Discover how to use the Shaper skill in Azure Cognitive Search to map unstructured data to projections effectively. Perfect for mastering knowledge mining and acing the Microsoft AI-102 certification exam.
Table of Contents
Question
Your organization, Nutex Corporation, is developing an AI-powered knowledge mining solution using Azure Cognitive Search to help a large healthcare provider extract meaningful insights from vast amounts of unstructured medical documents such as patient records, clinical notes, and research papers.
The indexing process has created unstructured data, resulting in a schema difficult to work with and making it hard to map to projections.
You want to simplify mapping the unstructured data to projections in a knowledge store. Which skill should you use?
A. Sentiment Analysis
B. Entity Recognition
C. Text Analytics
D. Shaper
Answer
D. Shaper
Explanation
In the given scenario, you would use the Shaper skill to simplify mapping the unstructured data to projections in a knowledge store. The Shaper skill in Azure Cognitive Search is designed to transform and structure the data extracted from documents. It enables you to reshape and project the data into a structured format, such as JSON, that can be efficiently stored and queried in a knowledge store. The Shaper skill enables the transformation of raw data into meaningful and structured formats. It can map fields from different parts of the document into a cohesive data structure. This skill allows defining projections that determine how the final data will be organized and stored.
Some of the scenarios where you can use the Shaper skill are:
- Transforming extracted text from documents into structured records.
- Organizing data fields into hierarchical JSON objects.
- Preparing data for storage in a knowledge store or for further analysis.
Projections in the context of Azure Cognitive Search refer to the process of defining how the data should be structured and stored after it has been enriched by the skillset. Projections are essential for creating a usable and efficient data store that supports complex queries and analytics.
You would not use the Text Analytics skill in the given scenario. The Text Analytics skill is used to extract insights such as key phrases, language detection, and sentiment analysis from text. It provides valuable insights. However, it does not transform and structure data into a specified format.
You would not use the Entity Recognition skill in the given scenario. The Entity Recognition skill identifies and categorizes entities within the text, such as people, organizations, and locations. This skill is useful for identifying key entities.
You would not use the Sentiment Analysis skill in the given scenario. The Sentiment Analysis skill evaluates the sentiment expressed in the text, classifying it as positive, negative, or neutral.
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