FREE PDF QUIZ 2025 IAPP AIGP USEFUL ADVANCED TESTING ENGINE

Free PDF Quiz 2025 IAPP AIGP Useful Advanced Testing Engine

Free PDF Quiz 2025 IAPP AIGP Useful Advanced Testing Engine

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If you want to improve your career prospects, obtaining IAPP Certified Artificial Intelligence Governance Professional, AIGP exam certificate is a great way for you. IAPP Certified Artificial Intelligence Governance Professional certificate will help you land a job in the industry. After passing the IAPP Certified Artificial Intelligence Governance Professional you can increase your earning potential. This is because employers are ready to pay more for candidates who have passed the IAPP AIGP Certification test. Success in the AIGP exam can impact your promotion. If you are already an employee you can promote yourself to the highest level after passing the IAPP AIGP test.

The AIGP exam simulator plays a vital role in increasing your knowledge for exam. The 2Pass4sure’ IAPP Testing Engine provides an expert help and it is an exclusive offer for those who spend most of their time in searching relevant content in the books. It offers demos free of cost in the form of the Free AIGP Dumps. The IAPP AIGP exam questions aid its customers with updated and comprehensive information in an innovative style.

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AIGP is so flexible that you can easily change the timings, types of questions, and topics for each mock exam. 2Pass4sure's IAPP Certified Artificial Intelligence Governance Professional practice test contains all the important questions that will appear in the actual AIGP Exam. We design and update our IAPP AIGP exam questions after receiving precious feedback. You can try a demo and sample of AIGP exam questions before purchasing.

IAPP AIGP Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • Understanding How Current Laws Apply to AI Systems: It focuses on laws that govern the use of artificial intelligence.
Topic 3
  • Implementing Responsible AI Governance and Risk Management: It explains the collaboration of major AI stakeholders in a layered approach.

IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q69-Q74):

NEW QUESTION # 69
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 .. 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.
During the first month when ABC monitors the model for bias, it is most important to?

  • A. Compare the results to human decisions prior to deployment.
  • B. Seek approval from management for any changes to the model.
  • C. Analyze the quality of the training and testing data.
  • D. Continue disparity testing.

Answer: D

Explanation:
During the first month of monitoring the model for bias, it is most important to continue disparity testing.
Disparity testing involves regularly evaluating the model's decisions to identify and address any biases, ensuring that the model operates fairly across different demographic groups.
Reference: Regular disparity testing is highlighted in the AIGP Body of Knowledge as a critical practice for maintaining the fairness and reliability of AI models. By continuously monitoring for and addressing disparities, organizations can ensure their AI systems remain compliant with ethical and legal standards, and mitigate any unintended biases that may arise in production.


NEW QUESTION # 70
According to November 2023 White House Executive Order, which of the following best describes the guidance given to governmental agencies on the use of generative Al as a workplace tool?

  • A. Limit access to specific uses of generative Al.
  • B. Impose a general ban on the use of generative Al.
  • C. Limit access of generative Al to engineers and developers.
  • D. Impose a ban on the use of generative Al in agencies that protect national security.

Answer: A

Explanation:
The November 2023 White House Executive Order provides guidance that governmental agencies should limit access to specific uses of generative AI. This means that generative AI tools should be used in a controlled manner, where their applications are restricted to well-defined, approved use cases that ensure the security, privacy, and ethical considerations are adequately addressed. This approach allows for the benefits of generative AI to be harnessed while mitigating potential risks and abuses.
Reference: AIGP BODY OF KNOWLEDGE, sections on AI governance and risk management, and the White House Executive Order of November 2023.


NEW QUESTION # 71
All of the following are examples of biometric data in the US EXCEPT?

  • A. Iris scans.
  • B. GPS location of a user's fitness watch.
  • C. Keystroke dynamics.
  • D. Walking gait.

Answer: B

Explanation:
Biometric data in the U.S. refers to data that relates to measurable biological and behavioral characteristics that can be used to identify an individual. Examples include fingerprints, facial recognition, iris scans, and behavior-based data like gait or keystrokes.
According to definitions and discussions from theAI Governance in Practice Report 2024and U.S. privacy frameworks:
"Biometric data includes physical and behavioral human characteristics that can be used to digitally identify a person to grant access to systems, devices, or data. Examples include facial images, iris patterns, gait analysis, and voice recognition." (Report context based on common frameworks in U.S. AI law and the use of biometrics in AI governance.) Here's how the options relate:
* A. Iris scans- These are physical biometric identifiers.
* B. Walking gait- Behavioral biometric used increasingly in surveillance and identification.
* C. Keystroke dynamics- Behavioral biometric based on typing patterns.
* D. GPS location of a user's fitness watch- This isnotbiometric data. It islocation data, which may be sensitive or personal, but not biometric.


NEW QUESTION # 72
CASE STUDY
A global marketing agency is adapting a large language model ("LLM") to generate content for an upcoming marketing campaign for a client's new product: a hard hat designed for construction workers of any gender to better protect them from head injuries.
The marketing agency is accessing the LLM through an application programming interface ("API") developed by a third-party technology company. They want to generate text to be used for targeted advertising communications that highlight the benefits of the hard hat to potential purchasers. Both the marketing agency and the technology company have taken reasonable steps to address Al governance.
The marketing company has:
* Entered into a contract with the technology company with suitable representations and warranties.
* Completed an impact assessment on the LLM for this intended use.
* Built technical guidance on how to measure and mitigate bias in the LLM.
* Enabled technical aspects of transparency, explainability, robustness and privacy.
* Followed applicable regulatory requirements.
* Created specific legal statements and disclosures regarding the use of the Al on its client's advertising.
The technology company has:
* Provided guidance and resources to developers to address environmental concerns.
* Build technical guidance on how to measure and mitigate bias in the LLM.
* Provided tools and resources to measure bias specific to the LLM.
* Enabled technical aspects of transparency, explainability, robustness and privacy.
* Mapped and mitigated potential societal harms and large-scale impacts.
* Followed applicable regulatory requirements and industry standards.
* Created specific legal statements and disclosures regarding the LLM. including with respect to IP and rights to data.
Which stakeholder is responsible for the lawful collection of data used to train the foundational AI model?

  • A. The data aggregator
  • B. The tech company
  • C. The marketing agency's client
  • D. The marketing agency

Answer: B

Explanation:
The correct answer is B - The tech company. The party that develops and trains the foundational model is responsible for ensuring the lawful collection of training data.
From the AIGP ILT Guide - Foundational Models & Data Governance:
"Responsibility for the lawfulness of data collection typically lies with the party that trains the model- usually the provider or developer of the foundational model." AI Governance in Practice Report 2024 confirms:
"General Purpose AI providers are required to ensure that training data is lawfully acquired, including compliance with intellectual property and privacy requirements." The marketing agency is only a user or downstream integrator, not responsible for original data collection.


NEW QUESTION # 73
Scenario:
An organization wants to leverage its existing compliance structures to identify AI-specific risks as part of an ongoing data governance audit.
Which of the following compliance-related controls within an organization is most easily adapted to identify AI risks?

  • A. Privacy training
  • B. Transfer risk assessments
  • C. Privacy impact assessments
  • D. Penetration testing

Answer: C

Explanation:
The correct answer is D - Privacy impact assessments (PIAs). These are directly adaptable for identifying risks in AI systems, particularly around data usage, bias, and individual impacts.
From the AIGP ILT Guide - Risk Management Module:
"PIAs and DPIAs are existing tools used in privacy compliance that can be extended to evaluate the risks of AI, including fairness, explainability, and legality." AI Governance in Practice Report 2024 further explains:
"Organizations can adapt privacy impact assessments to evaluate the ethical, legal, and technical risks posed by AI systems. They provide a structured and recognized method." PIAs are preferable over general security practices (like pen testing) which do not address algorithmic bias or legal compliance directly.


NEW QUESTION # 74
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