The question requires selecting two actions to prevent unfair bias in a model for targeted advertising. Option D (Collect a stratified sample of production traffic) is optimal because stratified sampling ensures proportional representation of demographic groups in the training data, reducing bias from underrepresentation. Option E (Conduct fairness tests across sensitive categories) is essential for post-training evaluation to detect and address disparities in model performance across demographics. These two actions address bias prevention both during dataset creation (D) and after model training (E). Option A is unsuitable as including all demographic features may not guarantee fairness and could introduce noise. Option B risks reinforcing bias by focusing only on high-interaction groups. Option C (random sampling) is less effective than stratified sampling for ensuring demographic balance, as it may not capture minority groups adequately.