[Nov 09, 2024] Fully Updated Free Actual IAPP AIGP Exam Questions
Free AIGP Questions for IAPP AIGP Exam [Nov-2024]
IAPP AIGP Exam Syllabus Topics:
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NEW QUESTION # 24
The OECD's Ethical Al Governance Framework is a self-regulation model that proposes to prevent societal harms by?
- A. Establishing explain ability criteria to responsibly source and use data to train Al systems.
- B. Focusing on Al technical design and post-deployment monitoring.
- C. Balancing Al innovation with ethical considerations.
- D. Defining requirements specific to each industry sector and high-risk Al domain.
Answer: C
Explanation:
The OECD's Ethical AI Governance Framework aims to ensure that AI development and deployment are carried out ethically while fostering innovation. The framework includes principles like transparency, accountability, and human rights protections to prevent societal harm. It does not focus solely on technical design or post-deployment monitoring (C), nor does it establish industry-specific requirements (B). While explainability is important, the primary goal is to balance innovation with ethical considerations (D).
NEW QUESTION # 25
According to the Singapore Model Al Governance Framework, all of the following are recommended measures to promote the responsible use of Al EXCEPT?
- A. Employing human-over-the-loop protocols for high-risk systems.
- B. Establishing communications and collaboration among stakeholders.
- C. Determining the level of human involvement in algorithmic decision-making.
- D. Adapting the existing governance structure algorithmic decision-making.
Answer: A
Explanation:
The Singapore Model AI Governance Framework recommends several measures to promote the responsible use of AI, such as determining the level of human involvement in decision-making, adapting governance structures, and establishing communications and collaboration among stakeholders. However, employing human-over-the-loop protocols is not specifically mentioned in this framework. The focus is more on integrating human oversight appropriately within the decision-making process rather than exclusively employing such protocols. Reference: AIGP Body of Knowledge, section on AI governance frameworks.
NEW QUESTION # 26
Random forest algorithms are in what type of machine learning model?
- A. Symbolic.
- B. Natural language processing.
- C. Generative.
- D. Discriminative.
Answer: D
Explanation:
Random forest algorithms are classified as discriminative models. Discriminative models are used to classify data by learning the boundaries between classes, which is the core functionality of random forest algorithms.
They are used for classification and regression tasks by aggregating the results of multiple decision trees to make accurate predictions.
Reference: The AIGP Body of Knowledge explains that discriminative models, including random forest algorithms, are designed to distinguish between different classes in the data, making them effective for various predictive modeling tasks.
NEW QUESTION # 27
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.
Which of the following measures should XYZ adopt to best mitigate its risk of reputational harm from using the Al tool?
- A. Test the Al tool pre- and post-deployment.
- B. Ensure the vendor assumes responsibility for all damages.
- C. Continue to require XYZ's hiring personnel to manually screen all applicants.
- D. Direct the procurement team to select the most economical Al tool.
Answer: A
Explanation:
To mitigate the risk of reputational harm from using an AI hiring tool, XYZ Corp should rigorously test the AI tool both before and after deployment. Pre-deployment testing ensures the tool works correctly and does not introduce bias or other issues. Post-deployment testing ensures the tool continues to operate as intended and adapts to any changes in data or usage patterns. This approach helps to identify and address potential issues proactively, thereby reducing the risk of reputational harm. Ensuring the vendor assumes responsibility for damages (B) does not address the root cause of potential issues, selecting the most economical tool (C) may compromise quality, and continuing manual screening (D) defeats the purpose of using the AI tool.
NEW QUESTION # 28
What is the key feature of Graphical Processing Units (GPUs) that makes them well-suited to running Al applications?
- A. GPUs run many tasks concurrently, resulting in faster processing.
- B. The number of transistors on GPUs doubles every two years, making thechips smaller and lighter.
- C. GPUs can run every task on a computer, making them more robust than CPUs.
- D. GPUs can access memory quickly, resulting in lower latency than CPUs.
Answer: A
Explanation:
GPUs (Graphical Processing Units) are well-suited to running AI applications due to their ability to run many tasks concurrently, which significantly enhances processing speed. This parallel processing capability makes GPUs ideal for handling the large-scale computations required in AI and deep learning tasks. Reference: AIGP BODY OF KNOWLEDGE, which explains the importance of compute infrastructure in AI applications.
NEW QUESTION # 29
When monitoring the functional performance of a model that has been deployed into production, all of the following are concerns EXCEPT?
- A. Data loss.
- B. Model drift.
- C. Feature drift.
- D. System cost.
Answer: D
Explanation:
When monitoring the functional performance of a model deployed into production, concerns typically include feature drift, model drift, and data loss. Feature drift refers to changes in the input features that can affect the model's predictions. Model drift is when the model's performance degrades over time due to changes in the data or environment. Data loss can impact the accuracy and reliability of the model. However, system cost, while important for budgeting and financial planning, is not a direct concern when monitoring the functional performance of a deployed model. Reference: AIGP Body of Knowledge on Model Monitoring and Maintenance.
NEW QUESTION # 30
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 tA. 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.
The best approach to enable a customer who wants information on the Al model's parameters for underwriting purposes is to provide?
- A. Detailed terms of service.
- B. Customer service support.
- C. A transparency notice.
- D. An opt-out mechanism.
Answer: C
Explanation:
The best approach to enable a customer who wants information on the AI model's parameters for underwriting purposes is to provide a transparency notice. This notice should explain the nature of the AI system, how it uses customer data, and the decision-making process it follows. Providing a transparency notice is crucial for maintaining trust and compliance with regulatory requirements regarding the transparency and accountability of AI systems.
Reference: According to the AIGP Body of Knowledge, transparency in AI systems is essential to ensure that stakeholders, including customers, understand how their data is being used and how decisions are made. This aligns with ethical principles of AI governance, ensuring that customers are informed and can make knowledgeable decisions regarding their interactions with AI systems.
NEW QUESTION # 31
In the machine learning context, feature engineering is the process of?
- A. Converting raw data into clean data.
- B. Extracting attributes and variables from raw data.
- C. Creating learning schema for a model apply.
- D. Developing guidelines to train and test a model.
Answer: B
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 # 32
Each of the following actors are typically engaged in the Al development life cycle EXCEPT?
- A. Legal and privacy governance experts.
- B. Socio-cultural and technical experts.
- C. Data architects.
- D. Government regulators.
Answer: D
Explanation:
Typically, actors involved in the AI development life cycle include data architects (who design the data frameworks), socio-cultural and technical experts (who ensure the AI system is socio-culturally aware and technically sound), and legal and privacy governance experts (who handle the legal and privacy aspects).
Government regulators, while important, are not directly engaged in the development process but rather oversee and regulate the industry. Reference: AIGP BODY OF KNOWLEDGE and AI development frameworks.
NEW QUESTION # 33
Retraining an LLM can be necessary for all of the following reasons EXCEPT?
- A. To minimize degradation in prediction accuracy due tochanges in data.
- B. Adjust the model's hyper parameters specific use case.
- C. To ensure interpretability of the model's predictions.
- D. Account for new interpretations of the same data.
Answer: C
Explanation:
Retraining an LLM (Large Language Model) is primarily done to improve or maintain its performance as data changes over time, to fine-tune it for specific use cases, and to incorporate new data interpretations to enhance accuracy and relevance. However, ensuring interpretability of the model's predictions is not typically a reason for retraining. Interpretability relates to how easily the outputs of the model can be understood and explained, which is generally addressed through different techniques or methods rather than through the retraining process itself. References to this can be found in the IAPP AIGP Body of Knowledge discussing model retraining and interpretability as separate concepts.
NEW QUESTION # 34
Which of the following Al uses is best described as human-centric?
- A. Machine learning is used for demand forecasting and inventory management, ensuring that consumers can find products they want when they want them.
- B. Pattern recognition algorithms are used to improve the accuracy of weather predictions, which benefits many industries and everyday life.
- C. Virtual assistants are used adapt educational content and teaching methods to individuals, offering personalized recommendations based on ability and needs.
- D. Autonomous robots are used to move products within a warehouse, allowing human workers to reduce physical strain and alleviate monotony.
Answer: C
Explanation:
Human-centric AI focuses on improving the human experience by addressing individual needs and enhancing human capabilities. Option D exemplifies this by using virtual assistants to tailor educational content to each student's unique abilities and needs, thereby supporting personalized learning and improving educational outcomes. This use case directly benefits individuals by providing customized assistance and adapting to their learning pace and style, aligning with the principles of human-centric AI.
Reference: AIGP BODY OF KNOWLEDGE, sections on trustworthy AI and human-centric AI principles.
NEW QUESTION # 35
Testing data is defined as a subset of data that is used to?
- A. Enable a model to discover and learn patterns.
- B. Provide a robust evaluation of a final model.
- C. Assess a model's on-going performance in production.
- D. Evaluate a model's handling of randomized edge cases.
Answer: B
Explanation:
Testing data is a subset of data used to provide a robust evaluation of a final model. After training the model on training data, it is essential to test its performance on unseen data (testing data) to ensure it generalizes well to new, real-world scenarios. This step helps in assessing the model's accuracy, reliability, and ability to handle various data inputs. Reference: AIGP Body of Knowledge on Model Validation and Testing.
NEW QUESTION # 36
Which of the following use cases would be best served by a non-AI solution?
- A. A business analyst wants to forecast future cost overruns and underruns.
- B. A customer service agency wants automate answers to common questions.
- C. A non-profit wants to develop a social media presence.
OB. An e-commerce provider wants to make personalized recommendations.
Answer: C
Explanation:
Developing a social media presence for a non-profit is best served by non-AI solutions. This task primarily involves content creation, community engagement, and strategic planning, which are effectively managed by human expertise and traditional marketing tools. AI is more suitable for tasks requiring automation, large-scale data analysis, and personalized recommendations, such as e-commerce personalization, forecasting cost overruns, or automating customer service responses. Reference: AIGP Body of Knowledge on AI Use Cases and Applications.
NEW QUESTION # 37
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. NIST Cyber Security Risk Management Framework (CSF 2.0).
- B. Human Rights, Democracy, and Rule of Law Impact Assessment (HUDERIA).
- C. NIST Information Security Risk (NIST SP 800-39).
- D. IEEE Ethical System Design Risk Management Framework (IEEE 7000-21).
Answer: D
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 # 38
A US company has developed an Al system, CrimeBuster 9619, that collects information about incarcerated individuals to help parole boards predict whether someone is likely to commit another crime if released from prison.
When considering expanding to the EU market, this type of technology would?
- A. Require the company to register the tool with the EU database.
- B. Be banned under the EU Al Act.
- C. Require a detailed conformity assessment.
- D. Be subject approval by the relevant EU authority.
Answer: C
Explanation:
Under the EU AI Act, high-risk AI systems like CrimeBuster 9619 would require a detailed conformity assessment before being deployed in the EU market. This assessment ensures that the AI system complies with all relevant regulations and standards, addressing potential risks related to privacy, security, and discrimination. The company would not need to register the tool with the EU database (A), seek approval from an EU authority (B), or face a ban (D) as long as it meets the necessary conformity requirements.
NEW QUESTION # 39
Which type of existing assessment could best be leveraged to create an Al impact assessment?
- A. An environmental impact assessment.
- B. A safety impact assessment.
- C. A security impact assessment.
- D. A privacy impact assessment.
Answer: D
Explanation:
A privacy impact assessment (PIA) can be effectively leveraged to create an AI impact assessment. A PIA evaluates the potential privacy risks associated with the use of personal data and helps in implementing measures to mitigate those risks. Since AI systems often involve processing large amounts of personal data, the principles and methodologies of a PIA are highly applicable and can be extended to assess broader impacts, including ethical, social, and legal implications of AI. Reference: AIGP Body of Knowledge on Impact Assessments.
NEW QUESTION # 40
You asked a generative Al tool to recommend new restaurants to explore in Boston, Massachusetts that have a specialty Italian dish made in a traditional fashion without spinach and wine. The generative Al tool recommended five restaurants for you to visit.
After looking up the restaurants, you discovered one restaurant did not exist and two others did not have the dish.
This information provided by the generative Al tool is an example of what is commonly called?
- A. Hallucination.
- B. Prompt injection.
- C. Model collapse.
- D. Overfitting.
Answer: A
Explanation:
In the context of AI, particularly generative models, "hallucination" refers to the generation of outputs that are not based on the training data and are factually incorrect or non-existent. The scenario described involves the generative AI tool providing incorrect and non-existent information about restaurants, which fits the definition of hallucination. Reference: AIGP BODY OF KNOWLEDGE and various AI literature discussing the limitations and challenges of generative AI models.
NEW QUESTION # 41
You are the chief privacy officer of a medical research company that would like to collect and use sensitive data about cancer patients, such as their names, addresses, race and ethnic origin, medical histories, insurance claims, pharmaceutical prescriptions, eating and drinking habits and physical activity.
The company will use this sensitive data to build an Al algorithm that will spot common attributes that will help predict if seemingly healthy people are more likely to get cancer. However, the company is unable to obtain consent from enough patients to sufficiently collect the minimum data to train its model.
Which of the following solutions would most efficiently balance privacy concerns with the lack of available data during the testing phase?
- A. Refocus the algorithm to patients without cancer.
- B. Extend the model to multi-modal ingestion with text and images.
- C. Utilize synthetic data to offset the lack of patient data.
- D. Deploy the current model and recalibrate it over time with more data.
Answer: C
Explanation:
Utilizing synthetic data to offset the lack of patient data is an efficient solution that balances privacy concerns with the need for sufficient data to train the model. Synthetic data can be generated to simulate real patient data while avoiding the privacy issues associated with using actual patient data. This approach allows for the development and testing of the AI algorithm without compromising patient privacy, and it can be refined with real data as it becomes available. Reference: AIGP Body of Knowledge on Data Privacy and AI Model Training.
NEW QUESTION # 42
The framework set forth in the White House Blueprint for an Al Bill of Rights addresses all of the following EXCEPT?
- A. Data privacy.
- B. High-risk mitigation standards.
- C. Safe and effective systems.
- D. Human alternatives, consideration and fallback.
Answer: B
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 # 43
What is the primary purpose of an Al impact assessment?
- A. To define and evaluate the legal risks associated with developing an Al system.
- B. Anticipate and manage the potential risks and harms of an Al system.
- C. To identify and measure the benefits of an Al system.
- D. To define and document the roles and responsibilities of Al stakeholders.
Answer: B
Explanation:
The primary purpose of an AI impact assessment is to anticipate and manage the potential risks and harms of an AI system. This includes identifying the possible negative outcomes and implementing measures to mitigate these risks. This process helps ensure that AI systems are developed and deployed in a manner that is ethically and socially responsible, addressing concerns such as bias, fairness, transparency, and accountability.
The assessment often involves a thorough evaluation of the AI system's design, data inputs, outputs, and the potential impact on various stakeholders. This approach is crucial for maintaining public trust and adherence to regulatory requirements.
NEW QUESTION # 44
Which of the following is NOT a common type of machine learning?
- A. Reinforcement learning.
- B. Deep learning.
- C. Cognitive learning.
- D. Unsupervised learning.
Answer: C
Explanation:
The common types of machine learning include supervised learning, unsupervised learning, reinforcement learning, and deep learning. Cognitive learning is not a type of machine learning; rather, it is a term often associated with the broader field of cognitive science and psychology. Reference: AIGP BODY OF KNOWLEDGE and standard AI/ML literature.
NEW QUESTION # 45
All of the following may be permissible uses of an Al system under the EU Al Act EXCEPT?
- A. To promote equitable distribution of welfare benefits.
- B. To implement social scoring.
- C. To manage border control.
- D. To detect an individual's intent for law enforcement purposes.
Answer: B
Explanation:
The EU AI Act explicitly prohibits the use of AI systems for social scoring by public authorities, as it can lead to discrimination and unfair treatment of individuals based on their social behavior or perceived trustworthiness. While AI can be used to promote equitable distribution of welfare benefits, manage border control, and even detect an individual's intent for law enforcement purposes (within strict regulatory and ethical boundaries), implementing social scoring systems is not permissible under the Act due to the significant risks to fundamental rights and freedoms.
NEW QUESTION # 46
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