Which Amazon SageMaker feature enhances model accuracy through human feedback?

Amazon SageMaker Ground Truth is the feature that enhances model accuracy through human feedback. It is a data labeling service that enables you to build highly accurate training datasets for machine learning quickly. Ground Truth incorporates human validation in the labeling process, allowing for human feedback to be added to models and improving their accuracy.

When using Ground Truth, human labelers review and correct model predictions on the data, thus providing critical feedback to the model. This iterative process allows for the continuous refinement of the model, increasing its performance on real-world data.

The other options, while valuable features in the SageMaker toolkit, serve different purposes. Amazon SageMaker Autopilot automates the model building process, including preprocessing and model selection, but does not directly involve human feedback for accuracy improvement. Amazon SageMaker Model Monitor focuses on tracking the performance of deployed models and detecting data drift over time rather than soliciting human feedback. Amazon SageMaker Data Wrangler is designed for preparing data for machine learning but does not include a mechanism for human-in-the-loop feedback to enhance models.

Therefore, SageMaker Ground Truth is distinct in its capability to integrate human insights into the model training process, making it the correct answer.