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[Hardware] IAPP AIGP Test Centres & Latest AIGP Test Cram

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【Hardware】 IAPP AIGP Test Centres & Latest AIGP Test Cram

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P.S. Free & New AIGP dumps are available on Google Drive shared by 2Pass4sure: https://drive.google.com/open?id=1uW21FJNCBTYE5-QUO20vhNHXzscj_VB3
The IAPP AIGP exam questions are being offered in three different formats. These formats are IAPP AIGP PDF dumps files, desktop practice test software, and web-based practice test software. All these three IAPP AIGP Exam Dumps formats contain the real IAPP Certified Artificial Intelligence Governance Professional (AIGP) exam questions that assist you in your AIGP practice exam preparation and finally, you will be confident to pass the final AIGP exam easily.
IAPP AIGP Exam Syllabus Topics:
TopicDetails
Topic 1
  • 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.
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 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 4
  • 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.

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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q15-Q20):NEW QUESTION # 15
MULTI-SELECT
Please select 3 of the 5 options below. No partial credit will be given.
What are the roles and responsibilities of deployers of a proprietary model?
  • A. Ethical testing.
  • B. Ethical design.
  • C. Technical performance.
  • D. Regulatory compliance.
  • E. System documentation.
Answer: A,C,D
Explanation:
Deployers of proprietary models arenot responsible for design, but they are accountable for how the system performsin their context of use, including ensuring ethical behavior, performance, and legal compliance.
From theAI Governance in Practice Report2025:
"Deployers of AI systems must take reasonable steps to ensure that systems are used ethically, perform safely, and align with applicable laws and standards." (p. 11-12)
"Operational governance... includes performance monitoring protocols, incident management plans, and regulatory oversight." (p. 12) Thus:
* #A. Ethical testing- Required to mitigate misuse and unintended harms.
* #B. Ethical design- Belongs todevelopers/providers, not deployers.
* #C. Technical performance- Deployers must ensure that AI performs as expected.
* #D. System documentation- This is theprovider'sobligation.
* #E. Regulatory compliance- Deployers must ensure system use complies with applicable laws.

NEW QUESTION # 16
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?
  • A. Document maintenance teams and processes.
  • B. Perform a readiness assessment.
  • C. Define a model-validation methodology.
  • D. Identify known edge cases to monitor post-deployment.
Answer: B
Explanation:
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.

NEW QUESTION # 17
What is the key feature of Graphical Processing Units (GPUs) that makes them well-suited to running Al applications?
  • A. GPUs can run every task on a computer, making them more robust than CPUs.
  • B. The number of transistors on GPUs doubles every two years, making thechips smaller and lighter.
  • C. GPUs run many tasks concurrently, resulting in faster processing.
  • D. GPUs can access memory quickly, resulting in lower latency than CPUs.
Answer: C
Explanation:
GPUs (Graphical Processing Units) are well-suited to running AI applications due to their ability to run many tasks concurrently, which significantly enhances processing speed. This parallel processing capability makes GPUs ideal for handling the large-scale computations required in AI and deep learning tasks. Reference: AIGP BODY OF KNOWLEDGE, which explains the importance of compute infrastructure in AI applications.

NEW QUESTION # 18
CASE STUDY
A company is considering the procurement of an AI system designed to enhance the security of IT infrastructure. The AI system analyzes how users type on their laptops, including typing speed, rhythm and pressure, to create a unique user profile. This data is then used to authenticate users and ensure that only authorized personnel can access sensitive resources.
When prioritizing the updates to its policies, rules and procedures to include the new AI system for user authentication, the organization should:
  • A. Reduce the complexity of the policy to make it easier for non-technical employees to understand
  • B. Update third-party data sharing policies
  • C. Update security controls for sensitive data
  • D. Ensure that any personal data used is only processed for a specific and lawful purpose
Answer: D
Explanation:
The correct answer is C. This action ties directly into principles of data minimization, purpose limitation, and lawfulness of processing, which are central to privacy and AI governance.
From the AIGP Body of Knowledge, Section on Privacy Considerations:
"Personal data must only be processed for specified and lawful purposes. Organizations must consider whether they have a legal basis for processing such data under data protection laws like the GDPR or CCPA." Additionally, AI Governance in Practice Report 2024 emphasizes:
"One of the most significant challenges when designing and developing AI systems is ensuring the data used is appropriate for the intended purpose... Managing unnecessary data, especially data that may contain sensitive attributes, can increase risk."

NEW QUESTION # 19
Scenario:
A company using AI for resume screening understands the risks of algorithmic bias and the evolving legal requirements across jurisdictions. It wants to implement the right governance controls to prevent reputational damage from misuse of the AI hiring tool.
Which of the following measures should the company adopt to best mitigate its risk of reputational harm from using the AI tool?
  • A. Require the procurement and deployment teams to agree upon the AI tool
  • B. Ensure the vendor provides indemnification for the AI tool
  • C. Test the AI tool pre- and post-deployment
  • D. Continue to require the company's hiring personnel to manually screen all applicants
Answer: C
Explanation:
The correct answer is A. Pre- and post-deployment testing ensures bias, accuracy, and fairness are evaluated and corrected as needed, which is essential for reputational risk mitigation.
From the AIGP Body of Knowledge:
"Testing AI systems before and after deployment is critical to ensure performance, fairness, and compliance.
Failing to do so may result in reputational damage and legal exposure." AI Governance in Practice Report 2024 (Bias/Fairness and Risk Sections):
"System impact assessments, testing, and post-deployment monitoring are necessary to identify and mitigate risks... This supports both compliance and public trust." Testing is proactive, unlike indemnification (which transfers risk after damage), or requiring manual review (which defeats automation).

NEW QUESTION # 20
......
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