IAPP CERTIFIED ARTIFICIAL INTELLIGENCE GOVERNANCE PROFESSIONAL PDF VCE DUMPS & AIGP FREE DOWNLOAD TRAINING COLLECTION

IAPP Certified Artificial Intelligence Governance Professional pdf vce dumps & AIGP free download training collection

IAPP Certified Artificial Intelligence Governance Professional pdf vce dumps & AIGP free download training collection

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The Platform IAPP Certified Artificial Intelligence Governance Professional AIGP Exam credential makes it simple to renew your skills and knowledge to keep up with the latest trends. The Platform IAPP Certified Artificial Intelligence Governance Professional AIGP exam certification is a worthwhile, internationally accepted industry credential. You can become a recognized specialist in addition to learning new technological needs and honing your abilities.

IAPP AIGP Exam Syllabus Topics:

TopicDetails
Topic 1
  • Understanding AI Impacts and Responsible AI Principles: This topic identifies different risks that that ungoverned AI systems. The topic also describes features and principles that are essential for trustworthy and ethical AI.
Topic 2
  • Understanding the Existing and Emerging AI Laws and Standards: This topic discusses global AI-specific laws such as the EU AI Act and copyright’s Bill C-27.
Topic 3
  • Implementing Responsible AI Governance and Risk Management: It explains the collaboration of major AI stakeholders in a layered approach.
Topic 4
  • Understanding How Current Laws Apply to AI Systems: It focuses on laws that govern the use of artificial intelligence.
Topic 5
  • Contemplating Ongoing Issues and Concerns: The topic focuses on issues around AI governance.

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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q29-Q34):

NEW QUESTION # 29
Scenario:
An enterprise is evaluating multiple third-party generative AI tools to integrate into its platform. As part of its AI governance policy, it is assessing the most effective methods to reduce risks related to bias, data misuse, and liability when using third-party solutions.
All of the following are commonly adopted processes and policies in reducing potential risks introduced by third-party AI tools or applications EXCEPT:

  • A. Requiring an independent third-party bias audit for third-party generative AI tools
  • B. Requiring new use cases of the generative AI tools or applications to be reviewed and approved by the generative AI governance body
  • C. Including clauses in the procurement agreement for buyers of generative AI tools to put certain liabilities on the tool supplier
  • D. Allowing publicly available information and personally identifiable information (PII) to be incorporated into the prompt

Answer: D

Explanation:
The correct answer is B. Allowing PII to be freely entered into prompts without safeguards is considered a major privacy and security risk and is not a responsible governance practice.
From the AIGP ILT Guide - Generative AI & Third-Party Risk Management:
"Use of personal or sensitive information in AI prompts can result in unintended exposure, regulatory breaches, and downstream liability." The AI Governance in Practice Report 2024 highlights:
"PII should be minimized or protected by design. Prompt engineering should prevent entry of personally identifiable data unless legally and technically safeguarded." A, C, and D are established best practices under responsible AI procurement and use.


NEW QUESTION # 30
Under the Canadian Artificial Intelligence and Data Act, when must the Minister of Innovation, Science and Industry be notified about a high-impact Al system?

  • A. When the algorithmic impact assessment has been completed.
  • B. Upon initial deployment of the system.
  • C. Upon release of a new version of the system.
  • D. When use of the system causes or is likely to cause material harm.

Answer: B

Explanation:
According to the Canadian Artificial Intelligence and Data Act, high-impact AI systems must notify the Minister of Innovation, Science and Industry upon initial deployment. This requirement ensures that the authorities are aware of the deployment of significant AI systems and can monitor their impacts and compliance with regulatory standards from the outset. This initial notification is crucial for maintaining oversight and ensuring the responsible use of AI technologies. Reference: AIGP Body of Knowledge, domain on AI laws and standards.


NEW QUESTION # 31
According to the EU Al Act, providers of what kind of machine learning systems will be required to register with an EU oversight agency before placing their systems in the EU market?

  • A. Al systems that are high-risk.
  • B. Al systems that are "strong" general intelligence.
  • C. Al systems trained on sensitive personal data.
  • D. Al systems that are harmful based on a legal risk-utility calculation.

Answer: A

Explanation:
According to the EU AI Act, providers of high-risk AI systems are required to register with an EU oversight agency before these systems can be placed on the market. This requirement is part of the Act's framework to ensure that high-risk AI systems comply with stringent safety, transparency, and accountability standards.
High-risk systems are those that pose significant risks to health, safety, or fundamental rights. Registration with oversight agencies helps facilitate ongoing monitoring and enforcement of compliance with the Act's provisions. Systems categorized under other criteria, such as those trained on sensitive personal data or exhibiting "strong" general intelligence, also fall under scrutiny but are primarily covered under different regulatory requirements or classifications.


NEW QUESTION # 32
Scenario:
A financial services company is planning a new AI project to assess creditworthiness. The AI team is mapping out what tasks should be completed during the planning phase of the AI lifecycle.
The planning phase of the AI lifecycle includes all of the following EXCEPT:

  • A. Choice of the architecture
  • B. Definition of underlying assumptions
  • C. Context in which the model will operate
  • D. Approach to governance

Answer: A

Explanation:
The correct answer is C. The choice of architecture (e.g., neural networks vs. decision trees) is typically part of the design and development phase, not the initial planning.
From the AIGP Body of Knowledge - AI Lifecycle Module:
"Planning involves scoping, context definition, stakeholder identification, governance planning, and assumptions-not yet model selection." Confirmed in the ILT Participant Guide:
"Design decisions such as architecture or algorithm type come after planning-usually during development based on technical feasibility and data availability."


NEW QUESTION # 33
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 a 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.
Which of the following is the most important reason to train the underwriters on the model prior to deployment?

  • A. Tosolicit on-going feedback on model performance.
  • B. Toensure they provide transparency applicants on the model.
  • C. Toapply their own judgment to the initial assessment.
  • D. Toprovide a reminder of a right appeal.

Answer: C

Explanation:
Training underwriters on the model prior to deployment is crucial so they can apply their own judgment to the initial assessment. While AI models can streamline the process, human judgment is still essential to catch nuances that the model might miss or to account for any biases or errors in the model's decision-making process.
Reference: The AIGP Body of Knowledge emphasizes the importance of human oversight in AI systems, particularly in high-stakes areas such as underwriting and loan approvals. Human underwriters can provide a critical review and ensure that the model's assessments are accurate and fair, integrating their expertise and understanding of complex cases.


NEW QUESTION # 34
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