Drag and Drop Question Match the machine learning tasks to the appropriate scenarios. To answer, drag the appropriate task from the column on the left to its scenario on the right. Each task may be us

The correct answer is Model evaluation; Feature engineering; Feature selection. The three correct matches align specific ML workflow activities to their definitions: Model evaluation involves assessing a trained model's performance using metrics to determine how well it generalizes; Feature engineering involves transforming or creating new input variables fr

Submitted by jian89· Mar 30, 2026Understand and apply core machine learning concepts and pipeline stages, including data preparation techniques (feature engineering and selection) and model assessment (evaluation), as tested in AWS Certified Machine Learning or similar ML certification exams.

Question

Drag and Drop Question Match the machine learning tasks to the appropriate scenarios. To answer, drag the appropriate task from the column on the left to its scenario on the right. Each task may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point. Answer:

Exhibit

Answer Area

Drag items

Feature engineeringFeature selectionModel deploymentModel evaluationModel training

Correct arrangement

Model evaluation
Feature engineering
Feature selection

Explanation

The three correct matches align specific ML workflow activities to their definitions: Model evaluation involves assessing a trained model's performance using metrics to determine how well it generalizes; Feature engineering involves transforming or creating new input variables from raw data to improve model performance; and Feature selection involves identifying and keeping only the most relevant features from a dataset to reduce dimensionality and noise. These three tasks represent distinct, sequential phases in the machine learning pipeline that address data preparation and model assessment.

Topics

#Machine Learning Workflow#Feature Engineering#Model Evaluation#Feature Selection

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