Answer: It transforms the raw sensor readings and maintenance records into meaningful features that the model can use to learn patterns indicative of impending failures.

**Correct Option:** C. It transforms the raw sensor readings and maintenance records into meaningful features that the model can use to learn patterns indicative of impending failures: This is correct because feature engineering involves selecting, modifying, or creating variables (features) from raw data that can effectively represent the underlying problem to the machine learning model. In this scenario, transforming raw sensor data into features that indicate potential failure patterns is crucial for the model's ability to predict equipment failures accurately. **Incorrect Options:** A. It directly evaluates the model's performance by comparing predicted failures to actual maintenance records: This is incorrect because evaluating model performance is a separate task that involves assessing how well the model predicts outcomes on a validation set, not part of feature engineering. B. It defines the project's objectives, such as reducing equipment downtime and predicting failures accurately: This is incorrect because defining the project's objectives is part of the initial problem framing and goal-setting process, not feature engineering. D. It determines the size of the dataset by deciding how many sensor readings to include in the model: This is incorrect because feature engineering focuses on creating effective features from existing data, not on determining the overall size of the dataset.

Author: LeetQuiz Editorial Team