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| Section | Weight | Objectives |
|---|---|---|
| Governance of AI Development | 21–25% | - Requirements gathering, risk assessment and mitigation - Testing, validation, documentation and transparency - Model management, version control and quality assurance - Governance through design and lifecycle management |
| Governance of AI Deployment and Use | 21–25% | - Implementation, monitoring and ongoing oversight - Incident response, accountability and audit - Lifecycle maintenance, decommissioning and updates - Selection, procurement and third-party management |
| Foundational Concepts of AI Governance | 24–28% | - Principles of responsible, ethical and trustworthy AI - Impacts, risks and benefits of AI systems - Governance frameworks and core concepts - Fundamental AI definitions, types and characteristics |
| Laws, Regulations, Standards and Frameworks | 24–28% | - Global and regional regulations (EU AI Act, US, etc.) - Alignment with data protection and privacy laws - Sector-specific requirements and compliance obligations - International standards (ISO 42001, NIST AI RMF, etc.) |
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NEW QUESTION # 27
CASE STUDY
Please use the following to answer the next question:
A leading insurance provider that offers a range of coverage options to individuals has decided to utilize AI to streamline and improve its customer acquisition and underwriting process, including the accuracy and efficiency of pricing policies. The company has engaged a cloud provider to utilize and fine-tune its pre-trained, general purpose large language model ("LLM").
The company intends to use its historical customer data - including applications, policies and claims - and proprietary pricing and risk strategies to provide an initial qualification assessment of potential customers, which would then be routed to a human underwriter for final review.
The company and the cloud provider have completed training and testing the LLM, performed a readiness assessment, and made the decision to deploy the LLM into production. They have designated an internal compliance team to monitor the model during the first month, specifically to evaluate the accuracy, fairness and reliability of its output.
After the first month in production, the company realizes that the LLM declines a higher percentage of women's applications.
What is the best strategy to mitigate the bias uncovered in the loan applications?
Answer: D
Explanation:
Retraining the model with data that reflects demographic parity helps reduce bias by balancing representation and mitigating unfair disparities in model outcomes.
NEW QUESTION # 28
CASE STUDY
Please use the following answer the next question:
ABC Corp, is a leading insurance provider offering a range of coverage options to individuals. ABC has decided to utilize artificial intelligence to streamline and improve its customer acquisition and underwriting process, including the accuracy and efficiency of pricing policies.
ABC has engaged a cloud provider to utilize and fine-tune its pre-trained, general purpose large language model ("LLM"). In particular, ABC intends to use its historical customer data-including applications, policies, and claims-and proprietary pricing and risk strategies to provide an initial qualification assessment of potential customers, which would then be routed tA. human underwriter for final review.
ABC and the cloud provider have completed training and testing the LLM, performed a readiness assessment, and made the decision to deploy the LLM into production. ABC has designated an internal compliance team to monitor the model during the first month, specifically to evaluate the accuracy, fairness, and reliability of its output. After the first month in production, ABC realizes that the LLM declines a higher percentage of women's loan applications due primarily to women historically receiving lower salaries than men.
Each of the following steps would support fairness testing by the compliance team during the first month in production EXCEPT?
Answer: A
Explanation:
Providing the loan applicants with information about the model capabilities and limitations would not directly support fairness testing by the compliance team. Fairness testing focuses on evaluating the model's decisions for biases and ensuring equitable treatment across different demographic groups, rather than informing applicants about the model.
Reference: The AIGP Body of Knowledge outlines that fairness testing involves technical assessments such as validating decision-making consistency across demographics and using tools to understand decision factors.
While transparency to applicants is important for ethical AI use, it does not contribute directly to the technical process of fairness testing.
NEW QUESTION # 29
A leading software development company wants to integrate AI-powered chatbots into their customer service platform. After researching various AI models in the market which have been developed by third-party developers, they're considering two options:
Option A - an open-source language model trained on a vast corpus of text data and capable of being trained to respond to natural language inputs.
Option B - a proprietary, generative AI model pre-trained on large data sets, which uses transformer-based architectures to generate human-like responses based on multimodal user input.
Option A would be the best choice for the company because?
Answer: D
Explanation:
Open-source modelsoffer morecustomization flexibility, allowing organizations to fine-tune or adapt the model tofit their own workflows, branding, or compliance needs- making it preferable when deep control is needed.
From theAI Governance in Practice Report 2024:
"Open-source AI allows organizations to review, adapt, and control model behavior in line with organizational needs and policies." (p. 39)
NEW QUESTION # 30
You are a privacy program manager at a large e-commerce company that uses an Al tool to deliver personalized product recommendations based on visitors' personal information that has been collected from the company website, the chatbot and public data the company has scraped from social media.
A user submits a data access request under an applicable U.S. state privacy law, specifically seeking a copy of their personal data, including information used to create their profile for productrecommendations.
What is the most challenging aspect of managing this request?
Answer: C
Explanation:
The most challenging aspect of managing a data access request in this scenario is dealing with unstructured data that cannot be easily disentangled from other data, including information about other individuals. Unstructured data, such as free-text inputs or social media posts, often lacks a clear structure and may be intermingled with data from multiple individuals, making it difficult to isolate the specific data related to the requester. This complexity poses significant challenges in complying with data access requests under privacy laws. Reference: AIGP Body of Knowledge on Data Subject Rights and Data Management.
NEW QUESTION # 31
What is the most important reason to document the results of AI testing?
Answer: D
Explanation:
Testing results need to bedocumented thoroughlyto ensuretraceability, accountability, and compliance.
This is central to enabling audits, investigations, or regulatory inquiries into the system's development and performance.
From theAI Governance in Practice Report 2024:
"Documentation and recordkeeping are essential components... to demonstrate AI system compliance, trace system behavior, and support audits and conformity assessments." (p. 34-35)
"Maintaining audit trails across development and deployment enables transparency and accountability." (p.
12)
* AandBare benefits, but not theprimary governance justification.
* D- Limiting future testing is not a recommended goal.
NEW QUESTION # 32
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