AI-102 Practice Test 45 — 15 Questions

Question 1

You have the above data source definition for Azure AI Search. You want to index only PDF files from the 'documents' container. How should you modify the data source?

Accepted Answer

Set the 'query' field to a blob prefix that corresponds to the folder containing PDF files.. To filter by file type, set a data change detection policy or use a container query. However, the simplest way is to set a file extraction filter or use a custom indexer parameter. Option D is correct: set the 'dataToExtract' to 'contentAndMetadata' and add a 'parsingMode' for PDF. But the best answer is to set a query parameter to filter by .pdf extension. Option A is incorrect because connection string doesn't filter. Option B is incorrect because container name can't have filters. Option C is correct: set the 'query' field to filter by .pdf. Actually, the query field in the container object can be used to specify a blob prefix. For example, "*.pdf" is not valid; you need to use a prefix. The correct approach is to set a custom indexer parameter 'parsingMode' to 'jsonArray'? No. The proper way is to set the 'dataToExtract' to 'contentAndMetadata' and use a skillset to filter. However, the simplest is to change the container query to filter by folder or prefix. Typically, you would set the container query to a folder that contains only PDFs. Option C is plausible: set the query to filter by .pdf extension. But note that blob storage does not support wildcard queries in the container query; you need to set a prefix. Option D is incorrect because you cannot set file type in connection string. The correct answer is C: set the 'query' field to a prefix that includes only PDFs. However, the exhibit shows empty query. To filter PDFs, you would set a prefix like 'pdf/' if they are in a folder. But the question expects to set the 'parsingMode' to 'jsonArray'? No. Let me think: Azure AI Search has a feature to filter by file type using the 'parsedContent' skill. But the simplest is to set a data change detection policy. Actually, the correct answer is: Option C: Set the 'query' field to a blob prefix that corresponds to PDF files. But since the exhibit has empty query, you can set it to a folder. However, the distractors: Option A: Change the connection string -> no. Option B: Change the container name -> no. Option C: Set the 'query' field to filter by .pdf extension -> this is not directly supported; you need to use a prefix. Option D: Add a 'fileExtension' parameter to the container -> not a valid property. The correct way is to use a custom indexer parameter 'excludedFileNameExtensions' or 'includedFileNameExtensions' in the indexer's parameters. So the answer is: modify the indexer parameters to include only PDF files. But the question asks to modify the data source. Since the data source does not support file extension filtering, the best answer is to change the container query to point to a folder that contains only PDFs. Option C is the closest. I'll go with C.

Answer

Change the container name to 'pdfs' and move all PDFs there.

Answer

Add a 'fileExtension' property to the container object.

Answer

Change the connection string to use a different storage account that contains only PDFs.

Question 2

Which TWO services can be used to enrich an Azure AI Search index with knowledge mining skills? (Choose two.)

Accepted Answer

Azure AI Computer Vision. Options B and D are correct. Azure AI Language Service provides entity recognition, key phrase extraction, etc. Azure AI Computer Vision provides image analysis, OCR, etc. Option A is wrong because Translator is for translation, not enrichment. Option C is wrong because Speech Service is for audio. Option E is wrong because Video Indexer is for video analysis.

Answer

Azure AI Video Indexer

Answer

Azure AI Speech Service

Answer

Azure AI Translator

Question 3

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 4

Your team is implementing a knowledge mining solution using Azure AI Search with custom skills. The custom skill, deployed as an Azure Function, calls a third-party API to enrich documents. You notice that some documents fail enrichment with HTTP 429 (too many requests) errors. You need to ensure all documents are enriched without losing data. What should you do?

Accepted Answer

Configure the custom skill to execute in batch mode and set a retry policy on the indexer. Option D is correct because configuring the custom skill to execute in batch mode with a retry policy allows the skillset to handle throttling by retrying failed batches. Option A is wrong because increasing the number of partitions does not affect custom skill execution. Option B is wrong because scaling the Azure Function may help but the skillset execution is controlled by the indexer. Option C is wrong because Azure AI Search manages retries for built-in skills, but custom skills need explicit retry configuration.

Answer

Increase the number of partitions in the Azure AI Search service

Answer

Enable indexer error handling to skip failed documents

Answer

Scale out the Azure Function to multiple instances

Question 5

Which THREE factors should you consider when designing a knowledge mining solution that uses Azure AI Search and custom skills to extract insights from large volumes of documents?

Accepted Answer

The maximum execution time of the custom skill must fit within the indexer timeout. Options A, C, and E are correct. A: Skillset execution time must be within indexer timeout limits. C: Azure Functions can scale to handle concurrent requests. E: Incremental enrichment reduces reprocessing. Option B is wrong because knowledge stores are for structured output, not performance. Option D is wrong because semantic ranking is for search relevance, not extraction.

Answer

The number of knowledge store projections affects indexing speed

Answer

Semantic ranking configuration must be included in the skillset

Question 6

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 7

You have the above skillset in Azure AI Search. The indexer processes a document with 12,000 characters of content. How many entity recognition skill executions occur?

Accepted Answer

3. The split skill splits content into pages of max 5000 characters with 500 overlap. For 12000 characters, pages: page1 (0-5000), page2 (4500-9500), page3 (9000-12000) => 3 pages. The entity skill runs per page (context /document/pages/*), so 3 executions.

Answer

4

Answer

2

Answer

1

Question 8

You are designing a knowledge mining solution for a legal firm. The solution must index contracts and support queries like 'find all contracts where the governing law is New York'. The contracts are stored as PDFs in Azure Blob Storage. Which approach should you use?

Accepted Answer

Use Azure AI Search with a custom skill to extract the governing law field. Option C is correct because Azure AI Search with a custom skill to extract the governing law field allows indexing that specific metadata. Option A is wrong because full-text search on raw text would not distinguish the governing law field. Option B is wrong because skillsets can extract fields, not just enrich. Option D is wrong because vector search is for semantic similarity, not exact field extraction.

Answer

Use Azure AI Search with vector search

Answer

Use Azure AI Search with a built-in skillset for OCR

Answer

Use Azure AI Search with full-text search on the extracted text

Question 9

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 10

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 11

Your knowledge mining solution uses Azure AI Document Intelligence to extract data from purchase orders. The extracted data is then indexed by Azure AI Search. You need to ensure that the search index includes the purchase order number and total amount as searchable fields. What should you do?

Accepted Answer

Create a custom skill that calls Azure AI Document Intelligence and returns extracted fields, then use outputFieldMappings to map to index fields.. First, use Azure AI Document Intelligence to extract fields. Then, use a custom skill or the Document Extraction skill combined with a Web API skill to pass the extracted data to the indexer. The indexer maps the output to index fields via outputFieldMappings.

Answer

Use Azure AI Document Intelligence's pre-built model to analyze documents and store results in a database, then use a SQL indexer to index the database.

Answer

Use the OCR skill to extract text and then use regular expressions to find PO number and total.

Answer

Manually enter the extracted data into the search index.

Question 12

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 13

You need to build a chatbot that answers questions based on your company's internal knowledge base. The knowledge base consists of Word documents and PDFs. Which service should you use to create a conversational interface that retrieves answers from these documents?

Accepted Answer

Azure AI Search with Azure AI Bot Service. Option D is correct because Azure AI Search can index documents and be used as a data source for a question-answering system, and Azure AI Bot Service can host the chatbot. Option A is wrong because Language Service's QnA Maker (now Custom Question Answering) can use a knowledge base but not directly index documents; it requires a search index for large datasets. Option B is wrong because Document Intelligence extracts data but does not answer questions. Option C is wrong because Computer Vision does not handle text queries.

Answer

Azure AI Language Service - Custom Question Answering

Answer

Azure AI Computer Vision

Answer

Azure AI Document Intelligence

Question 14

You are building a solution to extract key information from invoices using Azure AI Document Intelligence. The invoices contain fields such as invoice number, date, total amount, and line items. However, the model is not correctly extracting the line items. Which prebuilt model should you use?

Accepted Answer

Prebuilt-invoice model. Option B is correct because the prebuilt-invoice model is specifically designed to extract fields from invoices, including line items. Option A is wrong because the prebuilt-layout model extracts text and structure but not specific key-value pairs like line items. Option C is wrong because the prebuilt-receipt model is for receipts, not invoices. Option D is wrong because the prebuilt-idDocument model is for identity documents.

Answer

Prebuilt-receipt model

Answer

Prebuilt-idDocument model

Answer

Prebuilt-layout model

Question 15

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.

AI-102 Practice Test 45 — 15 Questions

Exhibit

AI-102 Practice Test 45 — 15 Questions

Exhibit