is correct because the parsingMode is set to 'json', which is for JSON files, not PDFs. For PDF parsing, the mode should be 'default' or 'text'. Option A is wrong because contentAndMetadata is fine. Option C is wrong because field mappings are correct. Option D is wrong because the path mapping is fine.

Answer

The field mapping from 'content' to 'content' is redundant and causes a conflict

Answer

The field mapping for 'metadata_storage_path' should be to 'metadata_storage_path'

Answer

The dataToExtract is set to 'contentAndMetadata' which is not supported for PDFs

Question 12

You executed the Azure CLI command shown to create an indexer. However, the indexer fails to run. The error indicates that the data source connection string is invalid. You have verified that the connection string is correct. What is the most likely issue?

Accepted Answer

The data source's connection string is not properly stored or the data source is not configured with the correct credentials. Option D is correct because the Azure CLI command uses single quotes around the parameters JSON, which is correct in Bash, but in PowerShell or other shells, it may cause issues. However, the most common issue is that the command is missing the '--connection-string' parameter or the data source itself has an incorrect connection string. But given the exhibit, the command does not include a '--connection-string' parameter; the data source is referenced by name. The problem might be that the data source was created with a connection string that is not valid. Option A is wrong because the parsingMode is valid. Option B is wrong because the skillset name is not causing the error. Option C is wrong because the query parameter is not the issue.

Answer

The --query parameter is incorrectly formatted

Answer

The skillset name does not exist in the search service

Answer

The parsingMode should be 'json' for PDF files

Question 13

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 14

Your company uses Azure AI Search for an internal knowledge base. Users complain that searches for 'annual report 2023' return irrelevant results. You analyze the search index and find that the content field contains large blocks of text from PDFs. You need to improve relevance without re-indexing all documents. Which approach should you take?

Accepted Answer

Enable semantic ranking on the search index. Option A is correct because applying Azure AI Search's built-in semantic ranking re-ranks results using language understanding, improving relevance for natural language queries without re-indexing. Option B is wrong because enabling spell correction in the query only fixes typos, not relevance. Option C is wrong because adding a custom scoring profile requires changes to the index definition, which may require re-indexing. Option D is wrong because changing the analyzer to a different language does not address the core relevance issue.

Answer

Enable spell correction in the search query

Answer

Add a custom scoring profile based on term frequency

Answer

Change the index analyzer to a different language

Question 15

You are building a knowledge mining solution to extract insights from customer support call transcripts. The solution must identify the customer's issue, the resolution provided, and the sentiment of the call. Which combination of Azure AI services should you use?

Accepted Answer

Azure AI Language (key phrase extraction, entity recognition, sentiment analysis). Option C is correct because Azure AI Language provides pre-built models for key phrase extraction, entity recognition (issue/resolution), and sentiment analysis. Option A is wrong because Azure AI Speech is for speech-to-text, not text analysis. Option B is wrong because Azure AI Translator is for translation. Option D is wrong because Azure AI Document Intelligence is for document extraction, not conversation text.

Answer

Azure AI Translator and Azure AI Search

Answer

Azure AI Speech and Azure AI Search

Answer

Azure AI Document Intelligence and Azure AI Language