AI-900: How Does AutoML Solve Real-World Problems Like Customer Churn Prediction?

Learn why training models to predict customer churn with AutoML is a key use case for the Microsoft AI-900 exam. Discover how AutoML simplifies machine learning workflows, reduces manual effort, and prepares you for certification success.

Question

Which of the following is a valid use case for AutoML?

A. Building a complex fraud-detection model requiring manual feature engineering
B. Building a personalized recommendation system for a large e-commerce platform
C. Training a model to predict future customer churn based on historical data
D. Identifying the optimal hyperparameters for a pre-selected machine learning algorithm

Answer

C. Training a model to predict future customer churn based on historical data

Explanation

Training a model to predict future customer churn based on historical data is a valid use case for AutoML. AutoML automates model selection and hyperparameter tuning, making it efficient for such cases. It primarily focuses on automating tasks within the realm of supervised learning, where models learn from labeled data to make predictions for new, unseen data. Key tasks supported by AutoML are:

Classification:

• Classifying data points into predefined categories (e.g., spam or not spam, cat or dog).
• Handling various classification problems, including multi-class and multi-label scenarios.

Regression:

• Predicting continuous numerical values based on input features (e.g., predicting house prices based on size and location).
• Supporting different regression tasks, including linear and non-linear regression.

Time-series Forecasting:

• Predicting future values in a sequence based on historical data (e.g., forecasting sales or electricity demand).
• Offering advanced techniques specifically designed for time-series data analysis.

Computer Vision:

• Building models for tasks such as image classification (e.g., classifying objects in images) and object detection (e.g., locating objects in images).
• Supporting various computer vision tasks with pre-built features for specific scenarios.

Natural Language Processing (NLP):

• Building models for tasks such as text classification (e.g., sentiment analysis) and named entity recognition (e.g., identifying names and locations in text).
• Offering NLP capabilities with pre-trained language models and customizable feature engineering.

Building a complex fraud-detection model requiring manual feature engineering is not a valid use case for AutoML. AutoML does not handle complex models requiring extensive manual feature engineering. This requires domain expertise and specific feature choices beyond AutoML’s scope.

Identifying the optimal hyperparameters for a pre-selected machine learning algorithm is not a valid use case for AutoML. AutoML is not suited for manually selecting and optimizing a single pre-defined algorithm. This requires specific knowledge and control over the algorithm’s behavior.

Building a personalized recommendation system for a large e-commerce platform is not a valid use case for AutoML. Recommendation systems often involve collaborative filtering (unsupervised) and content-based filtering (supervised) combined. While AutoML can handle the supervised part, it is not ideal for the entire system.

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.