AI-900 Question 26
Single answerA retail company has a large repository of scanned invoices, customer feedback documents, and product manuals stored in multiple formats, including PDFs, images, and Word files. They want to analyze these documents to extract key information such as customer names, product details, and trends from feedback to improve their services. Which Azure AI workload is most suitable for this scenario?
- A
Azure Machine Learning
- B
Azure Cognitive Search
- C
Azure Bot Services
- D
Azure Form Recognizer
Show answer and explanation
Correct answer: B
Explanation
Azure Cognitive Search is the most appropriate choice for knowledge mining workloads because it provides tools to ingest, enrich, and explore data from structured and unstructured sources. In this scenario, the retail company needs to analyze a diverse set of documents to extract insights, which aligns perfectly with the capabilities of Azure Cognitive Search.
- A. Incorrect.
Azure Machine Learning is designed for building, training, and deploying machine learning models, which is not directly related to extracting and analyzing data from a large repository of documents.
- B. Correct.
Azure Cognitive Search is specifically designed for knowledge mining workloads. It can index, search, and analyze structured and unstructured data, making it the best fit for extracting information from diverse documents.
- C. Incorrect.
Azure Bot Services is used to build conversational AI solutions and chatbots. It is not suitable for extracting and analyzing data from documents.
- D. Incorrect.
Azure Form Recognizer is useful for extracting structured data from specific documents like forms, invoices, or receipts, but it is not a comprehensive solution for large-scale knowledge mining across diverse document types.