AWS Certified AI Practitioner AIF-C01 Practice Question

High bias means the model is too simple to capture underlying patterns, so both training and validation accuracy remain low (underfitting). Increasing model complexity, such as using a deeper neural network or adding more relevant features, gives the model greater capacity to fit the data, often lifting both training and validation performance. The other actions either further restrict capacity (higher dropout), shorten learning (early stopping), or add more data without addressing the model's limited capacity; none of these directly resolve high bias.