AIGP DUMPS REVIEWS & AIGP RELIABLE TEST VOUCHER

AIGP Dumps Reviews & AIGP Reliable Test Voucher

AIGP Dumps Reviews & AIGP Reliable Test Voucher

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IAPP AIGP Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • Implementing Responsible AI Governance and Risk Management: It explains the collaboration of major AI stakeholders in a layered approach.
Topic 3
  • 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.

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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q59-Q64):

NEW QUESTION # 59
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. Transfer learning.
  • B. Differential privacy.
  • C. Data compartmentalization.
  • D. Clustering.

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 # 60
Which of the following would be the least likely step for an organization to take when designing an integrated compliance strategy for responsible Al?

  • A. Consulting experts to consider the ethical principles underpinning the use of Al within the organization.
  • B. Employing a new software platform to modernize existing compliance processes across the organization.
  • C. Conducting an assessment of existing compliance programs to determine overlaps and integration points.
  • D. Launching a survey to understand the concerns and interests of potentially impacted stakeholders.

Answer: B

Explanation:
When designing an integrated compliance strategy for responsible AI, the least likely step would be employing a new software platform to modernize existing compliance processes. While modernizing compliance processes is beneficial, it is not as directly related to the strategic integration of ethical principles and stakeholder concerns. More critical steps include conducting assessments of existing compliance programs to identify overlaps and integration points, consulting experts on ethical principles, and launching surveys to understand stakeholder concerns. These steps ensure that the compliance strategy is comprehensive and aligned with responsible AI principles. Reference: AIGP Body of Knowledge on AI Governance and Compliance Integration.


NEW QUESTION # 61
To maintain fairness in a deployed system, it is most important to?

  • A. Optimize computational resources and data to ensure efficiency and scalability.
  • B. Detect anomalies outside established metrics that require new training data.
  • C. Protect against loss of personal data in the model.
  • D. Monitor for data drift that may affect performance and accuracy.

Answer: D

Explanation:
To maintain fairness in a deployed system, it is crucial to monitor for data drift that may affect performance and accuracy. Data drift occurs when the statistical properties of the input data change over time, which can lead to a decline in model performance. Continuous monitoring and updating of the model with new data ensure that it remains fair and accurate, adapting to any changes in the data distribution. Reference: AIGP Body of Knowledge on Post-Deployment Monitoring and Model Maintenance.


NEW QUESTION # 62
CASE STUDY
Please use the following answer the next question:
A mid-size US healthcare network has decided to develop an Al solution to detect a type of cancer that is most likely arise in adults. Specifically, the healthcare network intends to create a recognition algorithm that will perform an initial review of all imaging and then route records a radiologist for secondary review pursuant Agreed-upon criteria (e.g., a confidence score below a threshold).
To date, the healthcare network has taken the following steps: defined its Al ethical principles: conducted discovery to identify the intended uses and success criteria for the system: established an Al governance committee; assembled a broad, crossfunctional team with clear roles and responsibilities; and created policies and procedures to document standards, workflows, timelines and risk thresholds during the project.
The healthcare network intends to retain a cloud provider to host the solution and a consulting firm to help develop the algorithm using the healthcare network's existing data and de-identified data that is licensed from a large US clinical research partner.
Which of the following steps can best mitigate the possibility of discrimination prior to training and testing the Al solution?

  • A. Engage a third party to perform an audit.
  • B. Perform an impact assessment.
  • C. Create a bias bounty program.
  • D. Procure more data from clinical research partners.

Answer: B

Explanation:
Performing an impact assessment is the best step to mitigate the possibility of discrimination before training and testing the AI solution. An impact assessment, such as a Data Protection Impact Assessment (DPIA) or Algorithmic Impact Assessment (AIA), helps identify potential biases and discriminatory outcomes that could arise from the AI system. This process involves evaluating the data and the algorithm for fairness, accountability, and transparency. It ensures that any biases in the data are detected and addressed, thus preventing discriminatory practices and promoting ethical AI deployment. Reference: AIGP Body of Knowledge on Ethical AI and Impact Assessments.


NEW QUESTION # 63
The most important factor in ensuring fairness when training an Al system is?

  • A. The data attributes and variability.
  • B. The data labeling and classification.
  • C. The architecture and model selection.
  • D. The model accuracy and scale.

Answer: A

Explanation:
Ensuring fairness when training an AI system largely depends on the data attributes and variability. This involves having a diverse and representative dataset that accurately reflects the population the AI system will serve. Fairness can be compromised if the data is biased or lacks variability, as the model may learn and perpetuate these biases. Diverse data attributes ensure that the model learns from a wide range of examples, reducing the risk of biased predictions. Reference: AIGP Body of Knowledge on Ethical AI Principles and Data Management.


NEW QUESTION # 64
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Our IAPP Certified Artificial Intelligence Governance Professional (AIGP) PDF format is user-friendly and accessible on any smart device, allowing applicants to study from anywhere at any time. We have included actual and updated IAPP AIGP questions in this IAPP Certified Artificial Intelligence Governance Professional (AIGP) Dumps PDF file. Our IAPP Certified Artificial Intelligence Governance Professional (AIGP) exam dumps PDF format is designed to help individuals acquire the knowledge necessary to succeed in the test.

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