LATEST AIGP TEST ONLINE | AIGP EXAM QUESTIONS ANSWERS

Latest AIGP Test Online | AIGP Exam Questions Answers

Latest AIGP Test Online | AIGP Exam Questions Answers

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IAPP AIGP Exam Syllabus Topics:

TopicDetails
Topic 1
  • Understanding the AI Development Life Cycle: The topic outlines the context in which AI risks are managed.
Topic 2
  • Understanding How Current Laws Apply to AI Systems: It focuses on laws that govern the use of artificial intelligence.
Topic 3
  • 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 4
  • Contemplating Ongoing Issues and Concerns: The topic focuses on issues around AI governance.
Topic 5
  • Implementing Responsible AI Governance and Risk Management: It explains the collaboration of major AI stakeholders in a layered approach.

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AIGP Exam Questions Answers & AIGP Simulation Questions

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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q16-Q21):

NEW QUESTION # 16
During the planning and design phases of the Al development life cycle, bias can be reduced by all of the following EXCEPT?

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

Answer: B

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 # 17
When monitoring the functional performance of a model that has been deployed into production, all of the following are concerns EXCEPT?

  • A. System cost.
  • B. Feature drift.
  • C. Data loss.
  • D. Model drift.

Answer: A

Explanation:
When monitoring the functional performance of a model deployed into production, concerns typically include feature drift, model drift, and data loss. Feature drift refers to changes in the input features that can affect the model's predictions. Model drift is when the model's performance degrades over time due to changes in the data or environment. Data loss can impact the accuracy and reliability of the model. However, system cost, while important for budgeting and financial planning, is not a direct concern when monitoring the functional performance of a deployed model. Reference: AIGP Body of Knowledge on Model Monitoring and Maintenance.


NEW QUESTION # 18
A company has trained an ML model primarily using synthetic data, and now intends to use live personal data to test the model.
Which of the following is NOT a best practice apply during the testing?

  • A. The test data should be representative of the expected operationaldata.
  • B. Testing should be performed specific to the intended uses.
  • C. Testing should minimize human involvement to the extent practicable.
  • D. The test data should be anonymized to the extent practicable.

Answer: C

Explanation:
Minimizing human involvement to the extent practicable is not a best practice during the testing of an ML model. Human oversight is crucial during testing to ensure that the model performs correctly and ethically, and to interpret any anomalies or issues that arise. Best practices include using representative test data, anonymizing data to the extent practicable, and performing testing specific to the intended uses of the model.
Reference: AIGP Body of Knowledge on AI Model Testing and Human Oversight.


NEW QUESTION # 19
Scenario:
A U.S.-based AI governance professional is evaluating resources from the National Institute of Standards and Technology (NIST) to guide the organization's AI risk assessment strategy. They are particularly interested in programs focused on assessing AI-specific impacts.
The main purpose of NIST's Assessing Risks and Impacts of AI (ARIA) program is to:

  • A. Pilot new standards for AI red-teaming
  • B. Promote interoperability across AI systems
  • C. Provide a suite of resources to manage risks
  • D. Offer a regulatory sandbox for risk reporting

Answer: C

Explanation:
The correct answer is A. The ARIA program by NIST is explicitly designed to support stakeholders in understanding and managing the risks and impacts of AI systems.
From the AIGP ILT Guide - U.S. Risk Frameworks Module:
"NIST's ARIA program develops and pilots assessment tools for AI risks and impacts, aimed at improving organizational capacity for responsible AI use." Also cited in the AI Governance in Practice Report 2024 (Frameworks Section):
"ARIA supports and aligns with the AI Risk Management Framework by helping organizations assess AI harms, safety concerns, and societal implications." ARIA is not a red-teaming or sandbox program-it's an assessment and governance resource.


NEW QUESTION # 20
Which model is best for efficiency and agility, and tailored for lower-resource settings?

  • A. Small language model.
  • B. Supervised learning model.
  • C. Multimodal model.
  • D. Generative language model.

Answer: A

Explanation:
Small language models (SLMs)arelightweight, requireless compute, and arebetter suited to low-resource or edge environments, making them ideal for agility and efficiency.
From general AI best practices:
"SLMs can be deployed in environments with limited computing power, ensuring lower cost and faster integration in constrained contexts." (aligned with industry-wide AI deployment strategies)


NEW QUESTION # 21
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