The correct answer isB. According to responsible AI practices,pre-deployment testingto ensurethe model behaves as expected and aligns with organizational requirements is critical.
From the AIGP ILT Guide:
"Testing for unacceptable outcomes such as toxicity, discrimination, or hallucinations should be included in the AI governance life cycle, particularly during development and prior to deployment." Also emphasized in the AI Governance in Practice Report 2024:
"Organizations must verify legal and regulatory compliance, monitor performance, and mitigate risksprior to deployment." Testing the model tomeet safety and appropriateness standardsis more proactive and preventive than relying solely on user reporting or requesting documentation.

Minimizing human involvement during testing is not a best practice; human oversight is critical to validate model behavior and ensure ethical and accurate performance.

The correct answer is D. Emotion recognition in the workplace is flagged as unacceptable or highly restricted under the EU AI Act due to its intrusive nature and potential for misuse.
From the AIGP ILT Guide - EU AI Act Training Module:
"AI systems that monitor individuals' emotions in the workplace or educational settings are listed among prohibited or strictly limited practices under Article 5." AI Governance in Practice Report 2024 supports this interpretation:
"Emotion recognition systems, especially in sensitive contexts such as employment or education, raise significant concerns under EU fundamental rights law and are likely to be restricted." Other uses listed-such as emergency response or emotion detection in healthcare-may fall under lawful and beneficial uses, especially when justified by public interest.

The most critical reason for documenting AI-related risks is toreduce exposure to legal, regulatory, and reputational liabilities. Clear documentation demonstrates thatrisks were identified, assessed, and addressed, which is essential for accountability and defensibility in the face of audits, litigation, or enforcement actions.
From theAI Governance in Practice Report2025:
"An effective AI governance model is about collective responsibility... which should encompass oversight mechanisms such as privacy, accountability, compliance." (p. 13)
"Accountability... is based on the idea that there should be a person or entity that is ultimately responsible for any harm resulting from the use of the data, algorithm and AI system's underlying processes." (p. 28) While transparency, alignment with standards, and knowledge sharing are all secondary benefits,risk documentation's primary role is liability mitigation.

Small language models are designed for efficiency and agility, making them suitable for deployment in lower-resource environments.