Table of Contents
Which machine learning model groups data based on measurements?
Prepare for the AI-900 exam by learning which AI workload type to use for grouping similar data points. Understand why machine learning clustering is the correct solution for grouping plants based on measurements and how it differs from other AI workloads like NLP and anomaly detection.
Question
Which AI workload type matches the following scenario? “Group plants based on multiple measurements.”
A. Natural language processing
B. Machine learning (clustering)
C. Azure AI Vision
D. Anomaly detection
Answer
B. Machine learning (clustering)
Explanation
The correct AI workload type for this scenario is B. Machine learning (clustering). This task involves organizing data points into groups based on their inherent similarities, which is the primary function of clustering algorithms.
Understanding Machine Learning (Clustering)
Clustering is a form of unsupervised machine learning, meaning it works with data that has not been pre-labeled with known categories. The goal of a clustering algorithm is to analyze the features of data points—in this case, the various measurements of the plants—and partition them into distinct groups, or “clusters.” Plants with similar measurements (e.g., similar petal length, stem width, and leaf color) will be placed in the same cluster, while plants with different measurements will be placed in separate clusters. This is a fundamental technique for exploratory data analysis and pattern recognition.
Why Other Options Are Incorrect
- Natural language processing (NLP): This workload is used for analyzing, understanding, and generating human language (text or speech). It is not applicable to grouping objects based on numerical measurements.
- Azure AI Vision: This computer vision service could be used to acquire the measurements by analyzing images of the plants. However, the task of grouping the plants based on those numerical measurements is a separate machine learning task, not a computer vision one.
- Anomaly detection: This workload is used to identify data points that are unusual or deviate significantly from the rest of the data. Its purpose is to find outliers, not to organize all data points into logical groups.
Microsoft Azure AI Fundamentals AI-900 certification exam practice question and answer (Q&A) dump with detail explanation and reference available free, helpful to pass the Microsoft Azure AI Fundamentals AI-900 exam and earn Microsoft Azure AI Fundamentals AI-900 certification.