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| Section | Objectives |
|---|---|
| AI Risk Management and Impact Assessment | - Risk identification and mitigation
|
| AI Lifecycle Governance Implementation | - Operational governance controls
|
| Regulatory and Legal Frameworks for AI | - Global AI governance regulations
|
| Foundations of AI Systems and Governance | - AI system concepts, lifecycle, and terminology
|
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NEW QUESTION # 46
Which stakeholder is responsible for lawful collection of data for the training of the foundational AI model?
Answer: B
Explanation:
Data aggregators are third parties that collect and license data from various sources. They are responsible for ensuring thelawful collectionandproper usage rightsof the data they distribute - especially when such data is used to train foundational AI models.
From theAI Governance in Practice Report 2025:
"As organizations have neither proximity to how third-party data was first collected nor direct control over the data governance practices of third parties, an organization can benefit from carrying out its own legal due diligence and third-party risk management." (p. 19)
"Legal due diligence may include verification of the personal data ' s lawful collection by the databroker..." (p. 19) This confirms thatdata aggregatorsbear the legal and ethical burden to verify that data has been lawfully collected and is appropriately licensed for use, including in AI training.
* A. The marketing agencyandD. its clientmay use data, but they rely on upstream providers for its lawful origin.
* B. The tech companymay train the model but depends on lawful sourcing by data aggregators.
NEW QUESTION # 47
Which of the following disclosures is NOT required for an EU organization that developed and deployed a high-risk AI system?
Answer: D
Explanation:
While transparency about AI use, human oversight, and contesting decisions are required disclosures for high-risk AI systems under the EU AI Act, the specific locations where data is stored are not mandated disclosures.
NEW QUESTION # 48
MULTI-SELECT
Please select 3 of the 5 options below. No partial credit will be given.
Training an AI model is time-consuming because of?
Answer: A,B,C
Explanation:
Training an AI model is time-consuming primarily due tomodel complexity,large data volumes, and the need forhigh-quality, well-prepared data.
From theAI Governance in Practice Report2025:
"Most AI requires sizeable amounts of high-quality data... to ensure desired and accurate output." (p. 15)
"The accuracy of AI model outputs depends significantly on the quality of their inputs." (p. 24)
"Complex AI systems... with many parameters... result in long development and training phases." (p. 32)
* B. Maturity of governanceaffects oversight, not training time.
* D. Number of stakeholdersaffects alignment, not direct training duration.
NEW QUESTION # 49
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?
Answer: A
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 # 50
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?
Answer: A
Explanation:
Differential privacy provides mathematical guarantees that individual data points have minimal influence on model outputs, protecting privacy during training.
NEW QUESTION # 51
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