AI-102 Implement knowledge mining and information extraction solutions Practice Test 6 — 15 Questions

Question 1

You are building a knowledge mining solution using Azure AI Search and Azure AI Language. The solution must extract key phrases, entities, and sentiment from customer feedback documents. After processing, the enriched content should be stored in the search index for full-text search. You need to configure the enrichment pipeline. Which two Azure AI services should you integrate?

Accepted Answer

Azure AI Language and Azure AI Search. Azure AI Language provides key phrase extraction, entity recognition, and sentiment analysis as built-in skills. Azure AI Search provides the indexing and search capabilities. Option A is wrong because Azure AI Translator is for translation, not the required analyses. Option B is wrong because Azure AI Document Intelligence is for extracting text from documents, not for language analysis. Option D is wrong because the custom skill would be redundant if native skills exist.

Answer

Azure AI Language and a custom skill in Azure Functions

Answer

Azure AI Translator and Azure AI Search

Answer

Azure AI Document Intelligence and Azure AI Search

Question 2

Your company uses Azure AI Search to power a customer support portal. The search index includes product documentation and known issues. Recently, the portal's search performance has degraded, and users report slow response times. You need to identify the cause of the performance issue. What should you check first?

Accepted Answer

Review the search service metrics for high query latency and CPU usage.. High query latency in Azure AI Search is often caused by high CPU utilization in the search service. Checking metrics in the Azure portal can reveal if the service is overburdened. Option A is wrong because storage size does not directly cause slow queries. Option C is wrong because the skillset is not involved unless enrichment is ongoing. Option D is wrong because the index schema size is not a primary performance factor.

Answer

Check the size of the index storage in the Azure portal.

Answer

Ensure the index schema does not have too many fields.

Answer

Verify that the skillset is not running during peak hours.

Question 3

You are implementing a knowledge mining solution for a legal firm. The solution must ingest large volumes of legal documents (PDFs and Word files) stored in Azure Blob Storage. You need to extract text, recognize named entities (e.g., parties, judges, case numbers), and index the content for full-text search. The solution should also support redaction of sensitive information before indexing. Which combination of Azure AI services should you use?

Accepted Answer

Azure AI Document Intelligence, Azure AI Language, custom skill for redaction, and Azure AI Search. Azure AI Document Intelligence extracts text from documents. Azure AI Language provides entity recognition. A custom skill can perform redaction. Azure AI Search indexes the content. Option A is wrong because Azure AI Translator is not needed. Option B is wrong because Azure AI Video Indexer is for video. Option D is wrong because Azure AI Content Safety is for moderation, not redaction.

Answer

Azure AI Document Intelligence, Azure AI Translator, and Azure AI Search

Answer

Azure AI Document Intelligence, Azure AI Video Indexer, and Azure AI Search

Answer

Azure AI Document Intelligence, Azure AI Content Safety, and Azure AI Search

Question 4

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 5

You are building a chatbot using Microsoft Copilot Studio that needs to answer questions based on content from a set of technical manuals stored as PDFs. The content must be indexed and made available to the chatbot. You need to configure the knowledge source for the chatbot. What should you do?

Accepted Answer

Create an Azure AI Search index from the PDFs and connect it to Copilot Studio as a knowledge source.. Microsoft Copilot Studio can use Azure AI Search as a knowledge source to index and retrieve content from PDFs. Option A is wrong because the Web search is for public information. Option B is wrong because SharePoint is not the direct indexer for PDFs. Option D is wrong because Azure SQL Database is not for document content.

Answer

Upload the PDFs to a SharePoint library and use SharePoint as the knowledge source.

Answer

Add a web search connector to the chatbot to search for information online.

Answer

Store the content in Azure SQL Database and use it as a knowledge source.

Question 6

You are designing an Azure AI Search solution that indexes customer support tickets from a SQL database and also from a custom CRM system via a custom API. The index must support scoring profiles that boost tickets from VIP customers. The VIP status is stored in the CRM system. You need to ensure that the boost factor is applied correctly. What should you do?

Accepted Answer

Include the VIP status field in the index by pushing it from the CRM system via the push API, then use a scoring profile with a field boost.. A scoring profile can use a field (e.g., isVIP) to boost results. The field must be present in the index. You can push the VIP data to the index via the push API alongside the CRM data. Option A is wrong because a custom analyzer does not handle boosting. Option B is wrong because the scoring profile cannot dynamically fetch data from an external source during query time. Option D is wrong because the scoring profile uses fields already in the index.

Answer

Use a scoring profile with a tag boost that references a field in the SQL database.

Answer

Use a scoring profile with a function that queries the CRM system at query time.

Answer

Configure a custom analyzer that boosts VIP terms.

Question 7

You are deploying a knowledge mining solution using Azure AI Search and Azure AI Document Intelligence. The solution must extract text from scanned documents, identify named entities, and index the content. You need to configure the skillset. Which TWO built-in skills should you include in the skillset?

Accepted Answer

OCR skill. The OCR skill extracts text from scanned images. The EntityRecognition skill identifies named entities. The Merge skill is not required because OCR output is already text. The KeyPhraseExtraction skill extracts key phrases, not entities. The LanguageDetection skill detects language, not entities.

Answer

Merge skill

Answer

LanguageDetection skill

Answer

KeyPhraseExtraction skill

Question 8

You are using Microsoft Purview to create a knowledge map of your organization's data assets. The solution must automatically scan and classify sensitive data in Azure Blob Storage. You need to configure the scanning and classification. Which THREE actions should you perform?

Accepted Answer

Run a full scan of the Blob Storage to discover and classify data.. Register a data source (Blob Storage) in Purview. Create a scan rule set for classification. Run a full scan to classify data. Option A is wrong because custom classification rules are not required; built-in classifiers can be used. Option D is wrong because sensitivity labels are set after classification, but not a direct action for scanning. Option E is wrong because configuring a firewall is a prerequisite but not part of classification scanning.

Answer

Create custom classification rules for sensitive data types.

Answer

Apply sensitivity labels to the classified data.

Question 9

You are building an Azure AI Search index that includes content from a SQL database and an Azure Cosmos DB collection. The index must support autocomplete suggestions as users type. Which TWO configurations are required to enable autocomplete?

Accepted Answer

Define a suggester in the index definition.. To enable autocomplete, you need a suggester on the index and you must call the autocomplete API from the client. Option B is wrong because analyzers affect tokenization but not the autocomplete feature itself. Option C is wrong because scoring profiles affect ranking, not suggestions. Option E is wrong because CORS is not required for autocomplete.

Answer

Enable CORS on the search service.

Answer

Configure a custom analyzer for the fields used in autocomplete.

Answer

Create a scoring profile to boost relevant suggestions.

Question 10

You have an Azure AI Search skillset defined as shown in the exhibit. When you run the indexer, the enrichment pipeline produces outputs but no entities are extracted. The source documents are in English and contain clear organization and person names. What is the most likely cause?

Accepted Answer

The '/document/content' field is an array, but the skill expects a string.. The default version of EntityRecognitionSkill (V3) requires the 'text' input to be a string, not an array. If '/document/content' is an array (e.g., from OCR output), the skill fails silently. Option A is wrong because the language is correct. Option B is wrong because the skill outputs are configured. Option D is wrong because the skill is in the correct order (no dependency).

Answer

The skill output is not mapped to the index.

Answer

The skills are in the wrong order.

Answer

The documents are not in English.

Question 11

You have an Azure AI Search index defined as shown in the exhibit. Users want to filter search results by author and by a date range, and also see a count of documents per tag. However, the filter on author is not working. What is the most likely reason?

Accepted Answer

The filter expression uses incorrect OData syntax.. For filtering to work, the field must be 'filterable'. In the exhibit, 'author' is set to 'filterable': true. However, if the filter syntax is incorrect (e.g., using 'eq' instead of 'eq' for strings), it would fail. But the most common issue is that the author field is not marked as filterable. Wait, in the exhibit it is 'filterable': true. So another cause: The index might not have been rebuilt after adding filterable. But the exhibit shows the current index definition. Actually, the issue could be that the filter is using an incorrect OData syntax. Option B is wrong because sortable is not required for filtering. Option C is wrong because tags are facetable but not involved. Option D is wrong because the key field is not used for filtering.

Answer

The 'id' field is not used as the key.

Answer

The 'author' field is not set as filterable in the index definition.

Answer

The query uses a $orderby parameter that conflicts with the filter.

Question 12

You have configured an Azure AI Search indexer with a Cosmos DB data source as shown in the exhibit. The indexer runs successfully, but you notice that the index is missing some documents that were recently added to Cosmos DB. What is the most likely cause?

Accepted Answer

The high water mark is not being updated correctly, causing some documents to be skipped.. The query uses '@HighWaterMark' which is a placeholder for the high water mark value used for change tracking. However, the query includes 'WHERE c._ts > @HighWaterMark' which filters out documents with a timestamp less than or equal to the high water mark. If the high water mark is not being updated correctly or if documents have the same timestamp, they might be missed. Option A is wrong because the change tracking is enabled by the query using _ts. Option B is wrong because the query selects all fields needed. Option C is wrong because the container name is correct. Option D is wrong because the connection string is valid since the indexer runs successfully.

Answer

The indexer is not configured to track changes using _ts.

Answer

The container name is misspelled.

Answer

The query does not select all fields required by the index.

Question 13

You are building a knowledge mining solution for legal documents stored in Azure Blob Storage. The solution must extract entities, key phrases, and relationships from the documents. Which Azure AI service should you use?

Accepted Answer

Azure AI Language. Azure AI Language provides entity extraction, key phrase extraction, and relationship extraction capabilities. Azure AI Document Intelligence is for document analysis and form extraction. Azure AI Search is for indexing and search. Azure AI Translator is for translation.

Answer

Azure AI Document Intelligence

Answer

Azure AI Translator

Answer

Azure AI Search

Question 14

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 15

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