Master time-series forecasting with Azure Machine Learning Studio for the AI-900 exam. Learn why automated ML is the complete supervised solution for predictive analytics and how it outperforms clustering or anomaly detection.
Table of Contents
Question
Which of the following is a complete, supervised machine learning solution that you can train by using automated machine learning (automated ML) through Azure Machine Learning Studio?
A. Anomaly detection
B. Clustering
C. Recommendation systems
D. Time-series forecasting
Answer
D. Time-series forecasting
Explanation
Automated ML can handle time-series data, enabling you to build models that predict future values in a sequence. It supports training models to predict categories or classes, making it suitable for solving classification tasks. Automating regression model training is another strength of Azure ML, allowing you to predict continuous numerical values based on input features. Azure Machine Learning Studio provides a cloud-based environment for developing, deploying, and managing machine learning workflows. Using automated ML inside Azure Machine Learning Studio involves leveraging the automated ML capabilities offered within the Studio platform.
Clustering is not inherently a complete, supervised machine learning solution. It is an unsupervised machine learning task where the algorithm groups similar data points together based on certain features. Automated ML in Azure Machine Learning Studio is designed for supervised learning scenarios, where there are datasets with features and corresponding labels.
Recommendation systems involve collaborative filtering and other techniques and are not specifically addressed by automated ML in this context. They are designed to provide personalized recommendations to users based on their preferences or behavior. While recommendation systems often involve machine learning techniques, automated ML in Azure Machine Learning Studio is more focused on supervised learning tasks.
Anomaly detection is the identification of unusual patterns or outliers in a dataset. Like clustering, anomaly detection is often categorized as an unsupervised learning task. While it can be approached using machine learning, it may not fit the automated ML framework within Azure Machine Learning Studio which is designed for supervised learning scenarios.
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.