Which best practice for data preparation aims to prevent issues arising from biased data samples?

The best practice for data preparation that aims to prevent issues arising from biased data samples is to avoid training server skew. Server skew refers to an imbalance in the representation of data across different servers or instances, which can lead to biased training outcomes. Ensuring that the training data is equally representative across all servers mitigates the risk of the model learning from skewed data distributions, which would ultimately result in biased predictions and unfair outcomes when deployed in real-world scenarios.

By avoiding training server skew, practitioners can ensure that the model is trained on a more comprehensive and representative sample of the data, thereby enhancing its ability to generalize well across various inputs. This practice fosters fairness and accuracy, making it a fundamental aspect of ethical machine learning and effective model performance.

The other practices listed, while valuable in overall data preparation, do not directly address the prevention of bias stemming from skewed data representation. Ensuring data completeness focuses on filling gaps in the data, monitoring model drift looks at performance over time, and minimizing data redundancy pertains to data storage efficiency rather than addressing sampling bias directly.