Your question is Bias-Variance and Transformers. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
Difference between variance and bias, and explain how transformers work.
Explain the bias variance tradeoff, including how underfitting, overfitting, regularization, and data quantity affect model performance. Then describe transformer architecture from tokenization through self-attention, positional information, feed-forward layers, residual connections, normalization, and output generation. Include a concise practical example or code showing how you would evaluate model generalization and construct a transformer component.