AIGP DOWNLOAD | LATEST AIGP DUMPS SHEET

AIGP Download | Latest AIGP Dumps Sheet

AIGP Download | Latest AIGP Dumps Sheet

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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q149-Q154):

NEW QUESTION # 149
All of the following are potential benefits of using private over public LLMs EXCEPT?

  • A. Application for specific use cases within the enterprise.
  • B. Reduction in time taken for data validation and verification.
  • C. Reduction in possibility of hallucinated information.
  • D. Confirmation of security and confidentiality.

Answer: B

Explanation:
Private LLMs offer advantages likecustomizability,reduced hallucination,confidentiality, andalignment with enterprise-specific tasks, but theydo not inherently reduce the time or effortneeded fordata validation or verification- which remains an essential step regardless of model privacy.
From the AI risk and quality sections:
"Ensuring the quality of the data... is highly contextual and must be validated regardless of the model's deployment environment." (p. 17)
* B, C, Dare legitimate benefits of private LLMs.
* Ais incorrect - validation still requires time and resources.


NEW QUESTION # 150
What is the primary purpose of an Al impact assessment?

  • A. To identify and measure the benefits of an Al system.
  • B. To define and document the roles and responsibilities of Al stakeholders.
  • C. Anticipate and manage the potential risks and harms of an Al system.
  • D. To define and evaluate the legal risks associated with developing an Al system.

Answer: C

Explanation:
The primary purpose of an AI impact assessment is to anticipate and manage the potential risks and harms of an AI system. This includes identifying the possible negative outcomes and implementing measures to mitigate these risks. This process helps ensure that AI systems are developed and deployed in a manner that is ethically and socially responsible, addressing concerns such as bias, fairness, transparency, and accountability. The assessment often involves a thorough evaluation of the AI system's design, data inputs, outputs, and the potential impact on various stakeholders. This approach is crucial for maintaining public trust and adherence to regulatory requirements.


NEW QUESTION # 151
What is the best method to proactively train an LLM so that there is mathematical proof that no specific piece of training data has more than a negligible effect on the model or its output?

  • A. Clustering.
  • B. Differential privacy.
  • C. Transfer learning.
  • D. Data compartmentalization.

Answer: B

Explanation:
Differential privacy is a technique used to ensure that the inclusion or exclusion of a single data point does not significantly affect the outcome of any analysis, providing a way to mathematically prove that no specific piece of training data has more than a negligible effect on the model or its output. This is achieved by introducing randomness into the data or the algorithms processing the data. In the context of training large language models (LLMs), differential privacy helps in protecting individual data points while still enabling the model to learn effectively. By adding noise to the training process, differential privacy provides strong guarantees about the privacy of the training data.
Reference: AIGP BODY OF KNOWLEDGE, pages related to data privacy and security in model training.


NEW QUESTION # 152
Under the NIST Al Risk Management Framework, all of the following are defined as characteristics of trustworthy Al EXCEPT?

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

Answer: A

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 # 153
Your organization is searching for a new way to help accurately forecast sales predictions by various types of customers.
Which of the following is the best type of model to choose if your organization wants to customize the model and avoid lock-in?

  • A. A proprietary generative AI model.
  • B. A classic machine learning model.
  • C. A free large language model.
  • D. A subscription-based, multimodal model.

Answer: B

Explanation:
Forcustomizable, interpretable modelsthat allow organizations toretain control and avoid vendor lock-in, classic ML models(e.g., regression, decision trees, random forests) are optimal.
From theAI Governance in Practice Report 2024:
"Organizations seeking transparency, customizability, and control often prefer classic ML models due to their flexibility and ease of governance." (p. 33)
* AandCmay have limited transparency and are often tied to specific providers.
* Dinvolves ongoing costs and limited model control.


NEW QUESTION # 154
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