What issue might arise from using small datasets with the Vanilla fine-tuning method in the...

Comprehensive and Detailed In-Depth Explanation:

Vanilla fine-tuning updates all model parameters, and with small datasets, it can overfit—memorizing the data rather than generalizing—leading to poor performance on unseen data. Option A is correct. Option B (underfitting) is unlikely with full updates—overfitting is the risk. Option C (data leakage) depends on data handling, not size. Option D (model drift) relates to deployment shifts, not training. Small datasets exacerbate overfitting in Vanilla fine-tuning.

OCI 2025 Generative AI documentation likely warns of overfitting under Vanilla fine-tuning limitations.