A platform aimed at teenagers hosts a Foundry-powered writing assistant that helps users draft short fiction. The moderation team wants to strictly block sexual content and self-harm content at the lowest severity threshold, while still allowing mild fictional violence such as a sword fight in an adventure story to pass through. Which content safety configuration approach fits this requirement?

The correct answer is A. Azure AI Foundry's content safety filtering lets a team set independent severity thresholds per category, such as hate, sexual, self-harm, and violence, so the moderation team can strictly block sexual and self-harm content at low severity while still permitting the milder violence typical of adventure fiction, which a single blanket setting cannot do. Option B is incorrect because a single global toggle applies the same threshold to every category at once, meaning any setting strict enough to catch sexual or self-harm content at low severity would also block the mild fictional violence the team wants to allow. Option C is incorrect because removing the filter and relying only on a system message leaves the assistant with no enforced technical safeguard; a system message is an instruction the model may not always follow, especially under adversarial or unusual prompts, so it cannot substitute for a moderation layer. Option D is incorrect because temperature controls the randomness of word choice, not the detection or blocking of harmful content categories; a model with temperature set to zero can still generate sexual or self-harm content if nothing is filtering for it. Per-category thresholds are the tool designed for exactly this mixed tolerance.