EFFICIENT AIGP RELIABLE DUMPS EBOOK - FIND SHORTCUT TO PASS AIGP EXAM

Efficient AIGP Reliable Dumps Ebook - Find Shortcut to Pass AIGP Exam

Efficient AIGP Reliable Dumps Ebook - Find Shortcut to Pass AIGP Exam

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Tags: AIGP Reliable Dumps Ebook, AIGP Exam Simulator, AIGP Exam Dumps.zip, Valid AIGP Braindumps, Related AIGP Certifications

We provide you with the latest prep material which is according to the content of IAPP AIGP certification exam and enhances your knowledge to crack the test. TrainingDumps practice material is made by keeping in focus all the sections of the current syllabus. Our primary objective is to provide you with IAPP Certified Artificial Intelligence Governance Professional (AIGP) actual questions to complete preparation for the test in few days. Our product includes IAPP Certified Artificial Intelligence Governance Professional real questions, desktop practice test software, and web-based practice exam. Keep reading to find out what are the specifications of these formats.

IAPP AIGP Exam Syllabus Topics:

TopicDetails
Topic 1
  • Contemplating Ongoing Issues and Concerns: The topic focuses on issues around AI governance.
Topic 2
  • Understanding How Current Laws Apply to AI Systems: It focuses on laws that govern the use of artificial intelligence.
Topic 3
  • Understanding the Foundations of Artificial Intelligence: This topic defines AI and machine learning. It also provides an overview of the different types of AI systems and their use cases.
Topic 4
  • Understanding the Existing and Emerging AI Laws and Standards: This topic discusses global AI-specific laws such as the EU AI Act and Canada’s Bill C-27.
Topic 5
  • 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 6
  • Implementing Responsible AI Governance and Risk Management: It explains the collaboration of major AI stakeholders in a layered approach.

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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q79-Q84):

NEW QUESTION # 79
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.
The best approach to enable a customer who wants information on the Al model's parameters for underwriting purposes is to provide?

  • A. A transparency notice.
  • B. Detailed terms of service.
  • C. Customer service support.
  • D. An opt-out mechanism.

Answer: A

Explanation:
The best approach to enable a customer who wants information on the AI model's parameters for underwriting purposes is to provide a transparency notice. This notice should explain the nature of the AI system, how it uses customer data, and the decision-making process it follows. Providing a transparency notice is crucial for maintaining trust and compliance with regulatory requirements regarding the transparency and accountability of AI systems.
Reference: According to the AIGP Body of Knowledge, transparency in AI systems is essential to ensure that stakeholders, including customers, understand how their data is being used and how decisions are made. This aligns with ethical principles of AI governance, ensuring that customers are informed and can make knowledgeable decisions regarding their interactions with AI systems.


NEW QUESTION # 80
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.
All of the following are obligations of the company as a data controller when implementing its AI system EXCEPT?

  • A. Implementing technical and organizational measures
  • B. Ensuring that third-party processors are based in the same country as the company
  • C. Conducting a Data Protection Impact Assessment (DPIA) / Privacy Impact Assessment (PIA)
  • D. Allowing data subject access requests (DSARs)

Answer: B

Explanation:
The correct answer is A. While location of processors may have implications (such as for data transfers under GDPR), there is no absolute requirement that third-party processors be based in the same country.
From the AI Governance in Practice Report 2024 and ILT Guide:
"Data controllers are responsible for ensuring that third-party processors have adequate protections, but not necessarily that they reside in the same jurisdiction. What is required is legal safeguards (e.g., SCCs) for international transfers, not same-country location." In contrast, DPIAs, DSARs, and implementation of technical/organizational safeguards are explicitly required under GDPR and responsible AI frameworks.


NEW QUESTION # 81
Under the NIST Al Risk Management Framework, all of the following are defined as characteristics of trustworthy Al EXCEPT?

  • A. Explainable and Interpretable.
  • B. Secure and Resilient.
  • C. Tested and Effective.
  • D. Accountable and Transparent.

Answer: C

Explanation:
The NIST AI Risk Management Framework outlines several characteristics of trustworthy AI, including being secure and resilient, explainable and interpretable, and accountable and transparent. While being tested and effective is important, it is not explicitly listed as a characteristic of trustworthy AI in the NIST framework.
The focus is more on the system's ability to function safely, securely, and transparently in a way that stakeholders can understand and trust. Reference: AIGP Body of Knowledge, NIST AI RMF section.


NEW QUESTION # 82
During the planning and design phases of the Al development life cycle, bias can be reduced by all of the following EXCEPT?

  • A. Data collection.
  • B. Stakeholder involvement.
  • C. Human oversight.
  • D. Feature selection.

Answer: D

Explanation:
Bias in AI can be reduced during the planning and design phases through stakeholder involvement, human oversight, and careful data collection. While feature selection is critical in the development phase, it does not specifically occur during planning and design. Ensuring diverse stakeholder involvement and human oversight helps identify and mitigate potential biases early, and data collection ensures a representative dataset. Reference: AIGP Body of Knowledge on AI Development Lifecycle and Bias Mitigation.


NEW QUESTION # 83
Machine learning is best described as a type of algorithm by which?

  • A. Systems can automatically improve from experience through predictive patterns.
  • B. Previously unknown properties are discovered in data and used to predict and make improvements in the data.
  • C. Systems can mimic human intelligence with the goal of replacing humans.
  • D. Statistical inferences are drawn from a sample with the goal of predicting human intelligence.

Answer: A

Explanation:
Machine learning (ML) is a subset of artificial intelligence (AI) where systems use data to learn and improve over time without being explicitly programmed. Option B accurately describes machine learning by stating that systems can automatically improve from experience through predictive patterns. This aligns with the fundamental concept of ML where algorithms analyze data, recognize patterns, and make decisions with minimal human intervention. Reference: AIGP BODY OF KNOWLEDGE, which covers the basics of AI and machine learning concepts.


NEW QUESTION # 84
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