An established failure mode for machine learning models occurs when the same features are equally likely to belong to class 0 and class 1. In such cases, existing ML models cannot correctly classify the sample. However, a solvable case emerges when the probabilities of class 0 and 1 vary with the context distribution. To the best of our knowledge, standard neural network architectures like MLPs or CNNs are not equipped to handle this.