[May 06, 2025] AIGP Exam Dumps - IAPP Practice Test Questions [Q45-Q60]

[May 06, 2025] AIGP Exam Dumps - IAPP Practice Test Questions

New Real AIGP Exam Dumps Questions

IAPP AIGP Exam Syllabus Topics:

NEW QUESTION # 45
In the machine learning context, feature engineering is the process of?

• A. Developing guidelines to train and test a model.
• B. Creating learning schema for a model apply.
• C. Converting raw data into clean data.
• 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 # 46
After completing model testing and validation, which of the following is the most important step that an organization takes prior to deploying the model into production?

• A. Define a model-validation methodology.
• B. Identify known edge cases to monitor post-deployment.
• C. Document maintenance teams and processes.
• D. Perform a readiness assessment.

Answer: D

Explanation:
After completing model testing and validation, the most important step prior to deploying the model into production is to perform a readiness assessment. This assessment ensures that the model is fully prepared for deployment, addressing any potential issues related to infrastructure, performance, security, and compliance. It verifies that the model meets all necessary criteria for a successful launch. Other steps, such as defining a model-validation methodology, documenting maintenance teams and processes, and identifying known edge cases, are also important but come secondary to confirming overall readiness. Reference: AIGP Body of Knowledge on Deployment Readiness.

NEW QUESTION # 47
Which of the following is NOT a common type of machine learning?

• A. Deep learning.
• B. Reinforcement 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 # 48
All of the following are included within the scope of post-deployment Al maintenance EXCEPT?

• A. Defining thresholds to conduct new impact assessments.
• B. Dedicating experts to continually monitor the model output.
• C. Evaluating the need for an audit under certain standards.
• D. Ensuring that all model components are subject a control framework.

Answer: A

Explanation:
Post-deployment AI maintenance typically includes ensuring that all model components are subject to a control framework, dedicating experts to continually monitor the model output, and evaluating the need for audits under certain standards. However, defining thresholds to conduct new impact assessments is usually part of the initial deployment and ongoing governance processes rather than a maintenance activity.
Maintenance focuses more on the operational aspects of the AI system rather than setting new thresholds for impact assessments.
Reference: AIGP BODY OF KNOWLEDGE, sections discussing AI lifecycle management and post-deployment activities.

NEW QUESTION # 49
Machine learning is best described as a type of algorithm by which?

• A. Systems can automatically improve from experience through predictive patterns.
• B. Systems can mimic human intelligence with the goal of replacing humans.
• C. Previously unknown properties are discovered in data and used to predict and make improvements in the data.
• D. Statistical inferences are drawn from a sample with the goal of predicting human intelligence.

Answer: A

Explanation:
Machine learning (ML) is a subset of artificial intelligence (AI) where systems use data to learn and improve over time without being explicitly programmed. Option B accurately describes machine learning by stating that systems can automatically improve from experience through predictive patterns. This aligns with the fundamental concept of ML where algorithms analyze data, recognize patterns, and make decisions with minimal human intervention. Reference: AIGP BODY OF KNOWLEDGE, which covers the basics of AI and machine learning concepts.

NEW QUESTION # 50
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. Continue to require XYZ's hiring personnel to manually screen all applicants.
• B. Direct the procurement team to select the most economical Al tool.
• C. Test the Al tool pre- and post-deployment.
• D. Ensure the vendor assumes responsibility for all damages.

Answer: C

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 # 51
Pursuant to the White House Executive Order of November 2023, who is responsible for creating guidelines to conduct red-teaming tests of Al systems?

• A. National Science and Technology Council (NSTC).
• B. Office of Science and Technology Policy (OSTP).
• C. National Institute of Standards and Technology (NIST).
• D. Department of Homeland Security (DHS).

Answer: C

Explanation:
The White House Executive Order of November 2023 designates the National Institute of Standards and Technology (NIST) as the responsible body for creating guidelines to conduct red-teaming tests of AI systems.
NIST is tasked with developing and providing standards and frameworks to ensure the security, reliability, and ethical deployment of AI systems, including conducting rigorous red-teaming exercises to identify vulnerabilities and assess risks in AI systems.
Reference: AIGP BODY OF KNOWLEDGE, sections on AI governance and regulatory frameworks, and the White House Executive Order of November 2023.

NEW QUESTION # 52
An Al system that maintains its level of performance within defined acceptable limits despite real world or adversarial conditions would be described as?

• A. Robust.
• B. Resilient.
• C. Reliable.
• D. Reinforced.

Answer: B

Explanation:
An AI system that maintains its level of performance within defined acceptable limits despite real-world or adversarial conditions is described as resilient. Resilience in AI refers to the system's ability to withstand and recover from unexpected challenges, such as cyber-attacks, hardware failures, or unusual input data. This characteristic ensures that the AI system can continue to function effectively and reliably in various conditions, maintaining performance and integrity. Robustness, on the other hand, focuses on the system's strength against errors, while reliability ensures consistent performance over time. Resilience combines these aspects with the capacity to adapt and recover.

NEW QUESTION # 53
During the development of semi-autonomous vehicles, various failures occurred as a result of the sensors misinterpreting environmental surroundings, such as sunlight.
These failures are an example of?

• A. Brittleness.
• B. Uncertainty.
• C. Forgetting.
• D. Hallucination.

Answer: A

Explanation:
The failures in semi-autonomous vehicles due to sensors misinterpreting environmental surroundings, such as sunlight, are examples of brittleness. Brittleness in AI systems refers to their inability to handle variations in input data or unexpected conditions, leading to failures when the system encounters situations that were not adequately covered during training. These systems perform well under specific conditions but fail when those conditions change. Reference: AIGP Body of Knowledge on AI System Robustness and Failures.

NEW QUESTION # 54
CASE STUDY
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 and responsibilities; 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. Engage a third party to perform an audit.
• B. Procure more data from clinical research partners.
• C. Create a bias bounty program.
• D. Perform an impact assessment.

Answer: D

Explanation:
Performing an impact assessment is the best step to mitigate the possibility of discrimination before training and testing the AI solution. An impact assessment, such as a Data Protection Impact Assessment (DPIA) or Algorithmic Impact Assessment (AIA), helps identify potential biases and discriminatory outcomes that could arise from the AI system. This process involves evaluating the data and the algorithm for fairness, accountability, and transparency. It ensures that any biases in the data are detected and addressed, thus preventing discriminatory practices and promoting ethical AI deployment. Reference: AIGP Body of Knowledge on Ethical AI and Impact Assessments.

NEW QUESTION # 55
Under the NIST Al Risk Management Framework, all of the following are defined as characteristics of trustworthy Al EXCEPT?

• A. Secure and Resilient.
• B. Explainable and Interpretable.
• C. Accountable and Transparent.
• D. Tested and Effective.

Answer: D

Explanation:
The NIST AI Risk Management Framework outlines several characteristics of trustworthy AI, including being secure and resilient, explainable and interpretable, and accountable and transparent. While being tested and effective is important, it is not explicitly listed as a characteristic of trustworthy AI in the NIST framework.
The focus is more on the system's ability to function safely, securely, and transparently in a way that stakeholders can understand and trust. Reference: AIGP Body of Knowledge, NIST AI RMF section.

NEW QUESTION # 56
All of the following are penalties and enforcements outlined in the EU Al Act EXCEPT?

• A. Fines for SMEs and startups will be proportionally capped.
• B. Rules on General Purpose Al will apply after 6 months as a specific provision.
• C. Fines for violations of banned Al applications will be €35 million or 7% global annual turnover (whichever is higher).
• D. The Al Pact will act as a transitional bridge until the Regulations are fully enacted.

Answer: D

Explanation:
The EU AI Act outlines specific penalties and enforcement mechanisms to ensure compliance with its regulations. Among these, fines for violations of banned AI applications can be as high as €35 million or 7% of the global annual turnover of the offending organization, whichever is higher. Proportional caps on fines are applied to SMEs and startups to ensure fairness. General Purpose AI rules are to apply after a 6-month period as a specific provision to ensure that stakeholders have adequate time to comply. However, there is no provision for an "AI Pact" acting as a transitional bridge until the regulations are fully enacted, making option C the correct answer.

NEW QUESTION # 57
You are an engineer that developed an Al-based ad recommendation tool.
Which of the following should be monitored to evaluate the tool's effectiveness?

• A. GPU performance, to evaluate the tool's robustness.
• B. Algorithmic patterns, to show the model has a high degree of accuracy.
• C. Input data, to ensure the ads are reaching the target audience.
• D. Output data, assess the delta between the prediction and actual ad clicks.

Answer: D

Explanation:
To evaluate the effectiveness of an AI-based ad recommendation tool, the most relevant metric is the output data, specifically assessing the delta between the prediction and actual ad clicks. This metric directly measures the tool's accuracy and effectiveness in making accurate recommendations that lead to user engagement. While monitoring algorithmic patterns and input data can provide insights into the model's behavior and targeting accuracy, and GPU performance can indicate the robustness and efficiency of the tool, the primary indicator of effectiveness for an ad recommendation tool is how well it predicts actual ad clicks.
Reference: AIGP BODY OF KNOWLEDGE, sections on AI performance metrics and evaluation methods.

NEW QUESTION # 58
A company is creating a mobile app to enable individuals to upload images and videos, and analyze this data using ML to provide lifestyle improvement recommendations. The signup form has the following data fields:
1.First name
2.Last name
3.Mobile number
4.Email ID
5.New password
6.Date of birth
7.Gender
In addition, the app obtains a device's IP address and location information while in use.
What GDPR privacy principles does this violate?

• A. Transparency and Accuracy.
• B. Accountability and Lawfulness.
• C. Integrity and Confidentiality.
• D. Purpose Limitation and Data Minimization.

Answer: D

Explanation:
The GDPR privacy principles that this scenario violates are Purpose Limitation and Data Minimization.
Purpose Limitation requires that personal data be collected for specified, explicit, and legitimate purposes and not further processed in a manner that is incompatible with those purposes. Data Minimization mandates that personal data collected should be adequate, relevant, and limited to what is necessary in relation to the purposes for which they are processed. In this case, collecting extensive personal information (e.g., IP address, location, gender) and potentially using it beyond the necessary scope for the app's functionality could violate these principles by collecting more data than needed and possibly using it for purposes not originally intended.

NEW QUESTION # 59
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. Overfitting.
• B. Hallucination.
• C. Prompt injection.
• D. Model collapse.

Answer: B

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 # 60
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