AI-102 Implement knowledge mining and document intelligence solutions — 10 Questions

Question 1

A company is building a knowledge mining solution using Azure AI Search. They need to extract key phrases from a large set of documents in multiple languages. Which skill should they 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 the most important phrases from text, which directly supports the requirement to extract key phrases from documents. Azure AI Search's built-in Key Phrase Extraction skill leverages natural language processing to analyze text and return a list of key phrases, making it the appropriate skill for this knowledge mining solution.

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Sentiment Analysis skill

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Language Detection skill

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Entity Recognition skill

Question 2

A healthcare organization uses Azure Document Intelligence to process patient intake forms. They notice that the confidence scores for field extraction are low. What is the most likely cause?

Accepted Answer

The custom model was trained with only 10 labeled forms. Custom models in Azure Document Intelligence require a minimum of five labeled forms for training, but low confidence scores typically indicate insufficient training data. With only 10 labeled forms, the model lacks enough examples to generalize well across variations in handwriting, formatting, and field values, leading to poor extraction confidence.

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The document resolution is too low

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The document layout is not analyzed

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The batch processing size is too large

Question 3

A company uses Azure Document Intelligence to extract data from invoices. They deploy the model to a container for on-premises processing. After deployment, they notice that the container consumes more memory than expected. What should they do to optimize memory usage?

Accepted Answer

Set the 'Memory' environment variable to a lower value in the container configuration. Option A is correct because Azure Document Intelligence containers expose a 'Memory' environment variable that allows you to limit the container's memory allocation. By setting this variable to a lower value, you constrain the container's memory usage, which directly addresses the issue of higher-than-expected consumption. This is the recommended approach for optimizing memory in containerized deployments.

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Use the 'Read' model instead of the 'Layout' model

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Use the cloud API instead of the container

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Reduce the batch size in the client application

Question 4

A company builds a knowledge mining solution using Azure AI Search with a custom skillset that includes an OCR skill. They want to ensure that images embedded in PDFs are processed. What should they configure?

Accepted Answer

Set the 'imageAction' parameter to 'generateNormalizedImages'. Option C is correct because the 'imageAction' parameter in Azure AI Search's OCR skill controls whether images embedded in documents (including PDFs) are extracted and processed. Setting it to 'generateNormalizedImages' ensures that images within PDFs are normalized and passed to the OCR skill for text extraction, which is essential for processing embedded images.

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Set the 'defaultLanguageCode' to 'en'

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Set the 'textExtractionAlgorithm' to 'printed'

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Set the 'lineEnding' parameter to 'space'

Question 5

A company uses Azure Document Intelligence to extract data from tax forms. They need to improve accuracy for a specific field. Which TWO actions should they take?

Accepted Answer

Label more examples of the specific field in the training set. Option A is correct because labeling more examples of the specific field in the training set directly provides the custom model with additional ground-truth annotations for that field. This increases the model's ability to learn the variations in handwriting, formatting, and layout for that field, which is the most effective way to improve extraction accuracy for a targeted field in Azure Document Intelligence custom models.

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Increase the batch size in the analysis request

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Reduce the image resolution to 200 DPI

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Use the prebuilt-tax.us model

Question 6

A company uses this skillset in an Azure AI Search enrichment pipeline. They notice that the enrichment pipeline fails when processing a document larger than 5000 characters. What is the most likely cause?

Accepted Answer

The output field mapping is missing or incorrect. The enrichment pipeline fails because the output field mapping is missing or incorrect. When a skillset processes documents, the output of each skill must be explicitly mapped to an index field; if this mapping is absent or misconfigured, the pipeline cannot store the enriched data and fails, especially for larger documents that produce more output data.

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The maximum page length is too small

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The default language code is not supported

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The text split mode should be 'sentences'

Question 7

A law firm uses Azure Document Intelligence to extract clauses from legal contracts. They have a custom model trained on 15 labeled contracts. The model extracts clauses with high confidence on similar documents but fails to extract correct clauses from a new batch of contracts that have a different font and layout. The firm needs to improve extraction accuracy without retraining the model from scratch. The solution must minimize manual effort and cost. What should they do?

Accepted Answer

Create a composed model that includes the existing model and a new model trained on 5 contracts with the new layout. Option D is correct because creating a composed model in Azure Document Intelligence allows you to combine the existing model (trained on the original layout) with a new model trained on just 5 labeled contracts from the new layout. This approach improves accuracy on the new layout without retraining from scratch, minimizing manual effort and cost by leveraging the composed model's ability to route documents to the appropriate sub-model based on layout similarity.

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Use the prebuilt-layout model to extract clauses instead

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Increase the OCR confidence threshold in the analysis request

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Label 15 more contracts with the original layout and retrain the model

Question 8

A company uses Azure Cognitive Search to index customer support emails. They need to implement a custom skill that extracts the sentiment of the email body and also identifies the primary product mentioned. The custom skill is a Python function deployed as an Azure Function. They want to ensure the skill can process multiple documents concurrently and handle errors gracefully. Which THREE configurations should they apply?

Accepted Answer

Set the inputs of the skill to include the 'text' field from the enriched document and the 'product' field from a previous skill.. Option B is correct because the custom skill must receive the email body text for sentiment analysis and the product field from a prior skill (e.g., a key phrase extraction or entity recognition skill) to identify the primary product. This ensures the Azure Function has all required inputs to produce the desired outputs (sentiment score and product name).

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Set continueOnError to false to ensure that if the skill fails for one document, the entire indexer run stops.

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Set the context property to '/document/pages/*' to process each page of the email individually.

Question 9

Refer to the exhibit. You have a skillset with two skills. You run the indexer and find that the output field 'organizations' is empty for documents that clearly contain organization names. The 'keyPhrases' output is populated correctly. What is the most likely cause of the issue?

Accepted Answer

The skill is not configured with a 'defaultLanguageCode' and the documents are not in English.. The 'keyPhrases' output is populated correctly, indicating the text extraction and overall pipeline are functional. The Entity Recognition skill requires a 'defaultLanguageCode' to correctly identify entities; if it is not set and the documents are not in English, the skill may fail to extract organizations, resulting in an empty 'organizations' field. This is a known behavior where the skill defaults to English and cannot process other languages without explicit configuration.

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The skill's 'name' property is set to '#1', which is invalid.

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The 'categories' property is misspelled; it should be 'entityCategories'.

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The input source '/document/content' is incorrect; it should be '/document/text'.

Question 10

You are a developer for a legal firm. The firm uses Azure Cognitive Search to index legal documents. They have a custom skill that performs OCR on scanned PDFs using Azure Form Recognizer. The skill is implemented as an Azure Function. Recently, the indexer has been failing with the error: "The request was canceled due to the configured HttpClient.Timeout of 100 seconds elapsing." The documents are large (up to 200 pages each). The skill calls the Form Recognizer API asynchronously. You need to resolve the timeout issue without losing the ability to process large documents. Current configuration: batchSize = 1, maxPageSize = 100, timeout = 100 seconds. You cannot change the execution time of the Form Recognizer API. What should you do?

Accepted Answer

Decrease the batchSize to 1 and increase the timeout property in the skillset skill definition to PT300S.. Option C is correct because the error indicates the HttpClient timeout (100 seconds) is too short for large documents processed by the custom skill. Increasing the timeout to PT300S (300 seconds) in the skill definition allows the Azure Function to wait longer for the asynchronous Form Recognizer API to complete, while keeping batchSize=1 ensures each invocation handles one document at a time, preventing overload and maintaining reliability for large PDFs.

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Increase the batchSize to 10 to process more documents per invocation, reducing the number of total invocations.

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Increase the maxPageSize to 500 to reduce the number of API calls to Form Recognizer.

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Remove the custom skill and use the built-in OCR skill in the skillset.