Microsoft Azure AI Fundamentals AI-900 Practice Question
An organization wants to build an internal system that indexes and analyzes a vast collection of unstructured documents, including text files, PDFs, and images, to help employees quickly find relevant information and uncover hidden insights.
Which AI workload is most appropriate for this scenario?
Knowledge Mining - This is the correct answer. Knowledge mining is the most appropriate AI workload for indexing and analyzing a vast collection of unstructured documents, such as text files, PDFs, and images. It involves extracting insights, identifying patterns, and making the information easily searchable to uncover hidden insights and help users find relevant data quickly.
Natural Language Processing (NLP) is useful for processing and understanding text, knowledge mining includes broader capabilities for extracting insights from various unstructured data types (e.g., text, PDFs and images), making it more suitable for this scenario.
Document Intelligence is focused on understanding and processing documents, such as extracting data from structured documents like forms or invoices, but it may not cover the full breadth of knowledge mining, which includes a wider range of unstructured data sources.
Content personalization is aimed at tailoring content or recommendations to individual users based on their preferences or behaviors. It does not directly address the need to index and analyze a large collection of unstructured documents.
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Microsoft Azure AI Fundamentals AI-900
Describe Artificial Intelligence Workloads and Considerations
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