AI-102 Implement knowledge mining and document intelligence solutions Practice Test 2 — 15 Questions

Question 1

A company uses Azure AI Document Intelligence with a custom extraction model to process purchase orders. They need to extract line items, including product codes, quantities, and prices, from tables in the documents. The model currently returns line items but the product codes are frequently missing or misaligned. You need to improve the extraction of line items with minimal effort. What should you do?

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

Accepted Answer

Enable the model's table extraction feature and label the line item table columns in the training documents.. For custom models, table extraction is enabled by labeling tables during training. By labeling the line item table columns, the model learns to associate product codes, quantities, and prices with the correct columns, improving alignment and completeness. Adding unlabeled documents, switching to a prebuilt model, or relying on post-processing do not address the root cause of misalignment.

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Increase the number of training documents by adding 50 more unlabeled purchase orders.

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Use the model's confidence scores to programmatically flag missing product codes and manually correct them in post-processing.

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Switch to the prebuilt invoice model, which automatically extracts line items from any document type.

Question 2

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 3

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 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'. 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 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. 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 6

Drag and drop the steps to set up Azure AI Content Safety for content moderation into the correct order.

Accepted Answer

1. Create Content Safety resource, 2. Get endpoint and key, 3. Call Content Safety API, 4. Analyze response, 5. Act on results. The correct order for setting up Azure AI Content Safety is: first create the Content Safety resource, then obtain its endpoint and key, call the Content Safety API with the content, analyze the response for moderation results, and finally act on the analysis (e.g., block or flag content). This sequence ensures proper authentication and logical flow from setup to action.

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1. Get endpoint and key, 2. Create Content Safety resource, 3. Call Content Safety API, 4. Analyze response, 5. Act on results

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1. Create Content Safety resource, 2. Get endpoint and key, 3. Call Content Safety API, 4. Act on results, 5. Analyze response

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1. Call Content Safety API, 2. Analyze response, 3. Create Content Safety resource, 4. Get endpoint and key, 5. Act on results

Question 7

Match each Azure AI tool to its purpose.

Accepted Answer

Azure Cognitive Search: AI-powered cloud search service for indexing and querying diverse content.. Correct matches: Azure Cognitive Search is an AI search service; Azure Bot Service is for building bots; Azure Cognitive Services offers pre-built AI APIs. Common confusions include swapping Cognitive Search with Machine Learning, and Bot Service with Cognitive Services.

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Azure Cognitive Search: Platform for building, training, and deploying machine learning models.

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Azure Bot Service: Pre-built APIs for vision and speech.

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Azure Cognitive Services: Tools to build and manage bots.

Question 8

You are developing an Azure AI Search solution that indexes scanned PDF invoices. The indexer must extract text from the PDFs and also recognize entities such as organization names and dates. You want to use built-in cognitive skills to minimize custom code. Which combination of skills should you include in the skillset?

Accepted Answer

OCR skill and Entity Recognition skill. The OCR skill extracts text from scanned PDFs, and the Entity Recognition skill identifies organizations and dates from the extracted text. Using these built-in skills avoids custom code and satisfies both requirements efficiently. Other combinations either lack OCR or entity recognition, or provide unrelated functionality.

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Key Phrase Extraction skill and Language Detection skill

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Text Merge skill and Text Split skill

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Image Analysis skill and Sentiment skill

Question 9

A company uses Azure Document Intelligence with a custom neural model to extract data from purchase orders. The model was trained on 50 labeled samples and performs well on similar documents. However, when processing new purchase orders from a different supplier, the model fails to extract the 'TotalAmount' field accurately. What should you do to improve the model's performance on the new supplier's documents?

Accepted Answer

Add labeled samples from the new supplier's purchase orders and retrain the model.. To improve extraction for documents from a new supplier, the custom neural model needs exposure to that supplier's document variations. Adding labeled samples and retraining is the direct method to enhance the model's field extraction accuracy. Other options do not address the model's knowledge gap.

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Increase the model's confidence threshold for the 'TotalAmount' field.

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Use the model's composed model feature to combine it with a prebuilt model.

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Switch to the prebuilt invoice model and map the 'TotalAmount' field to 'InvoiceTotal'.

Question 10

You are building an Azure AI Search solution that uses a custom skill to enrich documents. The custom skill is implemented as an Azure Function. You need to ensure that the custom skill can access the documents' content and output enriched data. Which two actions should you perform? (Choose two.)

Accepted Answer

Ensure the Azure Function accepts a JSON payload with the expected input and returns a JSON response.. The custom skill must be defined in the skillset with proper input and output mappings to integrate with the enrichment pipeline. Additionally, the Azure Function must handle the JSON payload correctly. These two actions ensure the skill can access document content and return enriched data for indexing.

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Add the Azure Function's output to the index's field mappings.

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Set the custom skill's batch size to 1 to ensure sequential processing.

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Configure the indexer to use a managed identity to access the Azure Function.

Question 11

You are building an Azure AI Search knowledge mining pipeline that enriches scanned PDF invoices. The PDFs are stored in Azure Blob Storage, and you need to extract text from each page before running downstream entity recognition. The solution must minimize development effort and rely on a built-in cognitive skill. Which skill should you add to the skillset?

Accepted Answer

OcrSkill. The pipeline must convert image-based invoice content into text before any language skill can run. OcrSkill is the built-in Azure AI Search cognitive skill designed for that conversion and outputs a text field that downstream skills consume. Image analysis, entity recognition, and key phrase extraction all assume text already exists, so they cannot satisfy the extraction requirement on their own.

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EntityRecognitionSkill

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ImageAnalysisSkill

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KeyPhraseExtractionSkill

Question 12

You are building a knowledge mining solution with Azure AI Search. The solution indexes scanned PDF reports stored in Azure Blob Storage. Each report contains multiple embedded images with text that must be searchable. You have already created a data source, index, and indexer. You need to ensure that text from the embedded images is extracted and mapped to the 'content' field in the index. Which two actions should you perform? (Choose two.)

Accepted Answer

Enable image extraction by setting the 'imageAction' parameter to 'generateNormalizedImages' in the indexer definition.. To make text from embedded images searchable, you must first extract the images from the documents by setting imageAction to generateNormalizedImages. Then, you add an OCR skill with context /document/normalized_images/* to process each image and output the recognized text. This text can then be mapped to the content field. Without both actions, the OCR skill either has no input or does not run per image.

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Configure the indexer to use the 'text' parsing mode instead of the default 'json' mode.

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Add an Entity Recognition skill to detect and extract text from images.

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Add a Shaper skill that merges all image text into a single string before indexing.

Question 13

A company uses Azure Document Intelligence to process purchase orders. They have trained a custom model with 10 labeled samples and deployed it as 'purchaseOrderModel'. When analyzing a new purchase order, the extracted 'TotalAmount' field is often incorrect. The company wants to improve the model's accuracy for this field. What should they do?

Accepted Answer

Retrain the model with additional labeled samples that include variations of the 'TotalAmount' field.. Custom model accuracy in Azure Document Intelligence depends heavily on the quality and quantity of labeled training data. When a specific field like TotalAmount is frequently misidentified, the most effective action is to retrain with more labeled examples that capture the field's variations. This helps the model learn the patterns and contexts associated with that field, leading to better generalization on new documents.

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Switch to the prebuilt invoice model, which automatically extracts total amounts from purchase orders.

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Increase the model's confidence threshold for the 'TotalAmount' field in the project settings.

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Add a labeled sample where the 'TotalAmount' field is left blank to teach the model to ignore missing values.

Question 14

You are designing an Azure AI Search enrichment pipeline that processes scanned PDF invoices stored in Azure Blob Storage. You need to extract both printed text and handwritten notes from the documents before sending the content to an Azure AI Language entity recognition skill. The solution must minimize development effort and cost. Which skill should you add to the skillset?

Accepted Answer

Microsoft.Skills.Vision.OcrSkill. The built-in OCR skill reads printed and handwritten text from images and PDFs and writes the extracted text into the enrichment tree. Because it is a first-party Azure AI Search skill, it requires no custom code, which satisfies the goals of low effort and predictable cost. Other text skills assume machine-readable input and cannot perform image recognition.

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Microsoft.Skills.Text.MergeSkill

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Microsoft.Skills.Custom.WebApiSkill

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Microsoft.Skills.Text.KeyPhraseExtractionSkill

Question 15

A company uses Azure AI Search to index documents from an Azure SQL Database. They have configured an indexer with a skillset that includes a custom skill hosted in an Azure Function. The custom skill enriches each document with a 'category' field. After running the indexer, they notice that the 'category' field is missing in the index for all documents. The Azure Function logs show that it is receiving requests and returning responses. What is the most likely cause?

Accepted Answer

The output field mapping for the custom skill is not correctly configured in the indexer.. The most likely cause is a missing or incorrect output field mapping. The custom skill executes and returns a response, but if the indexer is not configured to map that output to an index field, the enriched data never gets stored. This is a common configuration oversight: the skill definition includes an output, but the indexer's outputFieldMappings must explicitly associate it with a target field.

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The indexer's data source connection string is invalid, so documents are not being retrieved.

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The custom skill's context is set to /document/pages/*, but the documents do not have a 'pages' node.

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The custom skill's Azure Function is returning a 200 OK status but with an empty response body.