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

Question 1

Your organization has a large set of PDF invoices stored in Azure Blob Storage. You need to extract line-item details (product names, quantities, prices) and store them in Azure SQL Database for downstream reporting. The invoices have varied layouts. Which Azure AI service should you use?

Accepted Answer

Azure AI Document Intelligence. Option B is correct because Document Intelligence (formerly Form Recognizer) is designed to extract structured data from documents with varied layouts using prebuilt or custom models. Option A is wrong because Computer Vision can extract text but not structured line items. Option C is wrong because Language Service is for NLP tasks, not document extraction. Option D is wrong because Cognitive Search is for indexing and search, not extraction from documents.

Answer

Azure AI Computer Vision

Answer

Azure AI Language Service

Answer

Azure AI Search

Question 2

You are designing a solution to extract customer names and addresses from scanned handwritten forms. The forms are stored as images in Azure Blob Storage. The extraction must achieve high accuracy with minimal manual review. Which combination of Azure AI services should you use?

Accepted Answer

Azure AI Document Intelligence with a custom model trained on handwritten forms. Option D is correct because Document Intelligence's custom model can be trained on handwritten forms, and Language Service can be used to post-process entities. However, the best approach is to use Document Intelligence with a custom model trained on handwriting. Option A is wrong because Computer Vision OCR does not handle handwriting well. Option B is wrong because Language Service alone cannot extract from images. Option C is wrong because Form Recognizer (Document Intelligence) prebuilt models are not optimized for handwriting.

Answer

Azure AI Document Intelligence with prebuilt invoice and receipt models

Answer

Azure AI Language Service with custom Named Entity Recognition (NER)

Answer

Azure AI Computer Vision with OCR and Azure AI Search

Question 3

You are building an Azure AI Search solution to index a collection of technical manuals. Users need to find documents by searching for specific terms and also have the ability to filter by document category. Which feature should you configure in the index to support filtering?

Accepted Answer

Set the 'filterable' property to true on the category field. Option A is correct because filterable fields in Azure AI Search allow filtering of search results. Option B is wrong because facetable enables drill-down navigation, not direct filtering. Option C is wrong because sortable does not enable filtering. Option D is wrong because searchable determines full-text search, not filtering.

Answer

Set the 'facetable' property to true on the category field

Answer

Set the 'searchable' property to true on the category field

Answer

Set the 'sortable' property to true on the category field

Question 4

Your organization has a large corpus of legal documents stored in Azure Blob Storage. You need to build a solution that allows lawyers to ask natural language questions and get answers directly from the documents, without moving data out of Azure. Which service should you use?

Accepted Answer

Azure AI Search with semantic search. Option C is correct because Azure AI Search with semantic search can index documents and return answers using natural language queries. Option A is wrong because Document Intelligence extracts structured data but does not provide Q&A. Option B is wrong because Language Service provides Q&A but requires a knowledge base. Option D is wrong because Computer Vision is for image analysis.

Answer

Azure AI Document Intelligence

Answer

Azure AI Language Service with custom question answering

Answer

Azure AI Computer Vision

Question 5

You are using Azure AI Search to index a set of PDF documents. The index includes a 'content' field with the extracted text. Users report that when they search for 'budget forecast', documents containing only 'budget' or 'forecast' are ranked lower than expected. Which configuration change would improve the ranking for multi-word queries?

Accepted Answer

Enable semantic search on the index. Option B is correct because enabling semantic search improves ranking by understanding the context of multi-word queries. Option A is wrong because adding more fields does not directly improve ranking for multi-word queries. Option C is wrong because setting a higher boost on 'content' does not fix the issue of missing proximity. Option D is wrong because changing the analyzer to a different language may not help.

Answer

Add a separate field for each word in the document

Answer

Change the analyzer to a custom analyzer that splits on spaces only

Answer

Set the 'content' field to a higher boosting value

Question 6

You are building a knowledge mining solution using Azure AI Search. You need to ensure that sensitive information such as credit card numbers is automatically removed from the indexed content. Which built-in skill should you add to your skillset?

Accepted Answer

PII Detection skill. Option B is correct because the Text Translation skill does not handle sensitive data. Option C is correct because the Entity Recognition skill can detect entities but not redact. The correct skill is Text Analytics for health, but for PII redaction, use the PII detection skill (part of Azure AI Language). However, among the options, the correct one is not listed directly. Assuming the correct answer is the 'Text Analytics for PII' skill, but since it's not an option, the closest is 'Entity Recognition' which can detect but not redact. The question may be outdated. For the sake of this exercise, Option A is correct because the 'Conditional skill' can be used to conditionally redact, but that is not built-in. Actually, the built-in skill for PII redaction is 'PII detection' (preview). In the official skills, there is 'PII detection skill'. So I'll set Option D as correct.

Answer

Entity Recognition skill

Answer

Conditional skill

Answer

Text Translation skill

Question 7

Your organization is using Azure AI Document Intelligence to process expense reports. The reports are submitted as images and need to be classified into categories (e.g., travel, office supplies) before extraction. Which feature of Document Intelligence should you use?

Accepted Answer

Custom classification model. Option C is correct because Document Intelligence supports custom classification models for categorizing documents. Option A is wrong because OCR is for text extraction, not classification. Option B is wrong because layout extraction extracts text structure, not categories. Option D is wrong because prebuilt models are for specific document types, not custom categories.

Answer

OCR capability

Answer

Layout extraction

Answer

Prebuilt expense report model

Question 8

You are building an Azure AI Search solution that indexes data from multiple sources, including SQL Database and Azure Blob Storage. The index must be updated within 15 minutes of any source change. Which approach should you use to achieve near-real-time indexing?

Accepted Answer

Use the push API to send updates as soon as data changes. Option B is correct because the push API allows you to send updates directly to the index, providing near-real-time indexing. Option A is wrong because indexer-based scheduled indexing has a minimum interval of 5 minutes, and changes may not be picked up immediately. Option C is wrong because incremental enrichment is for AI enrichment, not for data updates. Option D is wrong because semantic search does not affect update frequency.

Answer

Enable incremental enrichment on the skillset

Answer

Use an indexer with a schedule set to run every 5 minutes

Answer

Enable semantic search to speed up indexing

Question 9

You are using Azure AI Language Service to extract key phrases from customer reviews. You notice that for reviews containing the word 'not good', the service sometimes extracts 'good' as a key phrase. What is the most likely reason?

Accepted Answer

Key phrase extraction does not consider negation. Option B is correct because the key phrase extraction model does not perform sentiment analysis; it extracts phrases based on frequency and relevance, ignoring negation. Option A is wrong because the language model is accurate for most languages. Option C is wrong because prebuilt models are generally robust. Option D is wrong because the service does not have a parameter to exclude negative phrases.

Answer

The language detection model misidentified the language

Answer

You need to set a confidence threshold to exclude negative phrases

Answer

The service is not trained on your specific domain

Question 10

You are building a knowledge mining solution using Azure AI Search with AI enrichment. Which TWO built-in skills can be used to extract information from images embedded in documents?

Accepted Answer

Image Analysis skill. Options A and D are correct. OCR skill extracts text from images. Image Analysis skill extracts visual features (captions, tags). Option B is wrong because Entity Recognition skill works on text. Option C is wrong because Key Phrase Extraction works on text. Option E is wrong because Text Translation works on text.

Answer

Entity Recognition skill

Answer

Key Phrase Extraction skill

Answer

Text Translation skill

Question 11

Your organization is using Azure AI Document Intelligence to process a mix of invoices and purchase orders. You need to ensure that documents are correctly classified before extraction. Which THREE steps should you take?

Accepted Answer

Create a custom classification model in Document Intelligence. Options A, B, and D are correct. A: Create a custom classification model to differentiate document types. B: Label at least 5 samples per class to train the classifier. D: Use the classification model as part of a composed model or in a workflow. Option C is wrong because prebuilt models are for extraction, not classification. Option E is wrong because training with only one sample is insufficient.

Answer

Train the classification model with one sample per type

Answer

Use the prebuilt invoice and purchase order models for classification

Question 12

Which TWO features of Azure AI Search allow you to improve the relevance of search results for users?

Accepted Answer

Semantic search. Options B and D are correct. B: Scoring profiles allow you to boost results based on criteria. D: Semantic search improves relevance by understanding query intent. Option A is wrong because filterable does not affect ranking. Option C is wrong because suggesters are for autocomplete, not relevance. Option E is wrong because synonym maps expand queries but do not directly improve relevance ranking.

Answer

Synonym maps

Answer

Suggesters

Answer

Filterable fields

Question 13

You are building a solution to extract customer feedback from PDF documents stored in Azure Blob Storage. The solution must extract key phrases and sentiment scores, but you cannot use any pre-built models from Azure AI Language. What should you use?

Accepted Answer

Train a custom entity extraction model in Azure AI Document Intelligence. Option D is correct because Azure AI Document Intelligence (formerly Form Recognizer) can be trained with custom models to extract entities like key phrases. Options A and B are pre-built models, which are disallowed. Option C is for extraction of text layout, not semantic entities.

Answer

Use the sentiment analysis capability in Azure AI Language

Answer

Use Azure AI Language's pre-built key phrase extraction API

Answer

Use Azure AI Document Intelligence with the pre-built read model

Question 14

You need to implement a solution that searches through a collection of scanned invoices and extracts invoice numbers, dates, and total amounts. The solution must run on a schedule without manual intervention. Which Azure service should you use?

Accepted Answer

Azure AI Document Intelligence. Option A is correct because Azure AI Document Intelligence is designed for extracting structured data from documents. Option B is for AI model deployment, not extraction. Option C is for indexing data for search. Option D is for conversational AI.

Answer

Azure Bot Service

Answer

Azure AI Search with built-in skills

Answer

Azure AI Foundry model catalog

Question 15

Your company has a large collection of legal contracts in PDF format stored in Azure Blob Storage. You need to extract key clauses, parties, and effective dates using a custom model in Azure AI Document Intelligence. The model must be retrained monthly as new contract templates are added. What is the recommended approach to handle model versioning and retraining?

Accepted Answer

Train a new model version using the 'compose' operation or copy the existing model and retrain with new samples. Option C is correct because Azure AI Document Intelligence allows training a new model version from an existing model using supervised training, preserving previously learned patterns. Option A is inefficient; Option B is not a feature; Option D is not supported.

Answer

Use a multi-model ensemble by training separate models per template

Answer

Retrain the model from scratch each month using all historical data

Answer

Use Azure Machine Learning pipelines to automate retraining and deploy a new endpoint