612-51 Sample Questions

612-51 Sample Questions & Answers

Governance frameworks, regulatory compliance, and threat and risk management share the heaviest weighting, with AI foundations and ethics, strategic planning, third-party supply-chain risk, security architecture, privacy, and incident auditing.

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Showing 6 of 12 free samples.

  1. Question 1Intermediate

    AI Foundations and Technology Ecosystem · AI Project Life Cycle, MLOps, and DataOps

    Your organization is retiring a legacy credit scoring model that was in production for 5 years. According to AI lifecycle governance best practices (e.g., NIST AI RMF GOVERN 1.7 decommissioning, SR 11-7 model inventory) and record-retention obligations (e.g., ECOA/Regulation B), what is the MINIMUM required action regarding the model artifacts?

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    Correct answer: B

    Governance does not end at deployment. When a model is retired, the organization should archive the model artifacts (weights, code, version), the training data snapshot or its lineage, and the decision logs for the defined retention/liability period, so it can answer later regulatory inquiries, litigation or consumer disputes about decisions the model made (NIST AI RMF GOVERN 1.7 covers decommissioning; SR 11-7 expects the model inventory to include retired models). Retention must follow a documented schedule: where personal data is kept, GDPR's storage-limitation principle requires deletion once the retention purpose ends. Deleting everything immediately destroys the audit trail.

  2. Question 2Advanced

    AI Concerns, Ethical Principles, and Responsible AI · Key Ethical, Societal, Privacy, and Security Concerns in AI

    You are auditing an AI recruiting tool used to screen resumes. The audit reveals that the model rejects female candidates at a rate significantly higher than male candidates, despite 'Gender' not being an input feature. Upon investigation, you find the model correlates 'Softball Team Captain' and 'Women's College' with negative outcomes. What type of bias is this, and which fairness metric would best detect this disparity in outcomes?

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    Correct answer: C

    This is a classic example of Proxy Bias, where a seemingly neutral feature (Softball Team) acts as a proxy for a protected attribute (Gender). To detect this disparity in outcomes (i.e., the rate at which different groups are selected), Demographic Parity (also known as Disparate Impact) is the appropriate metric. It compares the selection rate of the privileged group vs. the unprivileged group. Equal Opportunity focuses on True Positive Rates, which is different from the overall selection rate disparity described.

  3. Question 3Intermediate

    AI Concerns, Ethical Principles, and Responsible AI · Responsible AI Usage Practices

    A media company is launching a generative AI tool for creating marketing images. To adhere to Responsible AI principles regarding transparency and to mitigate the risk of deepfakes, which technical standard should the company implement to cryptographically bind provenance data to the generated content?

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    Correct answer: A

    C2PA is the open technical standard that allows publishers to embed tamper-evident metadata (provenance) into media files. This allows consumers to verify the origin of the content (e.g., that it was AI-generated by a specific tool). This is the current industry standard for content authenticity. SSL/TLS secures transport, not content provenance. JWT is for authorization. OAuth is for access delegation.

  4. Question 4Intermediate

    AI Concerns, Ethical Principles, and Responsible AI · Responsible AI Usage Practices

    In a 'Human-in-the-loop' (HITL) governance model for a high-risk autonomous weapons system, what distinguishes HITL from 'Human-on-the-loop' (HOTL)?

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    Correct answer: B

    In Human-in-the-loop (HITL), the system cannot execute the final action without active human confirmation (the human is part of the execution cycle). In Human-on-the-loop (HOTL), the system executes automatically but a human monitors it and can override or stop it (supervisory control). This distinction is critical in high-stakes ethical governance.

  5. Question 5Beginner

    AI Concerns, Ethical Principles, and Responsible AI · Key Ethical, Societal, Privacy, and Security Concerns in AI

    A research team is developing a new AI model for drug discovery that identifies toxic compounds. However, the same model could be used by malicious actors to design chemical weapons. This ethical challenge is best described as:

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    Correct answer: B

    The Dual-Use Dilemma refers to technologies that can be used for both beneficial (civilian/medical) and harmful (military/criminal) purposes. In AI governance, identifying dual-use risks is critical before open-sourcing models or publishing research.

  6. Question 6Intermediate

    AI Strategy and Planning · Use Case Prioritization and Roadmap

    Case Study:

    GlobalTech Inc. is planning its AI strategy for the next fiscal year. The company has a limited budget and three potential projects:

    1. Project A: An internal chatbot for IT support (Low Risk, Medium ROI, High Feasibility).
    2. Project B: An automated loan approval system for unbanked populations (High Risk, High ROI, Medium Feasibility).
    3. Project C: A facial recognition system for office entry (High Risk, Low ROI, High Feasibility).

    The AI Governance Board is using a risk-adjusted prioritization matrix.

    Which project should be prioritized as the 'Quick Win' to build momentum while minimizing regulatory exposure, and why?

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    Correct answer: B

    Project A represents a classic 'Quick Win' in AI strategy. It is Low Risk (internal, non-sensitive data), High Feasibility (mature technology), and Medium ROI. It allows the organization to test its MLOps and governance processes without triggering heavy regulatory requirements (like Project B would) or privacy controversies (like Project C). Project B is a 'Strategic Bet' requiring more maturity. Project C is likely a 'Money Pit' or compliance liability due to biometric data regulations.

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