Knowledge check - Training | Microsoft Learn

1.

Your financial services company deploys an Azure OpenAI chatbot for customer inquiries about investment products. Regulatory requirements prohibit any content that could be construed as discriminatory based on protected characteristics. During testing, the chatbot occasionally generates responses with subtle bias when discussing demographic investment patterns. Which content safety configuration best addresses this compliance requirement?

Set the hate and fairness threshold to level 0 to block any detected bias, accepting higher false positive rates to ensure regulatory compliance

Set the hate and fairness threshold to level 4 to balance compliance with functionality, and create a custom block list containing specific discriminatory terms identified by the legal team

Rely on default Microsoft-managed filters without custom configuration, since they provide baseline protection sufficient for most applications

2.

Your healthcare education platform uses Azure OpenAI to help medical students learn about trauma care and emergency procedures. The content frequently includes clinical descriptions of injuries, surgical procedures, and patient conditions that might trigger violence detection filters. Students report that legitimate educational content is being blocked, interrupting their learning. How should you adjust content safety settings?

Disable content filtering entirely for the education deployment since medical students need access to clinical content without restrictions

Increase the violence threshold to level 5 or 6 to allow clinical descriptions while still blocking graphic nonmedical content, and add medical terminology to a custom allow list

Maintain default violence thresholds at level 2 but create a custom content filter that excludes violence detection while keeping other categories active

3.

Your retail company's customer service chatbot must never mention competitor brand names in responses, even when customers explicitly ask for comparisons. Your security team also wants to prevent internal project code names from appearing in any customer-facing communications. Which content safety mechanism most efficiently enforces both requirements?

Create two separate custom block lists: one containing competitor brand names maintained by marketing, and one containing internal code names maintained by security, then associate both block lists with the customer service deployment

Configure custom content filter thresholds at the strictest levels across all categories to prevent any potentially sensitive content from appearing in responses

Train a custom Azure OpenAI model with examples of forbidden terms so the model learns not to generate competitor references or internal code names

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