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Q Why does enabling model invocation logging matter beyond debugging?

A It creates a source of real production queries for building representative evaluation datasets.

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Q When should Mechanical Turk be disqualified as a labeling workforce?

A When content is confidential or sensitive — use private or vendor workforces instead.

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Q What is SageMaker Ground Truth?

A AWS service for labeling data at volume, using private, vendor, or public workforces.

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Q Why does Bedrock Knowledge Base evaluation evaluate retrieval and generation as separate stages?

A So failures can be localized to a specific layer rather than blamed on the system as a whole.

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Q If exact brute-force search retrieves a passage but ANN search misses it, where is the problem?

A Index parameters — not embeddings or chunking.

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Q What does Bedrock's 'automatic' evaluation mode use to score model outputs?

A Built-in metrics computed programmatically over a dataset — no model judge or human needed.

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Q What evaluation approach sits between human evaluation (accurate but slow) and automated metrics (fast but crude)?

A LLM-as-a-judge: a model scores responses against explicit criteria

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Q When should you use human evaluation instead of LLM-as-judge or automated metrics?

A For final quality judgments, subtle domain distinctions, and calibrating automated metrics against ground truth.

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Q Which AWS service supports foundation model evaluation across accuracy, robustness, toxicity, and bias dimensions?

A SageMaker Clarify

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