已驗證的新版AIGP題庫上線|高通過率的考試材料|授權的AIGP：IAPP Certified Artificial Intelligence Governance Professional

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IAPP AIGP 考試大綱：

主題簡介
主題 1
  • Understanding How to Govern AI Deployment and Use: This section of the exam measures skills of technology deployment leads and covers the responsibilities associated with selecting, deploying, and using AI models in a responsible manner. It includes evaluating key factors and risks before deployment, understanding different model types and deployment options, and ensuring ongoing monitoring and maintenance. The domain applies to both proprietary and third-party AI models, emphasizing the importance of transparency, ethical considerations, and continuous oversight throughout the model’s operational life.
主題 2
  • Understanding How to Govern AI Development: This section of the exam measures the skills of AI project managers and covers the governance responsibilities involved in designing, building, training, testing, and maintaining AI models. It emphasizes defining the business context, performing impact assessments, applying relevant laws and best practices, and managing risks during model development. The domain also includes establishing data governance for training and testing, ensuring data quality and provenance, and documenting processes for compliance. Additionally, it focuses on preparing models for release, continuous monitoring, maintenance, incident management, and transparent disclosures to stakeholders.
主題 3
  • Understanding the Foundations of AI Governance: This section of the exam measures skills of AI governance professionals and covers the core concepts of AI governance, including what AI is, why governance is needed, and the risks and unique characteristics associated with AI. It also addresses the establishment and communication of organizational expectations for AI governance, such as defining roles, fostering cross-functional collaboration, and delivering training on AI strategies. Additionally, it focuses on developing policies and procedures that ensure oversight and accountability throughout the AI lifecycle, including managing third-party risks and updating privacy and security practices.
主題 4
  • Understanding How Laws, Standards, and Frameworks Apply to AI: This section of the exam measures skills of compliance officers and covers the application of existing and emerging legal requirements to AI systems. It explores how data privacy laws, intellectual property, non-discrimination, consumer protection, and product liability laws impact AI. The domain also examines the main elements of the EU AI Act, such as risk classification and requirements for different AI risk levels, as well as enforcement mechanisms. Furthermore, it addresses the key industry standards and frameworks, including OECD principles, NIST AI Risk Management Framework, and ISO AI standards, guiding organizations in trustworthy and compliant AI implementation.

>> 新版AIGP題庫上線 <<

IAPP 新版AIGP題庫上線：IAPP Certified Artificial Intelligence Governance Professional可靠的認證資源

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最新的 Artificial Intelligence Governance AIGP 免費考試真題 (Q127-Q132):

問題 #127
All of the following are examples of biometric data in the US EXCEPT?

答案：C

解題說明：
Biometric data in the U.S. refers to data that relates to measurable biological and behavioral characteristics that can be used to identify an individual. Examples include fingerprints, facial recognition, iris scans, and behavior-based data like gait or keystrokes.
According to definitions and discussions from theAI Governance in Practice Report2025and U.S. privacy frameworks:
"Biometric data includes physical and behavioral human characteristics that can be used to digitally identify a person to grant access to systems, devices, or data. Examples include facial images, iris patterns, gait analysis, and voice recognition." (Report context based on common frameworks in U.S. AI law and the use of biometrics in AI governance.) Here's how the options relate:
* A. Iris scans- These are physical biometric identifiers.
* B. Walking gait- Behavioral biometric used increasingly in surveillance and identification.
* C. Keystroke dynamics- Behavioral biometric based on typing patterns.
* D. GPS location of a user's fitness watch- This isnotbiometric data. It islocation data, which may be sensitive or personal, but not biometric.


問題 #128
Random forest algorithms are in what type of machine learning model?

答案：C

解題說明：
Random forest algorithms are classified as discriminative models. Discriminative models are used to classify data by learning the boundaries between classes, which is the core functionality of random forest algorithms. They are used for classification and regression tasks by aggregating the results of multiple decision trees to make accurate predictions.


問題 #129
MULTI-SELECT
Please select 3 of the 5 options below. No partial credit will be given.
The use of paid generative AI public tools is appealing because?

答案：C,D,E

解題說明：
Paid public generative AI services can be attractive because organizations can adopt them rapidly without developing and maintaining foundational models themselves. Commercial offerings may also provide organizational privacy, security, access-management, and administrative controls beyond those available through basic public access, while centrally managed services can receive frequent provider-driven feature enhancements. These characteristics support A, B, and C. However, payment does not guarantee transparency into proprietary model architecture, training data, weights, or decision processes, so D is incorrect. Nor does a paid service eliminate concerns regarding training-data provenance, intellectual property, personal information, confidential prompts, data retention, or generated outputs; consequently, E is incorrect. AI governance still requires vendor due diligence, contractual safeguards, acceptable-use controls, security assessment, and continuing monitoring when organizations depend on third-party generative AI services.


問題 #130
In procuring an AI system from a vendor, which of the following would be important to include in a contract to enable proper oversight and auditing of the system?

答案：D

解題說明：
Ensuringoversight and auditabilityrequires that the organization hassufficient access to data, documentation, and model internalsor outputs necessary for evaluation.
From theAI Governance in Practice Report2025:
"Access to technical documentation and system internals is essential to enable effective auditing, conformity checks, and accountability mechanisms." (p. 11, 34)
* Ais about liability, not auditability.
* Bmatters for IP rights, not oversight.
* Crelates to lifecycle responsibility but doesn't guarantee audit access.


問題 #131
You are part of your organization's ML engineering team and notice that the accuracy of a model that was recently deployed into production is deteriorating. What is the best first step to address this?

答案：B

解題說明：
Champion/challenger testing allows comparison between the current model and alternatives to identify if a replacement or adjustment is necessary, addressing accuracy deterioration systematically.


問題 #132
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