AIGP考試資料 - AIGP更新

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AIGP更新,AIGP考題資訊

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最新的 Artificial Intelligence Governance AIGP 免費考試真題 (Q121-Q126):

問題 #121
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 #122
Scenario:
An organization is developing a powerful general-purpose AI (GPAI) model that has systemic impact. The compliance team is assessing what legal obligations apply under the EU AI Act.
Under the EU AI Act, which of the following compliance actions appliesonly to General Purpose AI models with systemic risk?

答案:D

解題說明:
The correct answer isA. Only GPAI modelswith systemic riskmustpublish a detailed summary of training datato meet transparency and accountability standards under the EU AI Act.
From the AI Governance in Practice Report2025(EU AI Act Section):
"For GPAI systems with systemic risk, providers must publish sufficiently detailed summaries of the content used to train the model." Also, the AIGP ILT Guide confirms:
"The obligation to disclose summaries of training data applies only to systemic-risk GPAI models, not all general-purpose models or high-risk systems." This unique requirement is part of the Act's effort to increasetransparency and auditabilityfor powerful foundational models.


問題 #123
When evaluating which AI use cases to implement, an organization should consider all of the following EXCEPT:

答案:A

解題說明:
TEVV (testing, evaluation, verification, and validation) metrics apply after a use case is selected and designed. They are not a primary factor when initially evaluating which AI use cases to pursue.


問題 #124
You are an engineer that developed an Al-based ad recommendation tool.
Which of the following should be monitored to evaluate the tool's effectiveness?

答案:B

解題說明:
To evaluate the effectiveness of an AI-based ad recommendation tool, the most relevant metric is the output data, specifically assessing the delta between the prediction and actual ad clicks. This metric directly measures the tool's accuracy and effectiveness in making accurate recommendations that lead to user engagement. While monitoring algorithmic patterns and input data can provide insights into the model's behavior and targeting accuracy, and GPU performance can indicate the robustness and efficiency of the tool, the primary indicator of effectiveness for an ad recommendation tool is how well it predicts actual ad clicks.
Reference: AIGP BODY OF KNOWLEDGE, sections on AI performance metrics and evaluation methods.


問題 #125
What is the best method to apply to an AI model so that it is statistically unlikely that a specific piece of training data can be identified from a model ' s output?

答案:D

解題說明:
The correct answer is C because differential privacy is specifically designed to ensure that the inclusion or exclusion of any single data point does not significantly affect the output of a model. This provides strong mathematical guarantees that individual training data cannot be reverse-engineered or identified from model outputs. Differential privacy works by introducing controlled noise into the data or model outputs, thereby protecting individual-level information while preserving overall statistical patterns. In AI governance, it is widely recognized as a key privacy-enhancing technology for mitigating risks such as data leakage, membership inference attacks, and re-identification. Other options, such as homomorphic encryption, focus on secure computation, while data sharding and compartmentalization address data management but do not provide formal privacy guarantees at the model output level.


問題 #126
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