You have a Microsoft Foundry project that contains a customer support application.

The first statement is No because automatic Responsible AI checks applied to a fine-tuning dataset are separate from the runtime content-filter threshold. Changing the runtime filter from high to low does not correct unsafe or disallowed training examples that cause the fine-tuning safety evaluation to fail.

The second statement is Yes . A medium severity threshold evaluates or flags a broader set of content than a high-only threshold. Consequently, Run1 can legitimately report a larger defect rate—12%—while Run2 reports only 4%.

The third statement is also Yes . Protected-material evaluation is a separate evaluation dimension from the standard harmful-content severity categories. Because both runs use the same dataset and protected-material evaluator, an identical 6% result is consistent even though the harmful-content threshold changes.

The key distinction is between runtime harmful-content thresholds , protected-material detection , and automatic RAI checks for training/fine-tuning data . They are related responsible-AI controls but operate independently.

The AI-103 Study Guide covers safety filters, risk detection, safety evaluations, and responsible-AI instrumentation as separate operational controls. Microsoft Learn

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