The correct choice is Manages human labeling workflows and annotation UIs to capture rater feedback for RLHF.

Amazon SageMaker Ground Truth provides managed labeling jobs and workforce orchestration along with customizable annotation user interfaces and built in quality controls that let teams collect high quality human preference labels at scale for RLHF training. Ground Truth integrates with SageMaker data storage and pipelines so collected ratings and reviewer metadata can be exported for reward model training and iterative policy updates.

Amazon Translate is designed for language translation and does not provide workforce management or structured labeling UIs so it cannot coordinate reviewers or capture RLHF preference labels.

Generates synthetic conversation ratings so human reviewers are not needed is incorrect because RLHF depends on real human judgments to align model behavior with human preferences and synthetic ratings cannot replace human in the loop annotation for alignment tasks.

Amazon CloudWatch focuses on monitoring logs and metrics for observability and does not manage human labeling workflows or annotation tasks so it does not address RLHF data collection needs.

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