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IAPP AIGP Exam Questions

IAPP AIGP Exam Questions Answers

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IAPP AIGP Practice Test Questions ( Updated) – Real Exam Questions & Dumps PDF

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IAPP AIGP Sample Questions – Free Practice Test & Real Exam Prep

Question #1

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

  • A. Homomorphic encryption. 
  • B. Data sharding. 
  • C. Differential privacy. 
  • D. Data compartmentalization. 
Answer: C
Explanation:
Differential privacy adds mathematical noise to the training process making it statistically
unlikely that any specific individual's data can be reverse engineered from the model's
outputs. It provides formal privacy guarantees unlike the other options which are data storage
or security techniques that don't directly protect against inference attacks on model outputs.
Question #2

CASE STUDYA company is considering the procurement of an AI system designed to enhance thesecurity of IT infrastructure. The AI system analyzes how users type on their laptops,including typing speed, rhythm and pressure, to create a unique user profile. This data isthen used to authenticate users and ensure that only authorized personnel can accesssensitive resources.The data processed by the AI system would be classified as:

  • A. Non-sensitive personal data, since it does not reveal information about health, gender or race
  • B. Organizational data, since it is part of the authentication process 
  • C. Non-personal data, as long as it is not linked to a user ID 
  • D. Special category data, if it can be used to uniquely identify a person 
Answer: D
Explanation:
Keystroke dynamics including typing speed, rhythm and pressure are behavioral biometric
data. Under GDPR biometric data processed for the purpose of uniquely identifying a
natural person qualifies as special category data which requires a higher level of protection
and specific legal basis for processing.
Question #3

What is the technique to remove the effects of improperly used data from an ML system? 

  • A. Data cleansing. 
  • B. Model inversion. 
  • C. Data de-duplication. 
  • D. Model disgorgement. 
Answer: D
Explanation:
Model disgorgement is the process of purging the influence of specific improperly used data
from a trained model. Unlike data cleansing which happens before training, disgorgement is
applied after the fact when it is discovered that data was used without proper authorization
or consent. It is particularly relevant in legal and regulatory contexts where organizations are
required to undo the effects of unlawfully processed data.
Question #4

Which of the following is an example of a high-risk application under the EU Al Act? 

  • A. A resume scanning tool that ranks applicants. 
  • A. A resume scanning tool that ranks applicants. 
  • C. A government-run social scoring tool. 
  • D. A customer service chatbot tool. 
Answer: A
Explanation:
Under the EU AI Act, high-risk AI systems include those used in employment and recruitment
decisions, such as:
? Screening job applicants
? Ranking candidates
? Supporting hiring decisions
So:
? A. Resume scanning tool that ranks applicants ? ? High-risk (employment context)
? B. Inventory management tool ? Not high-risk (operational use)
? C. Government social scoring tool ? ?This is actually prohibited under the EU AI Act, not
just high-risk 
? D. Customer service chatbot ? Low-risk / minimal risk
Therefore, the correct example of a high-risk application is A.
Question #5

Scenario:An organization is planning to deploy a new internal application that uses AI to makeautomated decisions about individuals. This application will process personal informationand may affect individuals’ access to certain benefits or opportunities.Which of the following documents must be updated to ensure transparency?

  • A. The organization's website privacy notice 
  • B. The organization's acceptable use policy 
  • C. The organization's privacy policy 
  • D. The user privacy notice 
Answer: D
Explanation:
When an AI system makes automated decisions affecting individuals those individuals must
be directly informed through the user privacy notice. This is the document that
communicates to affected individuals how their data is being used and that automated
decision making is taking place. Updating internal policies or the website privacy notice
alone does not fulfill the direct transparency obligation to the individuals being impacted.
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