The correct answer is A because Amazon SageMaker Canvas is designed specifically for users with little or no machine learning or programming experience. It provides a visual interface to build ML models by simply uploading data, performing analysis, and generating predictions using a no-code environment.
From the AWS documentation:
"Amazon SageMaker Canvas enables business analysts and other users to generate accurate ML predictions using a visual, point-and-click interface without writing code or having prior ML experience."
This feature allows the user to:
Import datasets (e.g., HR data)
Automatically explore the data
Select the prediction column (e.g., attrition)
Train the model
Generate and export predictions
Explanation of other options:
B. SageMaker Clarify is used to detect bias and explain ML predictions but not to build models or make predictions without code.
C. SageMaker Model Monitor monitors model quality in production but doesn’t build or train models.
D. SageMaker Data Wrangler is used for data preprocessing and transformation but still requires some technical configuration.
Referenced AWS AI/ML Documents and Study Guides:
Amazon SageMaker Canvas Developer Guide
AWS Certified Machine Learning Specialty Study Guide – AutoML and No-Code Tools Section
AWS Machine Learning Blog: “Predict Employee Attrition with SageMaker Canvas”
