Free Authentic IAPP AIGP Actual Exam Questions
Dumps Box (DumpsBox) offers up-to-date practice exam questions for AIGP certification exam which are developed and validated by IAPP subject domain experts certified in Authentic IAPP AIGP . These practice questions are update regularly as we keep an eye on any recent changes in AIGP syllabus, and when there is update our team quickly adjusts the questions. This commitment to providing the best quality exam prep material to certification aspirants is what makes DumpsBox.com the best certification exam prep website. On top of that, our strong, yet strictly moderated, community based feedback keeps the content clean and current. Each question has helpful community discussion that provides it extra perspective and introduces helpful resources for better exam preparation. This also saves students from other outdated practice questions or illicit exam dumps that can have adverse affects on career. Browse through our Authentic IAPP AIGP exam questions and pass your exam on first try.
C, social scoring is the only one explicitly prohibited by the EU AI Act.
Maybe A, detecting intent seems more invasive and less allowed than managing borders.
Science and Industry be notified about a high-impact Al system?
It’s B, because notification should follow the impact assessment completion.
Makes sense that notification happens before any harm, so A feels too late. Notifying at deployment (D) is proactive and ensures the Minister’s aware from the start. I’d go with D.
A global marketing agency is adapting a large language model ("LLM") to generate content for an
upcoming marketing campaign for a client's new product: a hard hat designed for construction
workers of any gender to better protect them from head injuries.
The marketing agency is accessing the LLM through an application programming interface ("API")
developed by a third-party technology company. They want to generate text to be used for targeted
advertising communications that highlight the benefits of the hard hat to potential purchasers. Both
the marketing agency and the technology company have taken reasonable steps to address Al
governance.
The marketing company has:
• Entered into a contract with the technology company with suitable representations and
warranties.
• Completed an impact assessment on the LLM for this intended use.
• Built technical guidance on how to measure and mitigate bias in the LLM.
• Enabled technical aspects of transparency, explainability, robustness and privacy.
• Followed applicable regulatory requirements.
• Created specific legal statements and disclosures regarding the use of the Al on its client's
advertising.
The technology company has:
• Provided guidance and resources to developers to address environmental concerns.
• Build technical guidance on how to measure and mitigate bias in the LLM.
• Provided tools and resources to measure bias specific to the LLM.
• Enabled technical aspects of transparency, explainability, robustness and privacy.
• Mapped and mitigated potential societal harms and large-scale impacts.
• Followed applicable regulatory requirements and industry standards.
• Created specific legal statements and disclosures regarding the LLM. including with respect to
IP and rights to data.
The technology company has also addressed environmental concerns and societal harms.
Which of the following results would be considered biased outputs from this AI system EXCEPT?
A fits best since it’s about ad distribution, not biased AI-generated content.
Maybe A, since sending ads to companies isn’t really about biased AI content.
production?
D, it’s about meeting legal rules, not just usability or design.
Option D
Please use the following answer the next question:
A local police department in the United States procured an Al system to monitor and analyze social
media feeds, online marketplaces and other sources of public information to detect evidence of
illegal activities (e.g., sale of drugs or stolen goods). The Al system works by surveilling the public
sites in order to identify individuals that are likely to have committed a crime. It cross-references the
individuals against data maintained by law enforcement and then assigns a percentage score of the
likelihood of criminal activity based 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 their system'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 and based 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 the
implementation, 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 it into their
investigation process.
The police department has now been using the Al system for a year. An internal review has found
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 a crime. Based on
these results, the police department wants to forego investigations for cases where the Al system
gives a score of at least 90% and proceed directly with an arrest.
During the procurement process, what is the most likely reason that the third-party consultant asked
each vendor for information about the diversity of their datasets?
C imo, because having diverse datasets helps ensure the AI system performs consistently across different situations, which is crucial for its reliability, especially in law enforcement contexts.
B imo, diversity mainly targets fairness and bias reduction in AI.
Please use the following answer the next question:
A mid-size US healthcare network has decided to develop an Al solution to detect a type of cancer
that is most likely arise in adults. Specifically, the healthcare network intends to create a recognition
algorithm that will perform an initial review of all imaging and then route records a radiologist for
secondary review pursuant Agreed-upon criteria (e.g., a confidence score below a threshold).
To date, the healthcare network has taken the following steps: defined its Al ethical principles:
conducted discovery to identify the intended uses and success criteria for the system: established an
Al governance committee; assembled a broad, crossfunctional team with clear roles
andresponsibilities; and created policies and procedures to document standards, workflows,
timelines and risk thresholds during the project.
The healthcare network intends to retain a cloud provider to host the solution and a consulting firm
to help develop the algorithm using the healthcare network's existing data and de-identified data
that is licensed from a large US clinical research partner.
Which of the following steps can best mitigate the possibility of discrimination prior to training and
testing the Al solution?
A vs C? More diverse data could reduce bias before training, not just assess impact.
Option C makes sense since an impact assessment can help flag potential discrimination risks in the data and use case before any model training, letting them address issues proactively.
A, because the core role is to teach the model patterns from the data.
It’s D in a way because training data should reflect the same structure and stats as real-world data to make sure the model actually performs well when deployed. If the data’s too different from production, the model might learn irrelevant stuff or fail to generalize. So while A talks about learning patterns, without resembling production data (D), those patterns might not be useful. Training data’s quality isn’t just about what it teaches but how representative it is of what the model will see later on.
A company is considering the procurement of an AI system designed to enhance the security 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 is then used to authenticate users and
ensure that only authorized personnel can access sensitive resources.
When prioritizing the updates to its policies, rules and procedures to include the new AI system for
user authentication, the organization should:
C imo, because ensuring lawful processing covers the core privacy concern here.
Not B, because while security controls are important, the main focus should be on ensuring the biometric data is processed lawfully and specifically, which makes C the better choice here.
An organization is evaluating different AI models for integration into its internal workflows. Before
moving forward with a particular AI solution from a third-party vendor, the governance team needs
to assess the ethical and operational implications of the model.
The most important policy to assess the operations of an AI model is to follow the:
Maybe A, as it directly controls what’s allowed with the AI in operations.
A vs D? Not sure if ethical concerns fall under code of conduct or acceptable use.
Not B, since the project wasn’t about societal impacts but about defining AI as a new research area; D fits better because it’s recognized as the kickoff meeting for the AI field itself.
B tbh doesn’t really fit since the project wasn’t about broad tech impacts on society, and A is off because it wasn’t about computers launching commercially. C is tempting because of the machine intelligence aspect, but the key thing about the Dartmouth event is that it’s considered the birthplace of AI as a field. So D stands out as the main point—it was that famous meeting where AI was officially named and set up as a research discipline.
measures to promote the responsible use of Al EXCEPT?
Not A, because defining human involvement is a core part of responsible AI use.
Makes sense to exclude C since human-over-the-loop isn’t really a recognized approach in this framework. The others all align better with responsible AI governance. So, C it is.
decision-making assist with the opening of bank accounts. The platform has been subject to
thorough risk assessments and testing, where it proves to be effective in not discriminating against
any individual on the basis of a protected class.
What additional obligations must the bank fulfill prior to deployment?
D imo, since GDPR requires transparency and contest rights regardless of bias tests.
Maybe D. Even though the AI passed fairness tests, GDPR still demands transparency about automated decision-making and lets people contest decisions, especially if they’re fully automated. The other options don’t fit as well—A talks about explicit consent, but that’s more for data processing not automated decisions. B and C seem unrelated to this scenario. So disclosing the use and giving users a way to challenge sounds like the necessary step here before deployment.
automated decision-making, controllers are required to provide data subjects specific information
on?
It’s A, since the law highlights clarity on decisions, not officer contacts or full data specifics.
Option A looks right because the GDPR stresses informing people about automated decisions and their effects, not the full dataset or impact assessments. B feels too detailed about inferences, which isn't strictly required.
A global marketing agency is adapting a large language model ("LLM") to generate content for an
upcoming marketing campaign for a client's new product: a hard hat designed for construction
workers of any gender to better protect them from head injuries.
The marketing agency is accessing the LLM through an application programming interface
("API")developed by a third-party technology company. They want to generate text to be used for
targeted advertising communications that highlight the benefits of the hard hat to potential
purchasers. Both the marketing agency and the technology company have taken reasonable steps to
address Al governance.
The marketing company has:
• Entered into a contract with the technology company with suitable representations and
warranties.
• Completed an impact assessment on the LLM for this intended use.
• Built technical guidance on how to measure and mitigate bias in the LLM.
• Enabled technical aspects of transparency, explainability, robustness and privacy.
• Followed applicable regulatory requirements.
• Created specific legal statements and disclosures regarding the use of the Al on its client's
advertising.
The technology company has:
• Provided guidance and resources to developers to address environmental concerns.
• Build technical guidance on how to measure and mitigate bias in the LLM.
• Provided tools and resources to measure bias specific to the LLM.
• Enabled technical aspects of transparency, explainability, robustness and privacy.
• Mapped and mitigated potential societal harms and large-scale impacts.
• Followed applicable regulatory requirements and industry standards.
• Created specific legal statements and disclosures regarding the LLM. including with respect to
IP and rights to data.
The agency has taken governance actions such as:
Conducting an impact assessment
Providing legal disclosures
Enabling bias mitigation and explainability
Complying with regulatory requirements
Which of the following should be included in the marketing company’s disclosures about the use of
the LLM EXCEPT?
It’s B since proprietary methods are usually confidential, not for public disclosure.
B vs D? Sharing proprietary methods doesn’t seem right since it’s likely confidential. Acknowledging limitations helps manage expectations, so that should stay in the disclosures.
deployed a high-risk Al system?
C imo. The AI Act focuses more on transparency about AI use and decision rights, not explicit data storage locations—that's mostly GDPR stuff. So disclosing where data’s stored isn't a clear AI Act must-have.
C/D? The AI Act stresses transparency about AI use and oversight (A, B, D), but I don’t recall it requiring explicit data storage location disclosure—that feels more like GDPR territory.