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IAPP Certified Artificial Intelligence Governance Professional AIGP answers real questions can help candidates have correct directions and prevent useless effort. If you still lack of confidence in preparing your exam, choosing a good IAPP AIGP Answers Real Questions will be a wise decision for you, it is also an economical method which is saving time, money and energy.

IAPP AIGP Exam Syllabus Topics:

Topic
Details

Topic 1

Understanding How Laws, Standards, and Frameworks Apply to AI: This section of the exam measures skills of compliance officers and covers the application of existing and emerging legal requirements to AI systems. It explores how data privacy laws, intellectual property, non-discrimination, consumer protection, and product liability laws impact AI. The domain also examines the main elements of the EU AI Act, such as risk classification and requirements for different AI risk levels, as well as enforcement mechanisms. Furthermore, it addresses the key industry standards and frameworks, including OECD principles, NIST AI Risk Management Framework, and ISO AI standards, guiding organizations in trustworthy and compliant AI implementation.

Topic 2

Understanding How to Govern AI Deployment and Use: This section of the exam measures skills of technology deployment leads and covers the responsibilities associated with selecting, deploying, and using AI models in a responsible manner. It includes evaluating key factors and risks before deployment, understanding different model types and deployment options, and ensuring ongoing monitoring and maintenance. The domain applies to both proprietary and third-party AI models, emphasizing the importance of transparency, ethical considerations, and continuous oversight throughout the model’s operational life.

Topic 3

Understanding How to Govern AI Development: This section of the exam measures the skills of AI project managers and covers the governance responsibilities involved in designing, building, training, testing, and maintaining AI models. It emphasizes defining the business context, performing impact assessments, applying relevant laws and best practices, and managing risks during model development. The domain also includes establishing data governance for training and testing, ensuring data quality and provenance, and documenting processes for compliance. Additionally, it focuses on preparing models for release, continuous monitoring, maintenance, incident management, and transparent disclosures to stakeholders.

Topic 4

Understanding the Foundations of AI Governance: This section of the exam measures skills of AI governance professionals and covers the core concepts of AI governance, including what AI is, why governance is needed, and the risks and unique characteristics associated with AI. It also addresses the establishment and communication of organizational expectations for AI governance, such as defining roles, fostering cross-functional collaboration, and delivering training on AI strategies. Additionally, it focuses on developing policies and procedures that ensure oversight and accountability throughout the AI lifecycle, including managing third-party risks and updating privacy and security practices.

IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q92-Q97):

NEW QUESTION # 92
If it is possible to provide a rationale for a specific output of an Al system, that system can best be described as?

A. Reliable.
B. Transparent.
C. Explainable.
D. Accountable.

Answer: C

Explanation:
If it is possible to provide a rationale for a specific output of an AI system, that system can best be described as explainable. Explainability in AI refers to the ability to interpret and understand the decision-making process of the AI system. This involves being able to articulate the factors and logic that led to a particular output or decision. Explainability is critical for building trust, enabling users to understand and validate the AI system's actions, and ensuring compliance with ethical and regulatory standards. It also facilitates debugging and improving the system by providing insights into its behavior.

NEW QUESTION # 93
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?

A. Using tools to help understand factors that may account for differences in decision-making.
B. Validating a similar level of decision-making across different demographic groups.
C. Identifying if additional training data should be collected for specific demographic groups.
D. Providing the loan applicants with information about the model capabilities and limitations.

Answer: D

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 # 94
All of the following issues are unique for proprietary AI model deployments EXCEPT?

A. The necessity of performing conformity assessments.
B. The potential for bias.
C. The cost of AI chips.
D. The acquisition of training data.

Answer: B

Explanation:
Biasis a common risk acrossboth proprietary and open-source models, andnot uniqueto proprietary deployments. All AI systems - regardless of origin - require evaluation for fairness, accuracy, and representativeness.
From theAI Governance in Practice Report 2024:
"Bias, discrimination and fairness challenges are present in both open and closed models, regardless of how the model is sourced." (p. 41)

NEW QUESTION # 95
According to the GDPR, what is an effective control to prevent a determination based solely on automated decision-making?

A. Establish a human-in-the-loop procedure.
B. Provide a right to review automated decision.
C. Provide a just-in-time notice about the automated decision-making logic.
D. Define suitable measures to safeguard personal data.

Answer: A

Explanation:
The GDPR requires that individuals have the right to not be subject to decisions based solely on automated processing, including profiling, unless specific exceptions apply. One effective control is to establish a human-in-the-loop procedure (D), ensuring human oversight and the ability to contest decisions. This goes beyond just-in-time notices (A), data safeguarding (B), or review rights (C), providing a more robust mechanism to protect individuals' rights.

NEW QUESTION # 96
During the development of semi-autonomous vehicles, various failures occurred as a result of the sensors misinterpreting environmental surroundings, such as sunlight.
These failures are an example of?

A. Brittleness.
B. Forgetting.
C. Uncertainty.
D. Hallucination.

Answer: A

Explanation:
The failures in semi-autonomous vehicles due to sensors misinterpreting environmental surroundings, such as sunlight, are examples of brittleness. Brittleness in AI systems refers to their inability to handle variations in input data or unexpected conditions, leading to failures when the system encounters situations that were not adequately covered during training. These systems perform well under specific conditions but fail when those conditions change. Reference: AIGP Body of Knowledge on AI System Robustness and Failures.

NEW QUESTION # 97
......

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