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| Section | Weight | Objectives |
|---|---|---|
| Governance of AI Development | 21–25% | - Testing, validation, documentation and transparency - Requirements gathering, risk assessment and mitigation - Model management, version control and quality assurance - Governance through design and lifecycle management |
| Governance of AI Deployment and Use | 21–25% | - Incident response, accountability and audit - Lifecycle maintenance, decommissioning and updates - Selection, procurement and third-party management - Implementation, monitoring and ongoing oversight |
| Foundational Concepts of AI Governance | 24–28% | - Governance frameworks and core concepts - Principles of responsible, ethical and trustworthy AI - Fundamental AI definitions, types and characteristics - Impacts, risks and benefits of AI systems |
| Laws, Regulations, Standards and Frameworks | 24–28% | - Sector-specific requirements and compliance obligations - Global and regional regulations (EU AI Act, US, etc.) - International standards (ISO 42001, NIST AI RMF, etc.) - Alignment with data protection and privacy laws |
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NEW QUESTION # 12
To assist its internal recruiters with filtering job applications, a company decides to develop in-house an AI model for screening and ranking job applicants ' resumes.
Which of the following is a unique issue this company might face compared with using a third-party AI service?
Answer: A
Explanation:
Developing an AI model internally creates a direct need for technical expertise covering model development, infrastructure, deployment, monitoring, retraining, security, testing, and maintenance. This makes A the strongest distinguishing issue. Third-party AI services can reduce this operational burden because the external provider supplies much of the model and underlying infrastructure; IAPP guidance specifically notes that third-party APIs can accelerate deployment and reduce the burden of managing complex infrastructure. B is incorrect because an internally developed model generally provides more , not less, technical control. C is not necessarily true because total costs depend on scale, licensing, infrastructure, and usage. D is also too absolute: although developing a system can create substantial provider obligations, responsibility under AI laws and governance frameworks may still be distributed among providers, deployers, vendors, processors, and other actors depending on the circumstances.
NEW QUESTION # 13
Which of the following disclosures is NOT required for an EU organization that developed and deployed a high-risk AI system?
Answer: B
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 # 14
Which of the following arenotconsidered biometric data under U.S. privacy laws?
Answer: B
Explanation:
The correct answer isD. GPS location data isnot biometric data-it is consideredgeolocation data, which is personal data butnot biometricunder most U.S. laws.
From the AIGP ILT Guide (Data Privacy Module):
"Biometric data includes measurable biological or behavioral characteristics such as iris scans, facial recognition, voice prints, and keystroke patterns when used for identification." AI Governance in Practice Report2025(Privacy and Data Protection section):
"Location data, while sensitive, is not considered biometric unless it's tied to a uniquely identifyingbiological trait." Thus,GPS location data, while potentially sensitive, is not classified as biometric.
NEW QUESTION # 15
CASE STUDY
Please use the following to answer the next question:
You have recently assumed the role of AI Governance leader for a California-based medical technology company. The organization primarily serves hospitals and has recently expanded to include walk-in clinics located within local pharmacies.
The company ' s core business focuses on diagnostic assistance powered by a large language model LLM and back-office process optimization using Agentic AI, including chatbots, medical record request handling, scheduling and billing.
In preparation for its next round of funding, the board has asked you to prepare an AI Risk report to demonstrate to investors how the company is addressing AI-related risks. In preparing the report you learn that last year the company generated 30 million dollars in gross revenue across the US, EU, India, and South Korea and that vendors are engaged for various activities, including model testing and providing third-party AI solutions for chatbots.
Which of the following would provide you the best information addressing quality principles pertaining to the functioning of the AI agents and LLM?
Answer: B
Explanation:
The correct answer is D because it directly reflects core data and model quality principles such as accuracy, performance consistency, and real-world effectiveness across different user groups. AI governance frameworks emphasize that quality must be evaluated based on whether outputs are accurate, complete, and fit for purpose in real-world conditions. Measuring accuracy by user group also supports fairness and bias detection, which are essential components of trustworthy AI. Option D captures outcome-based performance and aligns with continuous monitoring expectations across the AI lifecycle. In contrast, options A and C focus more on operational or technical metrics, while B reflects user sentiment rather than objective quality.
According to AI governance principles, high-quality AI systems require ongoing evaluation of outputs against real-world results to ensure reliability, validity, and safe deployment.
NEW QUESTION # 16
All of the following are common optimization techniques in deep learning to determine weights that represent the strength of the connection between artificial neurons EXCEPT?
Answer: C
Explanation:
Autoregression is not a common optimization technique in deep learning to determine weights for artificial neurons. Common techniques include gradient descent, momentum, and backpropagation. Autoregression is more commonly associated with time-series analysis and forecasting rather than neural network optimization. Reference: AIGP BODY OF KNOWLEDGE, which discusses common optimization techniques used in deep learning.
NEW QUESTION # 17
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