FinSight LLC runs a pre-training bias assessment with SageMaker Clarify on a loan-approval dataset stored in an S3 bucket (s3://finsight-prod/data). The Clarify pre-training report shows Class Imbalance (CI) = 0.12 for the target label (acceptable below 0.2) and Difference in Positive Proportions (DPP) for gender (male vs female) = 0.18 (exceeds the policy threshold of 0.10). The model team plans to retrain on an ml.c5.4xlarge training instance. What is the most appropriate immediate action to address these Clarify results before retraining?

The correct action is to create a pre-processing pipeline for group-aware resampling or sample re-weighting. The Clarify report indicates a pre-training bias (DPP = 0.18) that exceeds the policy threshold (0.10). This bias exists in the dataset before training, so it must be addressed at the data preparation stage. Techniques like resampling or re-weighting can balance the representation of different gender groups, mitigating the bias. A SageMaker Processing job is suitable for this large-scale data transformation on S3. Ignoring the CI is correct as it's within policy, but DPP is a pre-training issue, not solely post-training. Removing the gender column is ineffective because bias can still manifest through correlated features. Using Model Monitor after deployment is too late; the goal is to address the pre-training bias before the model is trained and deployed.