AIGP Question Includes: Single Choice Questions: 189, Multiple Choice Questions: 5,
Which model is best for efficiency and agility, and tailored for lower-resource settings?
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.
The marketing company and its tech provider have taken reasonable steps to govern the AI’s use, including legal disclosures, impact assessments, and bias mitigation. However, the company wants to takeone more stepto improve governance and reduce risks related to ongoing oversight and accountability.
While the marketing agency took steps to mitigate its risks, the best additional step would be to:
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?
Scenario:
A financial services company is planning a new AI project to assess creditworthiness. The AI team is mapping out what tasks should be completed during theplanning phaseof the AI lifecycle.
The planning phase of the AI lifecycle includes all of the following EXCEPT:
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.
In the design phase, which of the following steps is most important in gathering the data from the clinical research partner?
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The IAPP AIGP Exam, officially titled Artificial Intelligence Governance Professional, certifies your ability to manage and govern AI systems responsibly. It’s designed for professionals who want to ensure ethical, safe, and compliant AI deployment across industries.
The AIGP credential is suitable for professionals in roles such as AI compliance, risk management, data scientists, AI project managers, model operations (MLOps), legal & governance, privacy professionals, and those building or overseeing AI systems who must ensure ethical, safe, and trustworthy AI.
The AIGP exam includes the following core domains:
AI Fundamentals and Use Cases
Responsible AI Principles
AI Risk Management
AI Governance Frameworks
Legal and Regulatory Landscape
AI Lifecycle and Oversight
Ethical Considerations and Emerging Issues
The AIGP exam includes 85 scored multiple-choice questions and 15 unscored questions, with a total duration of 3 hours. It is delivered online via a secure testing platform.
No formal prerequisites are required to register for the AIGP exam. You do not need prior IAPP certifications, though a strong background or understanding of AI, privacy, risk, or governance frameworks is highly beneficial.
The AIGP certification is valid for two years from the date you pass the exam. To maintain it, holders must: (1) earn 20 continuing education credits (CPEs) relevant to the AIGP BoK.
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