Completeness refers to the degree to which all necessary data is present and accounted for. In the context of machine learning, if a model is trained without including all relevant dimensions (such as demographic information in this case), it might lead to poor performance because it lacks important data needed to make accurate predictions.
Option D: Completeness is correct because the lack of demographic information represents incomplete data.
The absence of this data dimension could prevent the model from learning patterns that are influenced by demographic factors, thereby impacting its overall effectiveness.
References:
* Google Cloud Documentation: Data Quality in Machine Learning
* Google Cloud AI Platform: Best Practices for Training Data Preparation