Microsoft Azure AI Fundamentals (AI-900) Practice Exam

Which aspect of AI focuses on understanding the relationship between cause and effect in data?

1. Data representation

2. Model evaluation

3. Causal inference

4. Predictive modeling

The correct answer is: Causal inference

Causal inference is the aspect of AI that specifically focuses on understanding the relationship between cause and effect in data. This involves identifying whether a change in one variable leads to a change in another variable and establishing a clear cause-and-effect relationship. Causal inference seeks to analyze data in a way that goes beyond mere correlation, enabling practitioners to infer what might happen if a specific intervention is applied. In various applications, such as healthcare or economics, determining causality can be crucial for making informed decisions based on data. For instance, if a healthcare study suggets that a new treatment leads to better recovery rates, causal inference techniques would help confirm whether the treatment itself is responsible for this improvement or if other factors are at play. Other aspects mentioned, such as data representation, model evaluation, and predictive modeling, serve different purposes in AI. Data representation is concerned with how data is organized and encoded for processing, model evaluation involves assessing the performance of a predictive model, and predictive modeling focuses on using historical data to forecast outcomes. While these factors play important roles in machine learning and AI, they do not specifically address the fine nuances of causality in the way that causal inference does.