AI-102 Implement knowledge mining and information extraction solutions Practice Test 2 — 15 Questions

Question 1

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?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

The context of the SentimentSkill is set to an array, which is not supported. Setting the context of the SentimentSkill to an array (e.g., '/document/pages') causes the skill to run once on the entire array of pages, rather than iterating over each page individually. This results in incorrect sentiment analysis because the skill receives a collection of strings as a single input, which is not the intended per-page processing. The correct configuration is to set the context to '/document/pages/*' so that the skill executes on each page separately.

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The maximumPageLength of 5000 is too high for sentiment analysis

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The input source for SentimentSkill should be '/document/pages/*' but the SplitSkill output is named 'pages', so the input should be '/document/pages'

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The SentimentSkill uses an incorrect @odata.type version

Question 2

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'. The parsingMode set to 'json' tells the indexer to expect JSON files, not PDFs. Since PDFs are binary or text-based, the indexer cannot extract any content, but because it does not fail (JSON parsing simply returns no text), no error is reported. Changing parsingMode to 'default' or 'text' allows the indexer to correctly parse the PDF binary stream and extract text.

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

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

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

Question 3

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. The error message explicitly states that the data source connection string is invalid, even though you have verified the string itself is correct. This typically occurs when the connection string is not stored correctly in the search service (e.g., it was truncated, escaped improperly, or contains special characters that were misinterpreted) or when the data source configuration lacks the correct credentials (e.g., the managed identity is not enabled, or the key/secret is missing). Option D directly addresses this mismatch between a verified string and the service's stored configuration.

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The --query parameter is incorrectly formatted

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The skillset name does not exist in the search service

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The parsingMode should be 'json' for PDF files

Question 4

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. Azure AI Document Intelligence custom extraction model is the correct choice because it is specifically designed to extract structured fields (like clauses, parties, and dates) from documents such as PDF contracts. It uses a prebuilt layout model combined with custom training on a small set of labeled documents, minimizing manual labeling effort while achieving high accuracy through transfer learning and table/key-value pair extraction.

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Azure OpenAI Service with GPT-4 prompt engineering

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Azure AI Search with built-in blob indexing

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Azure AI Language custom named entity recognition

Question 5

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. Semantic ranking re-ranks search results using deep learning models to understand the intent and context of the query, rather than just keyword matching. Since the content field contains large text blocks from PDFs, semantic ranking can extract the most relevant passages and improve result relevance without requiring re-indexing or modifying the existing index schema.

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Enable spell correction in the search query

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Add a custom scoring profile based on term frequency

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Change the index analyzer to a different language

Question 6

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). Azure AI Language provides the specific capabilities needed: key phrase extraction to identify the customer's issue, entity recognition to identify the resolution, and sentiment analysis to determine the call sentiment. These are all built-in features of the Language service, making it the single-service solution for this knowledge mining task.

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Azure AI Translator and Azure AI Search

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Azure AI Speech and Azure AI Search

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Azure AI Document Intelligence and Azure AI Language

Question 7

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. Configuring the custom skill to execute in batch mode reduces the number of HTTP requests to the third-party API by processing multiple documents per invocation, while setting a retry policy on the indexer ensures that failed documents due to transient HTTP 429 errors are automatically retried. This combination prevents data loss by not skipping documents and by handling rate-limiting gracefully.

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Increase the number of partitions in the Azure AI Search service

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Enable indexer error handling to skip failed documents

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Scale out the Azure Function to multiple instances

Question 8

You need to extract product codes (e.g., 'PRD-12345') from scanned invoices using Azure AI Document Intelligence. The product codes always follow a pattern of three uppercase letters, a hyphen, and five digits. Which approach should you use?

Accepted Answer

Build a custom skill in Azure AI Search using a Python regex. The pre-built invoice model in Azure AI Document Intelligence does not support adding custom fields with regex patterns; custom field extraction with regex is only available in custom models. Option B is correct: you can build a custom skill in Azure AI Search using a Python regex to extract the product codes from the text output of Document Intelligence. This approach allows you to apply a regex pattern to the extracted content, providing accurate and flexible extraction without needing to train a model or use a large language model. Option C is less suitable because training a custom NER model requires labeled data and may not guarantee exact pattern matching. Option D is overkill for a simple regex pattern.

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Use the pre-built invoice model in Azure AI Document Intelligence with a regex field extraction

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Train a custom NER model in Azure AI Language

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Use Azure OpenAI GPT-4 with document vision to extract the codes

Question 9

Your Azure AI Search indexer is failing to index a large number of PDFs from Azure Blob Storage. The error log shows 'Document extraction timeout' for many documents. You need to resolve this issue without losing data. What should you do?

Accepted Answer

Increase the indexer execution timeout in the indexer definition. The 'Document extraction timeout' error indicates that the indexer is taking longer than the default 24-hour timeout to process certain PDFs. Increasing the indexer execution timeout in the indexer definition allows the indexer to continue processing these large documents without losing data, as it extends the maximum time the indexer can run for a single execution.

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Change the parsing mode of the indexer to 'text'

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Split large PDFs into smaller files before uploading

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Enable incremental enrichment on the skillset

Question 10

You are building a knowledge mining solution to extract key information from handwritten forms. The forms contain checkboxes, signatures, and handwritten text. Which Azure AI service should you use?

Accepted Answer

Azure AI Document Intelligence layout model. Azure AI Document Intelligence's layout model is designed to extract text, tables, checkboxes, signatures, and structure from documents, including handwritten forms. It combines OCR with deep learning models to understand the spatial relationships between elements, making it the correct choice for this scenario.

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Azure AI Language

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Azure AI Speech

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Azure AI Vision OCR

Question 11

Which TWO actions should you perform to ensure that an Azure AI Search indexer can successfully enrich documents using a custom skill that calls an external API?

Accepted Answer

Configure a retry policy in the skillset definition for the custom skill. Option B is correct because a custom skill in an Azure AI Search skillset can define a retry policy (for example, via the WebApiSkill's 'retryPolicy' or by setting batchSize and maxAttempts) so that transient failures or throttling from the external API do not cause the whole indexer run to fail. Option C is correct because when the custom skill calls an Azure Function or other protected endpoint, the search service must authenticate; assigning a managed identity to the search service and granting it access (for example, via Azure RBAC or function authorization) lets the indexer invoke the external API securely without embedding keys. Option A is not required because CORS governs browser-based cross-origin requests, not server-to-server calls made by the indexer. Option D is not appropriate because indexer execution timeouts are bounded by the service tier and cannot be set to unlimited. Option E is not a real Azure AI Search indexer setting; there is no 'allowed domains list' for indexers, so it does not enable custom skill enrichment.

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Enable CORS on the Azure Function app to allow cross-origin requests

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Set the indexer's execution timeout to unlimited

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Add the external API endpoint to the indexer's allowed domains list

Question 12

You deploy the ARM template shown in the exhibit to create an Azure AI Search indexer. The indexer fails to run, and you see an error that the skillset 'demo-skillset' does not exist. What is the most likely cause?

Accepted Answer

The skillset resource was not deployed before the indexer. The ARM template deploys resources in parallel by default, so the indexer can be created before the skillset. Since the indexer references 'demo-skillset' in its configuration, the deployment fails because the skillset resource does not exist at the time the indexer is created. To fix this, you must add a 'dependsOn' property in the indexer resource definition to ensure the skillset is deployed first.

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The field mapping source field 'metadata_storage_path' is incorrect

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The schedule start time is in the past, causing the indexer to be disabled

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The data source 'demo-datasource' does not exist

Question 13

You have an Azure AI Search solution that indexes customer support tickets. The index includes a 'category' field that should be automatically populated using a custom skill that calls an Azure Machine Learning model. However, the skill fails intermittently with HTTP 429 errors. What is the most likely cause and the best fix?

Accepted Answer

The skill execution batch size is too large, causing high call volume; reduce batch size in the skillset definition.. HTTP 429 errors indicate rate limiting, which occurs when the custom skill sends too many requests to the Azure Machine Learning endpoint in a short period. The skill execution batch size controls how many documents are processed per skill invocation; a large batch size increases call volume and triggers throttling. Reducing the batch size in the skillset definition lowers the request rate, preventing 429 errors.

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The skill has a timeout set too short; increase the timeout.

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The indexer runs too frequently; increase the indexer schedule interval.

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The skill output field mapping is incorrect; fix the mapping.

Question 14

You are designing a knowledge mining solution to extract information from scanned invoices stored as multi-page TIFF images. Which two Azure AI services should you combine to extract text and structure the data?

Accepted Answer

Azure AI Document Intelligence and Azure AI Vision. Azure AI Document Intelligence is the correct choice because it is purpose-built to extract text, key-value pairs, and tables from documents such as scanned invoices, and it natively supports multi-page TIFF input through its Read and prebuilt-invoice models. Azure AI Vision complements it by providing OCR/image analysis capabilities for extracting text from images when needed, making the combination the right fit for scanned invoice processing. Azure AI Language and Azure AI Search (option A) focus on NLP and indexing rather than document/image text extraction, so they don't address the OCR requirement. Azure AI Search and Azure AI Vision (option C) lack the structured document extraction that Document Intelligence provides, and Azure AI Translator and Azure AI Document Intelligence (option D) adds translation, which is irrelevant to extracting and structuring invoice data.

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Azure AI Language and Azure AI Search

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Azure AI Search and Azure AI Vision

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Azure AI Translator and Azure AI Document Intelligence

Question 15

Your organization uses Microsoft Purview to catalog data assets. You need to enable knowledge mining on these assets to allow users to search across structured and unstructured data. Which integration should you use to connect Microsoft Purview with Azure AI Search?

Accepted Answer

Create an Azure AI Search indexer that connects to Microsoft Purview's data source using the Purview REST API.. Azure AI Search can use an indexer to connect to Microsoft Purview's Atlas REST API, which allows it to crawl and ingest metadata (e.g., table schemas, file types, classifications) from Purview's data catalog. This integration enables knowledge mining by making both structured and unstructured data assets searchable through a unified Azure AI Search index, without requiring manual metadata push or intermediate tools.

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Configure Microsoft Purview to push metadata directly to an Azure AI Search index.

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Use Microsoft Foundry's built-in connector to import Purview metadata.

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Use Power BI to export Purview metadata to Azure AI Search.