AI-102 Implement knowledge mining and information extraction solutions Practice Test 9 — 15 Questions

Question 1

You are designing an Azure Cognitive Search solution for a legal document repository. The solution must support search across multiple languages, including English and Spanish. You need to ensure that searches return documents regardless of the language of the query. Which approach should you use?

Accepted Answer

Use the Microsoft analyzer for English and Spanish in a multi-language field.. Option D is correct because using a multi-language analyzer that includes both English and Spanish microsoft analyzers allows the index to handle multiple languages in the same field. Option A is wrong because a single analyzer cannot handle multiple languages. Option B is wrong because a custom analyzer would not be prebuilt for multiple languages. Option C is wrong because language-specific fields would require the user to know the language of the query.

Answer

Create a custom analyzer that splits text by language.

Answer

Use a single language analyzer set to English, and assume Spanish documents are translated.

Answer

Create separate fields for each language and set the analyzer per field.

Question 2

You are using Azure AI Document Intelligence to process a large batch of PDF forms. The forms have varying layouts and handwriting. You need to extract text and key-value pairs. Which custom model type should you train?

Accepted Answer

Custom neural model. Option B is correct because a template model works best with forms that have a fixed layout, but since layouts vary, a neural model is more robust. Option A is wrong because a template model assumes fixed layout. Option C is wrong because a composed model combines multiple models but is not the primary choice for varying layouts. Option D is wrong because a prebuilt model is not customized for your forms.

Answer

Custom template model

Answer

Prebuilt-layout model

Answer

Custom composed model

Question 3

Your company has a large set of PDF documents stored in Azure Blob Storage. You need to index these documents in Azure Cognitive Search so that users can search the text content. What is the first step you should take?

Accepted Answer

Create a data source that connects to Azure Blob Storage.. Option A is correct because you need to create a data source that points to the Blob Storage container to fetch the documents. Option B is wrong because you need a data source before creating an indexer. Option C is wrong because the indexer will create the index based on the data source. Option D is wrong because the indexer will handle skillset execution.

Answer

Create an index with a field for each metadata property.

Answer

Create a skillset to extract text from PDFs.

Answer

Create an indexer that runs daily.

Question 4

You are implementing a knowledge mining solution using Azure Cognitive Search with built-in AI enrichment. The pipeline must extract named entities and key phrases from documents. The enrichment pipeline should be triggered only for documents that are larger than 1 MB. Which approach should you use?

Accepted Answer

Implement a custom skill that checks the document size and only calls the built-in skills if size > 1 MB.. Option D is correct because a custom skill can evaluate document size and conditionally call built-in skills. Option A is wrong because skills run on all documents regardless of size. Option B is wrong because indexer parameters cannot conditionally skip skills based on document content. Option C is wrong because skillset execution order cannot be changed by document size.

Answer

Configure the skillset to run entity recognition and key phrase extraction on all documents.

Answer

Use indexer parameters to specify a document size filter.

Answer

Create two indexers: one for large documents with skills, one for small documents without.

Question 5

You are using Azure AI Language to extract information from medical research papers. You need to identify terms like 'dosage', 'side effects', and 'contraindications' specific to the medical domain. Which capability should you use?

Accepted Answer

Custom Named Entity Recognition (NER). Option B is correct because custom Named Entity Recognition allows you to train a model to recognize custom entities like medical terms. Option A is wrong because prebuilt NER only recognizes general entities like person, location, etc. Option C is wrong because entity linking links to external knowledge bases. Option D is wrong because PII detection is for personal information.

Answer

Prebuilt Named Entity Recognition (NER)

Answer

PII detection

Answer

Entity linking

Question 6

You are building a question answering solution using Azure AI Language. You have a set of frequently asked questions (FAQs) in a Word document. You need to import the FAQs into a project. Which approach should you use?

Accepted Answer

Create a custom question answering project and import the Word document as a source.. Option A is correct because custom question answering can import FAQ content from a Word document using the 'Import' feature. Option B is wrong because prebuilt question answering does not support document import. Option C is wrong because conversational language understanding is for intent classification, not FAQ import. Option D is wrong because Document Intelligence is for form extraction, not QnA.

Answer

Use Azure AI Document Intelligence to extract QnA pairs.

Answer

Use the prebuilt question answering API to parse the document.

Answer

Use conversational language understanding (CLU) to extract intents and entities.

Question 7

You are designing an Azure Cognitive Search solution that indexes customer support tickets. The index must include a field for 'sentiment' that is populated from an AI enrichment pipeline. Which TWO actions are required to achieve this?

Accepted Answer

Add a built-in Sentiment skill to the skillset.. Options A and C are correct. The enrichment pipeline must include a built-in sentiment skill (A), and a field mapping must be added to map the sentiment output to the index field (C). Option B is wrong because the sentiment skill is already built-in. Option D is wrong because a custom skill is not required. Option E is wrong because the index definition defines the field but does not populate it.

Answer

Implement a custom skill to normalize sentiment values.

Answer

Create a custom sentiment analysis skill using Azure AI Language.

Answer

Define a 'sentiment' field in the index with type Collection(Edm.String).

Question 8

You are using Azure AI Document Intelligence to extract data from scanned contracts. The contracts contain tables and handwritten signatures. Which TWO features should you enable?

Accepted Answer

Train a custom neural model to recognize handwritten signatures.. Options A and C are correct. The neural model handles handwriting (A), and the table extraction capability (C) extracts tables. Option B is wrong because the prebuilt-layout model is not custom-trained. Option D is wrong because form recognition is part of Document Intelligence but not specific to contracts. Option E is wrong because OCR is built-in and not a separate feature to enable.

Answer

Enable OCR to read scanned text.

Answer

Use form recognition to capture key-value pairs.

Answer

Use the prebuilt-layout model for all extraction.

Question 9

You are building a knowledge mining solution that uses Azure Cognitive Search and Azure AI Language. The solution must extract key phrases and detect the language of documents. Which THREE components are required?

Accepted Answer

A search index that contains fields for the extracted data.. Options A, C, and D are correct. A skillset with key phrase extraction and language detection skills (A), a data source connecting to the documents (C), and a search index to store the enriched data (D). Option B is wrong because the indexer is required, but the index is needed too. Option E is wrong because custom skills are not required.

Answer

A custom skill to combine key phrases and language.

Answer

An indexer that runs on a schedule.

Question 10

You are a data scientist for Contoso Pharmaceuticals. The company has thousands of research documents in PDF format stored in Azure Blob Storage. You need to build an Azure Cognitive Search solution that enables researchers to search for documents based on chemical compound names, disease mentions, and experimental results. The solution must extract these entities using a custom AI model built in Azure AI Language. Additionally, the solution must support semantic search for natural language queries. The search index must be updated daily with new documents. You have an existing Azure AI Language custom entity extraction model that recognizes chemical compounds and diseases. The model is deployed as an endpoint. You need to configure the enrichment pipeline. What should you do?

Accepted Answer

Create a custom skill in the skillset that calls the custom entity extraction endpoint via HTTP.. Option B is correct because you need to create a custom skill in the Azure Cognitive Search skillset that calls the Azure AI Language custom entity extraction endpoint. The built-in skills do not support custom models directly. Option A is wrong because built-in skills only cover prebuilt entities. Option C is wrong because the custom model is already deployed. Option D is wrong because a custom skill is needed before adding to the index.

Answer

Deploy the custom model to Azure AI Document Intelligence and use a Document Intelligence skill.

Answer

Add the custom entity extraction as a field mapping in the indexer.

Answer

Use the built-in Entity Recognition skill and configure it to use your custom model endpoint.

Question 11

You are building a knowledge mining solution for a legal firm that needs to extract key clauses from thousands of scanned contract PDFs. The solution must identify parties, effective dates, and termination conditions. Which Azure AI service should you use as the primary component?

Accepted Answer

Azure AI Document Intelligence. Option B is correct because Azure AI Document Intelligence (formerly Form Recognizer) is designed for extracting structured information from documents using prebuilt and custom models. Option A is incorrect because Azure AI Language is for text analytics and NLP but not optimized for scanned documents. Option C is incorrect because Azure AI Search is for indexing and searching, not extraction. Option D is incorrect because Azure AI Vision is for image analysis, not document extraction.

Answer

Azure AI Vision

Answer

Azure AI Language

Answer

Azure AI Search

Question 12

A company uses Azure AI Search to index customer support tickets. They need to automatically extract key phrases from each ticket to improve search relevance. Which built-in skill should they add to the skillset?

Accepted Answer

Key Phrase Extraction. Option C is correct because the Key Phrase Extraction skill extracts key phrases from text. Option A is incorrect because Entity Recognition extracts named entities. Option B is incorrect because Sentiment Analysis determines sentiment. Option D is incorrect because OCR extracts text from images.

Answer

Entity Recognition

Answer

Sentiment Analysis

Answer

OCR

Question 13

You are designing a knowledge mining solution using Azure AI Search. The solution must process large volumes of PDFs daily. You need to minimize the cost of cognitive skills execution while ensuring the pipeline can handle transient failures. Which approach should you recommend?

Accepted Answer

Enable incremental enrichment on the indexer. Option A is correct because enabling incremental enrichment caches intermediate results and recovers from failures without re-processing unchanged documents, reducing cost. Option B is incorrect because increasing the number of replicas improves query performance, not indexing. Option C is incorrect because disabling field mappings would break the pipeline. Option D is incorrect because using a free tier is not feasible for large volumes.

Answer

Disable field mappings

Answer

Increase the number of replicas

Answer

Use the free tier for the indexer

Question 14

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 15

You are implementing a knowledge mining solution with Azure AI Search that ingests data from Azure Blob Storage. The pipeline includes a custom skill that calls an external API for specialized entity extraction. The custom skill sometimes returns HTTP 429 (Too Many Requests). How should you handle this to ensure reliable indexing?

Accepted Answer

Configure a retry policy on the custom skill. Option D is correct because the indexer retry policy can be configured to handle transient failures, including 429 errors, by retrying after a delay. Option A is incorrect because reducing the batch size might not help if the API rate limit is per request. Option B is incorrect because increasing timeouts does not address rate limiting. Option C is incorrect because scheduling the indexer less frequently does not prevent failures during execution.

Answer

Reduce the batch size in the indexer

Answer

Increase the skill timeout

Answer

Schedule the indexer to run less frequently