A fully updated 2026 AIGP Exam Dumps exam guide from training expert DumpsReview
Provides complete coverage of every objective on exam and exam preparation AIGP
IAPP AIGP Exam Syllabus Topics:
| Topic | Details |
|---|---|
| Topic 1 |
|
| Topic 2 |
|
| Topic 3 |
|
| Topic 4 |
|
NEW QUESTION # 17
CASE STUDY
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.
Which stakeholder is responsible for the lawful collection of data used to train the foundational AI model?
- A. The data aggregator
- B. The marketing agency's client
- C. The marketing agency
- D. The tech company
Answer: D
Explanation:
The correct answer is B - The tech company. The party that develops and trains the foundational model is responsible for ensuring the lawful collection of training data.
From the AIGP ILT Guide - Foundational Models & Data Governance:
"Responsibility for the lawfulness of data collection typically lies with the party that trains the model- usually the provider or developer of the foundational model." AI Governance in Practice Report 2024 confirms:
"General Purpose AI providers are required to ensure that training data is lawfully acquired, including compliance with intellectual property and privacy requirements." The marketing agency is only a user or downstream integrator, not responsible for original data collection.
NEW QUESTION # 18
According to the GDPR's transparency principle, when an AI system processes personal data in automated decision-making, controllers are required to provide data subjects specific information on?
- A. The existence of automated decision-making and meaningful information on its logic and consequences.
- B. The contact details of the data protection officer and the data protection national authority.
- C. The personal data used during processing, including inferences drawn by the AI system about the data.
- D. The data protection impact assessments carried out on the AI system and legal bases for processing.
Answer: A
Explanation:
GDPR's transparency principle mandates that data subjects be informed about the existence of automated decision-making, along with meaningful information about the logic involved and the potential consequences.
NEW QUESTION # 19
In the machine learning context, feature engineering is the process of?
- A. Converting raw data into clean data.
- B. Developing guidelines to train and test a model.
- C. Creating learning schema for a model apply.
- D. Extracting attributes and variables from raw data.
Answer: D
Explanation:
In the machine learning context, feature engineering is the process of extracting attributes and variables from raw data to make it suitable for training an AI model. This step is crucial as it transforms raw data into meaningful features that can improve the model's accuracy and performance. Feature engineering involves selecting, modifying, and creating new features that help the model learn more effectively. Reference: AIGP Body of Knowledge on AI Model Development and Feature Engineering.
NEW QUESTION # 20
A leading software development company wants to integrate AI-powered chatbots into their customer service platform. After researching various AI models in the market which have been developed by third party developers, they're considering two options:
Option A - an open-source language model trained on a vast corpus of text data and capable of being trained to respond to natural language inputs.
Option B - a proprietary, generative AI model pre-trained on large data sets, which uses transformer-based architectures to generate human-like responses based on multimodal user input.
Option A would be the best choice for the company because:
- A. It is less expensive to run.
- B. It can handle voice commands and is more suitable for phone-based customer support.
- C. It is built for large-scale, complex dialogues and would be more effective in handling high-volume customer inquiries.
- D. It may be better suited for applications requiring customization.
Answer: D
Explanation:
Open-source models can be customized extensively, making them better suited for applications requiring specific adaptations.
NEW QUESTION # 21
CASE STUDY
Please use the following answer the next question:
ABC Corp, is a leading insurance provider offering a range of coverage options to individuals. ABC has decided to utilize artificial intelligence to streamline and improve its customer acquisition and underwriting process, including the accuracy and efficiency of pricing policies.
ABC has engaged a cloud provider to utilize and fine-tune its pre-trained, general purpose large language model ("LLM"). In particular, ABC intends to use its historical customer data-including applications, policies, and claims-and proprietary pricing and risk strategies to provide an initial qualification assessment of potential customers, which would then be routed .. human underwriter for final review.
ABC and the cloud provider have completed training and testing the LLM, performed a readiness assessment, and made the decision to deploy the LLM into production. ABC has designated an internal compliance team to monitor the model during the first month, specifically to evaluate the accuracy, fairness, and reliability of its output. After the first month in production, ABC realizes that the LLM declines a higher percentage of women's loan applications due primarily to women historically receiving lower salaries than men.
During the first month when ABC monitors the model for bias, it is most important to?
- A. Continue disparity testing.
- B. Seek approval from management for any changes to the model.
- C. Analyze the quality of the training and testing data.
- D. Compare the results to human decisions prior to deployment.
Answer: A
Explanation:
During the first month of monitoring the model for bias, it is most important to continue disparity testing.
Disparity testing involves regularly evaluating the model's decisions to identify and address any biases, ensuring that the model operates fairly across different demographic groups.
Reference: Regular disparity testing is highlighted in the AIGP Body of Knowledge as a critical practice for maintaining the fairness and reliability of AI models. By continuously monitoring for and addressing disparities, organizations can ensure their AI systems remain compliant with ethical and legal standards, and mitigate any unintended biases that may arise in production.
NEW QUESTION # 22
The White House Executive Order from November 2023 requires companies that develop dual-use foundation models to provide reports to the federal government about all of the following EXCEPT?
- A. The results of red-team testing of each dual-use foundation model.
- B. The physical and cybersecurity protection measures of their dual-use foundation models.
- C. Any current training or development of dual-use foundation models.
- D. Any environmental impact study for each dual-use foundation model.
Answer: D
Explanation:
The White House Executive Order from November 2023 requires companies developing dual-use foundation models to report on their current training or development activities, the results of red-team testing, and the physical and cybersecurity protection measures. However, it does not mandate reports on environmental impact studies for each dual-use foundation model. While environmental considerations are important, they are not specified in this context as a reporting requirement under this Executive Order.
Reference: AIGP BODY OF KNOWLEDGE, sections on compliance and reporting requirements, and the White House Executive Order of November 2023.
NEW QUESTION # 23
Machine learning is best described as a type of algorithm by which?
- A. Systems can automatically improve from experience through predictive patterns.
- B. Previously unknown properties are discovered in data and used to predict and make improvements in the data.
- C. Statistical inferences are drawn from a sample with the goal of predicting human intelligence.
- D. Systems can mimic human intelligence with the goal of performing routine tasks.
Answer: A
Explanation:
Machine learning is defined as systems that automatically improve their performance through experience by identifying predictive patterns in data.
NEW QUESTION # 24
What is the most important factor when deciding whether or not to select a proprietary AI model?
- A. What business purpose it will serve.
- B. Whether its training data is disclosed.
- C. How frequently it will be updated.
- D. Whether its system card identifies risks.
Answer: A
Explanation:
The primary consideration in selecting a proprietary AI model is whether it effectively serves the intended business purpose.
NEW QUESTION # 25
CASE STUDY
Please use the following answer the next question:
XYZ Corp., a premier payroll services company that employs thousands of people globally, is embarking on a new hiring campaign and wants to implement policies and procedures to identify and retain the best talent. The new talent will help the company's product team expand its payroll offerings to companies in the healthcare and transportation sectors, including in Asia.
It has become time consuming and expensive for HR to review all resumes, and they are concerned that human reviewers might be susceptible to bias.
Address these concerns, the company is considering using a third-party Al tool to screen resumes and assist with hiring. They have been talking to several vendors about possibly obtaining a third-party Al-enabled hiring solution, as long as it would achieve its goals and comply with all applicable laws.
The organization has a large procurement team that is responsible for the contracting of technology solutions.
One of the procurement team's goals is to reduce costs, and it often prefers lower-cost solutions. Others within the company are responsible for integrating and deploying technology solutions into the organization's operations in a responsible, cost-effective manner.
The organization is aware of the risks presented by Al hiring tools and wants to mitigate them. It also questions how best to organize and train its existing personnel to use the Al hiring tool responsibly. Their concerns are heightened by the fact that relevant laws vary across jurisdictions and continue to change.
The frameworks that would be most appropriate for XYZ's governance needs would be the NIST Al Risk Management Framework and?
- A. IEEE Ethical System Design Risk Management Framework (IEEE 7000-21).
- B. NIST Cyber Security Risk Management Framework (CSF 2.0).
- C. NIST Information Security Risk (NIST SP 800-39).
- D. Human Rights, Democracy, and Rule of Law Impact Assessment (HUDERIA).
Answer: A
Explanation:
The IEEE Ethical System Design Risk Management Framework (IEEE 7000-21) would be most appropriate for XYZ Corp's governance needs in addition to the NIST AI Risk Management Framework. The IEEE framework specifically addresses ethical concerns during system design, which is crucial for ensuring the responsible use of AI in hiring. It complements the NIST framework by focusing on ethical risk management, aligning well with XYZ Corp's goals of deploying AI responsibly and mitigating associated risks.
NEW QUESTION # 26
Which of the following deployments of generative Al best respects intellectual property rights?
- A. The system produces content that includes trademarks and copyrights.
- B. The system provides attribution to creators of publicly available information.
- C. The system produces content that is modified to closely resemble copyrightedwork.
- D. The system categorizes and applies filters to content based on licensing terms.
Answer: D
Explanation:
Respecting intellectual property rights means adhering to licensing terms and ensuring that generated content complies with these terms. A system that categorizes and applies filters based on licensing terms ensures that content is used legally and ethically, respecting the rights of content creators. While providing attribution is important, categorization and application of filters based on licensing terms are more directly tied to compliance with intellectual property laws. This principle is elaborated in the IAPP AIGP Body of Knowledge sections on intellectual property and compliance.
NEW QUESTION # 27
What is the 1956 Dartmouth summer research project on Al best known as?
- A. A research project on the impacts of technology on society.
- B. A research project to create a test for machine intelligence.
- C. A meeting focused on the impacts of the launch of the first mass-produced computer.
- D. A meeting focused on the founding of the Al field.
Answer: D
Explanation:
The 1956 Dartmouth summer research project on AI is best known as a meeting focused on the founding of the AI field. This conference is historically significant because it marked the formal beginning of artificial intelligence as an academic discipline. The term "artificial intelligence" was coined during this event, and it laid the foundation for future research and development in AI.
Reference: The AIGP Body of Knowledge highlights the importance of the Dartmouth Conference as a pivotal moment in the history of AI, which established AI as a distinct field of study and research.
NEW QUESTION # 28
What is the most significant risk of deploying an AI model that can create realistic images and videos?
- A. Copyright infringement.
- B. Security breaches.
- C. Downstream harms.
- D. Output cannot be protected.
Answer: C
Explanation:
The greatest risk from AI systems generatingrealistic synthetic mediaisdownstream harm, such asdeepfakes, misinformation, reputational damage, and erosion of trust.
From theAI Governance in Practice Report2025:
"With generative AI, downstream harms such as deception, reputational damage, misinformation, and manipulation can emerge even if original use was lawful." (p. 55-56)
NEW QUESTION # 29
The framework set forth in the White House Blueprint for an Al Bill of Rights addresses all of the following EXCEPT?
- A. Human alternatives, consideration and fallback.
- B. Data privacy.
- C. Safe and effective systems.
- D. High-risk mitigation standards.
Answer: D
Explanation:
The White House Blueprint for an AI Bill of Rights focuses on protecting civil rights, privacy, and ensuring AI systems are safe and effective. It includes principles like data privacy (D), human alternatives (A), and safe and effective systems (C). However, it does not specifically address high-risk mitigation standards as a distinct category (B).
NEW QUESTION # 30
Which risk category addresses situations where AI systems behave in ways inconsistent with their intended objectives?
- A. Vendor lock-in risk
- B. Data leakage risk
- C. Misalignment risk
- D. Availability risk
Answer: C
NEW QUESTION # 31
Which of the following disclosures is NOT required for an EU organization that developed and deployed a high-risk AI system?
- A. The fact that an AI system is being used.
- B. The human oversight measures employed.
- C. How an individual may contest a decision.
- D. The location(s) where data is stored.
Answer: D
Explanation:
While transparency about AI use, human oversight, and contesting decisions are required disclosures for high-risk AI systems under the EU AI Act, the specific locations where data is stored are not mandated disclosures.
NEW QUESTION # 32
If it is possible to provide a rationale for a specific output of an AI system, that system can best be described as:
- A. Explainable.
- B. Reliable.
- C. Transparent.
- D. Accountable.
Answer: A
Explanation:
An AI system that can provide a rationale for its specific outputs is considered explainable because it offers understandable reasons for its decisions.
NEW QUESTION # 33
......
Tested Material Used To AIGP: https://quizmaterials.dumpsreview.com/AIGP-exam-dumps-review.html
