AI-102 Implement knowledge mining and information extraction solutions Practice Test 10 — 15 Questions

Question 1

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.. Option A is correct because the Push API (the REST/SDK endpoint that accepts JSON documents directly into an index) lets an application read rows from Azure SQL Database and send them straight to Azure AI Search, bypassing any intermediate storage. Option E is correct because Azure AI Search provides a built-in Azure SQL indexer (created via the portal, REST, or the Azure.Search.Documents SDK) that connects to Azure SQL Database using a change-tracking column (or high-water mark) to pull and index rows on a schedule. Option B is not a direct indexing method for Azure SQL Database; it inserts an unnecessary Data Factory copy to Blob Storage and then relies on a Blob indexer, which is a different data source. Option C is wrong because Azure AI Document Intelligence extracts text from documents (PDFs, images, forms), not from relational Azure SQL tables. Option D is wrong because Event Hubs is a streaming ingestion service and Azure AI Search has no Event Hubs indexer; streaming into the index would still require the Push API.

Answer

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

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

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

Question 2

You run the Azure CLI command 'az search indexer list --search-service mysearch --query "[].{name:name, status:status, lastResult:lastResult}"' and get the above output. Your indexer shows 5 warnings. What should you do to investigate the warnings?

Accepted Answer

Run 'az search indexer show --name myindexer' and review the 'warnings' array in the output.. The 'az search indexer list' command with the query you used returns a summary of indexer status and last result, but it does not include the detailed 'warnings' array. To investigate the 5 warnings, you need to use 'az search indexer show --name myindexer', which returns the full indexer execution history, including a 'warnings' array that lists each warning with its message and details. This allows you to understand the nature of each warning and take corrective action if needed.

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Run 'az search indexer run --name myindexer' to trigger a new run.

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Ignore the warnings because they are not errors.

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Run 'az search indexer reset --name myindexer' to reset the indexer.

Question 3

Your company has a large repository of scanned invoices in PDF format. You need to extract invoice number, date, total amount, and vendor name from these PDFs. Which Azure AI service should you use?

Accepted Answer

Azure AI Document Intelligence. Azure AI Document Intelligence (formerly Form Recognizer) is purpose-built for extracting structured data (fields like invoice number, date, total amount, vendor name) from scanned documents such as invoices and receipts. It uses prebuilt models trained specifically on invoice layouts and supports both OCR and key-value pair extraction from PDFs.

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Azure AI Language

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Azure AI Search

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Azure AI Vision

Question 4

You plan to use Azure AI Search to index a large number of text documents stored in Azure Blob Storage. The documents are in English. You want to automatically extract key phrases from the content during indexing. What should you add to the skillset?

Accepted Answer

Key Phrase Extraction skill. The Key Phrase Extraction skill is the correct choice because it is specifically designed to identify and extract important phrases from text content, which aligns with the requirement to automatically extract key phrases during indexing. This skill is part of Azure AI Search's cognitive skillset and operates on English text to produce a list of key phrases per document.

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Sentiment skill

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Language Detection skill

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Entity Recognition skill

Question 5

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). Azure AI Document Intelligence (formerly Form Recognizer) is purpose-built for extracting structured fields from documents like invoices, receipts, and IDs. Its prebuilt invoice model returns vendor, invoice number, line items, totals, and dates, and its custom/neural models handle both printed and handwritten text. It also natively ingests PDFs, making it the correct choice for scanned invoices with mixed print/handwriting.

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Language Service

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Speech Service

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Computer Vision

Question 6

Your team is using Azure AI Search to index a large collection of technical manuals. Users report that searches for 'disk failure' do not return relevant results because the manuals use terms like 'hard drive crash'. Which feature should you implement to improve recall?

Accepted Answer

Add a synonym map to the index. A synonym map in Azure AI Search allows you to define equivalent terms (e.g., 'disk failure' = 'hard drive crash') so that queries automatically expand to include synonyms. This directly addresses the vocabulary mismatch between user queries and indexed content, improving recall without requiring changes to the documents or queries.

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Apply a filter

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Configure a scoring profile

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Enable semantic search

Question 7

You need to extract entities such as dates, locations, and organization names from unstructured text documents. Which Azure AI service should you use?

Accepted Answer

Azure AI Language Service. Azure AI Language Service provides pre-built capabilities for entity recognition, including extracting dates, locations, and organization names from unstructured text via its Named Entity Recognition (NER) feature. This service is specifically designed for text analytics tasks, making it the correct choice for entity extraction from text documents.

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Computer Vision

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Azure AI Document Intelligence

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Azure AI Speech Service

Question 8

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. A custom skill in the Azure AI Search skillset lets you invoke a custom function (e.g., Azure Function calling Azure AI Language's PII detection or a regex-based redactor) during the enrichment pipeline, before documents are indexed. This ensures PII is detected and redacted/removed at ingestion time, so it never appears in the search index or query results.

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Use Azure AI Search with encryption at rest

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Implement role-based access control on the search index

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Configure field mappings to exclude PII fields

Question 9

You are extracting text from scanned documents that are in French. Which capability of Azure AI Document Intelligence should you use?

Accepted Answer

Read API. The Read API is the correct choice because it is specifically designed for extracting printed and handwritten text from scanned documents, including support for multiple languages like French. It performs optical character recognition (OCR) to digitize text without requiring any additional training or customization, making it ideal for general text extraction from scanned documents.

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Custom model

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Layout model

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Prebuilt invoice model

Question 10

Refer to the exhibit. You have this Azure AI Search indexer configuration. The indexer is failing after processing 6 documents that contain errors. What should you do to ensure the indexer continues processing even if some documents fail?

Accepted Answer

Increase maxFailedItems to a higher value, such as 100. The indexer is failing after processing 6 documents because the `maxFailedItems` threshold has been reached. By increasing `maxFailedItems` to a higher value like 100, the indexer will continue processing even if more documents fail, as long as the total number of failed items stays below the new threshold.

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Decrease the batch size to 5

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Increase batch size to 20

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Remove the schedule to run the indexer on demand

Question 11

Refer to the exhibit. You execute a search query on an Azure AI Search index and get these results. The query was 'brown fox'. Why is the first result scored higher than the second?

Accepted Answer

The first document has a higher term frequency and better term proximity for the query terms. The first document is scored higher because it has a higher term frequency and better term proximity for the query terms 'brown fox'. In Azure AI Search's default scoring profile, documents that contain the query terms more frequently and closer together receive higher relevance scores.

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The first document has a higher value in a scoring profile field

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The first document is more similar to the query in vector space

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The first document was boosted by a semantic ranking function

Question 12

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. The prebuilt-invoice model is specifically trained to extract key fields from invoices, including invoice number, date, total amount, and line items. Unlike other prebuilt models, it has dedicated field extraction for line item details such as description, quantity, unit price, and total, making it the correct choice for this scenario.

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Prebuilt-receipt model

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Prebuilt-idDocument model

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Prebuilt-layout model

Question 13

Your company uses Azure Cognitive Search to index millions of documents. Users report that search results include irrelevant documents. You need to improve search relevance by boosting documents that contain the search term in the title field. Which scoring profile configuration should you use?

Accepted Answer

Create a tagging scoring profile that boosts by the title field with a weight of 10.. A tagging scoring profile boosts documents based on matching tags from a specific field (like title), and the weight parameter controls the boost magnitude. Option B is incorrect because a freshness scoring profile boosts by recency, not by term presence in a field. Option C is incorrect because a distance scoring profile boosts based on geospatial proximity, not text fields. Option D is incorrect because a magnitude scoring profile boosts based on numeric field values, not text fields; a linear boosting function is used for magnitude profiles, but this does not apply to boosting by title.

Answer

Create a freshness scoring profile with a boosting duration of 30 days.

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Create a distance scoring profile with a reference point parameter.

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Create a magnitude scoring profile with a boosting function of 'linear'.

Question 14

You are using Azure AI Language to perform entity recognition on customer feedback. You need to identify the sentiment expressed towards specific entities. Which feature should you use?

Accepted Answer

Sentiment analysis with opinion mining. Sentiment analysis with opinion mining is the correct feature because it not only detects the overall sentiment of a text but also associates specific sentiments with particular entities or aspects mentioned in the text. This allows you to determine, for example, that a customer feels positively about 'product quality' but negatively about 'customer support', which is exactly what the question requires.

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Named Entity Recognition (NER)

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Entity linking

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Key phrase extraction

Question 15

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. Custom neural model. Azure AI Document Intelligence offers custom template models for forms with fixed layouts and custom neural models for forms with varying layouts and handwriting. Neural models use deep learning to handle variability in structure and handwriting, making them ideal for this scenario. Option A (Custom template model) assumes a fixed layout and fails with varying layouts. Option B (Prebuilt-layout model) is a prebuilt model that extracts text, tables, and selection marks but not customized key-value pairs for your forms. Option D (Custom composed model) is a combination of multiple models, but the primary choice for varying layouts is the neural model, not composed. Therefore, C is the best choice.

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Custom template model

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Prebuilt-layout model

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Custom composed model