- (Topic 2)
All of the following are elements of establishing a global Al governance infrastructure EXCEPT?

1. A. Providing training to foster a culture that promotes ethical behavior.
2. B. Creating policies and procedures to manage third-partyrisk.
3. C. Understanding differences in norms across countries.
4. D. Publicly disclosing ethical principles.

Correct Answer: D
Establishing a global AI governance infrastructure involves several key elements, including providing training to foster a culture that promotes ethical behavior, creating policies and procedures to manage third-party risk, and understanding differences in norms across countries. While publicly disclosing ethical principles can enhance transparency and trust, it is not a core element necessary for the establishment of a governance infrastructure. The focus is more on internal processes and structures rather than public disclosure. Reference: AIGP Body of Knowledge on AI Governance and Infrastructure.

- (Topic 2)
After completing model testing and validation, which of the following is the most important step that an organization takes prior to deploying the model into production?

1. A. Perform a readiness assessment.
2. B. Define a model-validation methodology.
3. C. Document maintenance teams and processes.
4. D. Identify known edge cases to monitor post-deployment.

Correct Answer: A
After completing model testing and validation, the most important step prior to deploying the model into production is to perform a readiness assessment. This assessment ensures that the model is fully prepared for deployment, addressing any potential issues related to infrastructure, performance, security, and compliance. It verifies that the model meets all necessary criteria for a successful launch. Other steps, such as defining a model-validation methodology, documenting maintenance teams and processes, and identifying known edge cases, are also important but come secondary to confirming overall readiness. Reference: AIGP Body of Knowledge on Deployment Readiness.

- (Topic 1)
All of the following are common optimization techniques in deep learning to determine weights that represent the strength of the connection between artificial neurons EXCEPT?

1. A. Gradient descent, which initially sets weights arbitrary values, and then at each step changes them.
2. B. Momentum, which improves the convergence speed and stability of neural network training.
3. C. Autoregression, which analyzes and makes predictions about time-series data.
4. D. Backpropagation, which starts from the last layer working backwards.

Correct Answer: C
Autoregression is not a common optimization technique in deep learning to
determine weights for artificial neurons. Common techniques include gradient descent, momentum, and backpropagation. Autoregression is more commonly associated with time- series analysis and forecasting rather than neural network optimization. Reference: AIGP BODY OF KNOWLEDGE, which discusses common optimization techniques used in deep learning.

- (Topic 1)
What is the primary purpose of an Al impact assessment?

1. A. To define and evaluate the legal risks associated with developing an Al system.
2. B. Anticipate and manage the potential risks and harms of an Al system.
3. C. To define and document the roles and responsibilities of Al stakeholders.
4. D. To identify and measure the benefits of an Al system.

Correct Answer: B
The primary purpose of an AI impact assessment is to anticipate and manage the potential risks and harms of an AI system. This includes identifying the possible negative outcomes and implementing measures to mitigate these risks. This process helps ensure that AI systems are developed and deployed in a manner that is ethically and socially responsible, addressing concerns such as bias, fairness, transparency, and accountability. The assessment often involves a thorough evaluation of the AI system's design, data inputs, outputs, and the potential impact on various stakeholders. This approach is crucial for maintaining public trust and adherence to regulatory requirements.

- (Topic 1)
Under the NIST Al Risk Management Framework, all of the following are defined as characteristics of trustworthy Al EXCEPT?

1. A. Tested and Effective.
2. B. Secure and Resilient.
3. C. Explainable and Interpretable.
4. D. Accountable and Transparent.

Correct Answer: A
The NIST AI Risk Management Framework outlines several characteristics of trustworthy AI, including being secure and resilient, explainable and interpretable, and accountable and transparent. While being tested and effective is important, it is not explicitly listed as a characteristic of trustworthy AI in the NIST framework. The focus is more on the system's ability to function safely, securely, and transparently in a way that stakeholders can understand and trust. Reference: AIGP Body of Knowledge, NIST AI RMF section.