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The modern IAPP world is changing its dynamics at a fast pace. To stay updated and competitive you have to learn these technological changes. With the one IAPP Certified Artificial Intelligence Governance Professional (AIGP) certification exam you can do this easily. The IAPP Certified Artificial Intelligence Governance Professional (AIGP) certification exam offers a unique and quick way to learn new in-demand expertise and enhance your knowledge.

IAPP AIGP Exam Syllabus Topics:

Topic
Details

Topic 1

Understanding the Foundations of AI Governance: This section of the exam measures skills of AI governance professionals and covers the core concepts of AI governance, including what AI is, why governance is needed, and the risks and unique characteristics associated with AI. It also addresses the establishment and communication of organizational expectations for AI governance, such as defining roles, fostering cross-functional collaboration, and delivering training on AI strategies. Additionally, it focuses on developing policies and procedures that ensure oversight and accountability throughout the AI lifecycle, including managing third-party risks and updating privacy and security practices.

Topic 2

Understanding How Laws, Standards, and Frameworks Apply to AI: This section of the exam measures skills of compliance officers and covers the application of existing and emerging legal requirements to AI systems. It explores how data privacy laws, intellectual property, non-discrimination, consumer protection, and product liability laws impact AI. The domain also examines the main elements of the EU AI Act, such as risk classification and requirements for different AI risk levels, as well as enforcement mechanisms. Furthermore, it addresses the key industry standards and frameworks, including OECD principles, NIST AI Risk Management Framework, and ISO AI standards, guiding organizations in trustworthy and compliant AI implementation.

Topic 3

Understanding How to Govern AI Deployment and Use: This section of the exam measures skills of technology deployment leads and covers the responsibilities associated with selecting, deploying, and using AI models in a responsible manner. It includes evaluating key factors and risks before deployment, understanding different model types and deployment options, and ensuring ongoing monitoring and maintenance. The domain applies to both proprietary and third-party AI models, emphasizing the importance of transparency, ethical considerations, and continuous oversight throughout the model’s operational life.

Topic 4

Understanding How to Govern AI Development: This section of the exam measures the skills of AI project managers and covers the governance responsibilities involved in designing, building, training, testing, and maintaining AI models. It emphasizes defining the business context, performing impact assessments, applying relevant laws and best practices, and managing risks during model development. The domain also includes establishing data governance for training and testing, ensuring data quality and provenance, and documenting processes for compliance. Additionally, it focuses on preparing models for release, continuous monitoring, maintenance, incident management, and transparent disclosures to stakeholders.

IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q157-Q162):

NEW QUESTION # 157
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 # 158
In the machine learning context, feature engineering is the process of?

A. Developing guidelines to train and test a model.
B. Converting raw data into clean data.
C. Extracting attributes and variables from raw data.
D. Creating learning schema for a model apply.

Answer: C

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 # 159
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. Perform an impact assessment.
B. Create a bias bounty program.
C. Engage a third party to perform an audit.
D. Procure more data from clinical research partners.

Answer: A

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 # 160
Scenario:
An organization wants to leverage its existing compliance structures to identify AI-specific risks as part of an ongoing data governance audit.
Which of the following compliance-related controls within an organization is most easily adapted to identify AI risks?

A. Transfer risk assessments
B. Privacy impact assessments
C. Privacy training
D. Penetration testing

Answer: B

Explanation:
The correct answer is D - Privacy impact assessments (PIAs). These are directly adaptable for identifying risks in AI systems, particularly around data usage, bias, and individual impacts.
From the AIGP ILT Guide - Risk Management Module:
"PIAs and DPIAs are existing tools used in privacy compliance that can be extended to evaluate the risks of AI, including fairness, explainability, and legality." AI Governance in Practice Report 2024 further explains:
"Organizations can adapt privacy impact assessments to evaluate the ethical, legal, and technical risks posed by AI systems. They provide a structured and recognized method." PIAs are preferable over general security practices (like pen testing) which do not address algorithmic bias or legal compliance directly.

NEW QUESTION # 161
A company has trained an ML model primarily using synthetic data, and now intends to use live personal data to test the model.
Which of the following is NOT a best practice apply during the testing?

A. The test data should be anonymized to the extent practicable.
B. Testing should be performed specific to the intended uses.
C. The test data should be representative of the expected operationaldata.
D. Testing should minimize human involvement to the extent practicable.

Answer: D

Explanation:
Minimizing human involvement to the extent practicable is not a best practice during the testing of an ML model. Human oversight is crucial during testing to ensure that the model performs correctly and ethically, and to interpret any anomalies or issues that arise. Best practices include using representative test data, anonymizing data to the extent practicable, and performing testing specific to the intended uses of the model.
Reference: AIGP Body of Knowledge on AI Model Testing and Human Oversight.

NEW QUESTION # 162
......

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Alan Bell Alan Bell

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