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IAPP AIGP Exam Questions

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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.
Question #6

CASE STUDYPlease use the following answer the next question:A local police department in the United States procured an Al system to monitor andanalyze social media feeds, online marketplaces and other sources of public information todetect evidence of illegal activities (e.g., sale of drugs or stolen goods). The Al systemworks by surveilling the publicsites in order to identify individuals that are likely to havecommitted a crime. It cross-references the individuals against data maintained by lawenforcement and then assigns a percentage score of the likelihood of criminal activitybased on certain factors like previous criminal history, location, time, race and gender.The police department retained a third-party consultant assist in the procurement process,specifically to evaluate two finalists. Each of the vendors provided information about theirsystem's accuracy rates, the diversity of their training data and how their system works.The consultant determined that the first vendor’s system has a higher accuracy rate andbased on this information, recommended this vendor to the police department.The police department chose the first vendor and implemented its Al system. As part of theimplementation, the department and consultant created a usage policy for the system,which includes training police officers on how the system works and how to incorporate itinto their investigation process.The police department has now been using the Al system for a year. An internal review hasfound that every time the system scored a likelihood of criminal activity at or above 90%,the police investigation subsequently confirmed that the individual had, in fact, committed acrime. Based on these results, the police department wants to forego investigations forcases where the Al system gives a score of at least 90% and proceed directly with anarrest.When notifying an accused perpetrator, what additional information should a police officerprovide about the use of the Al system?

  • A. Information about the accuracy of the Al system. 
  • B. Information about how the accused can oppose the charges. 
  • C. Information about the composition of the training data of the system. 
  • D. Information about how the individual was identified by the Al system. 
Answer: D
Explanation:
When an AI system is used to identify someone as a suspect the accused has a right to
understand how that identification was made. Explaining how the AI flagged them is
essential for due process and allows the individual to meaningfully contest the decision.
Accuracy rates and training data composition are technical details that go beyond what is
necessary for the accused to understand their situation and challenge the charges.
Question #7

Scenario:A European AI technology company was found to be non-compliant with certain provisionsof the EU AI Act. The regulator is considering penalties under the enforcement provisionsof the regulation.According to the EU AI Act, which of the following non-compliance examples could lead tofines of up to €15 million or 3% of annual worldwide turnover(whichever is higher)?

  • A. In case of AI Act prohibitions 
  • B. In case of breach of a provider's obligations for high-risk AI systems 
  • C. In case of the supply of misleading information to notified bodies in reply to a request 
  • D. In case of a breach of AI Act prohibition by the Union institutions, bodies, offices and agencies
Answer: B
Explanation:
Under the EU AI Act the fine tiers are structured as follows: €35 million or 7% for prohibited
AI practices, €15 million or 3% for breaches of provider obligations for high-risk AI systems,
and €7.5 million or 1.5% for supplying misleading information. So a breach of high-risk AI
provider obligations correctly corresponds to the €15 million or 3% tier.
Question #8

What is the main purpose of accountability structures under the Govern function of theNIST Al Risk Management Framework?

  • A. To empower and train appropriate cross-functional teams. 
  • B. To establish diverse, equitable and inclusive processes. 
  • C. To determine responsibility for allocating budgetary resources. 
  • D. To enable and encourage participation by external stakeholders. 
Answer: A
Explanation:
Under the NIST AI RMF Govern function accountability structures are primarily designed to ensure
the right people with the right roles and training are empowered to manage AI risks across the
organization. This includes defining who is responsible for what and equipping those teams to fulfill
their governance responsibilities.
Question #9

You are part of your organization’s ML engineering team and notice that the accuracy of amodel that was recently deployed into production is deteriorating.What is the best first step address this?

  • A. Replace the model with a previous version. 
  • B. Conduct champion/challenger testing. 
  • C. Perform an audit of the model. 
  • D. Run red-teaming exercises. 
Answer: B
Explanation:
When a deployed model starts deteriorating the best first step is champion/challenger
testing which compares the current model against an alternative to identify whether a better
performing version exists and what is causing the degradation. Replacing with a previous 
version is premature without testing, an audit is too broad as a first step and red-teaming
focuses on adversarial threats not performance decline.
Question #10

During the planning and design phases of the Al development life cycle, bias can bereduced by all of the following EXCEPT?

  • A. Stakeholder involvement. 
  • B. Feature selection. 
  • C. Human oversight. 
  • D. Data collection. 
Answer: B
Explanation:
Human oversight is an important governance control but it is primarily a deployment and
monitoring measure rather than a planning and design phase activity for reducing bias.
Feature selection, stakeholder involvement and data collection are all direct actions taken
during planning and design that directly influence whether bias gets built into the model from
the start.
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