New AIGP Study Materials & New AIGP Exam Name

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IAPP AIGP Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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.
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 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 4
  • 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.

>> New AIGP Study Materials <<

Latest AIGP Preparation Materials: IAPP Certified Artificial Intelligence Governance Professional - AIGP Study Guide - TestPassKing

The TestPassKing is committed to making the entire AIGP exam preparation journey simple, smart, and successful. To achieve this objective the TestPassKing is offering the top-rated and updated AIGP exam practice test questions in three different formats. All these three TestPassKing AIGP Exam Questions formats contain the real, valid, and error-free IAPP Certified Artificial Intelligence Governance Professional (AIGP) exam practice test questions that are ideal study material for quick IAPP AIGP exam preparation.

IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q74-Q79):

NEW QUESTION # 74
MULTI-SELECT
Please select 3 of the 5 options below. No partial credit will be given.
Training an AI model is time-consuming because of?

Answer: A,C,D

Explanation:
Training an AI model is time-consuming primarily due tomodel complexity,large data volumes, and the need forhigh-quality, well-prepared data.
From theAI Governance in Practice Report2025:
"Most AI requires sizeable amounts of high-quality data... to ensure desired and accurate output." (p. 15)
"The accuracy of AI model outputs depends significantly on the quality of their inputs." (p. 24)
"Complex AI systems... with many parameters... result in long development and training phases." (p. 32)
* B. Maturity of governanceaffects oversight, not training time.
* D. Number of stakeholdersaffects alignment, not direct training duration.


NEW QUESTION # 75
All of the following are common optimization techniques in deep learning to determine weights that represent the strength of the connection between artificial neurons EXCEPT:

Answer: D

Explanation:
Autoregression is a statistical modeling technique for time-series analysis, not a weight optimization technique used in deep learning neural networks.


NEW QUESTION # 76
CASE STUDY
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.
All of the following are obligations of the company as a data controller when implementing its AI system EXCEPT?

Answer: D

Explanation:
The correct answer isA. While location of processors may have implications (such as for data transfers under GDPR), thereis noabsoluterequirement thatthird-party processors be based in the same country.
From the AI Governance in Practice Report2025and ILT Guide:
"Data controllers are responsible for ensuring that third-party processors have adequate protections, but not necessarily that they reside in the same jurisdiction. What is required is legal safeguards (e.g., SCCs) for international transfers, not same-country location." In contrast, DPIAs, DSARs, and implementation of technical/organizational safeguardsare explicitlyrequired underGDPR and responsible AI frameworks.


NEW QUESTION # 77
Which of the following use cases would be best served by a non-AI solution?

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 # 78
CASE STUDY
Please use the following to answer the next question:
A leading insurance provider that offers a range of coverage options to individuals has decided to utilize AI to streamline and improve its customer acquisition and underwriting process, including the accuracy and efficiency of pricing policies. The company has engaged a cloud provider to utilize and fine-tune its pre-trained, general purpose large language model ("LLM").
The company 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 to a human underwriter for final review.
The company 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. They have 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, the company realizes that the LLM declines a higher percentage of women's applications.
The best approach to enable a customer who wants information on the AI model's parameters for underwriting purposes is to provide:

Answer: B

Explanation:
An AI model card is the appropriate mechanism for providing transparent, structured information about the model's purpose, parameters, data use, evaluation results, and limitations. It gives customers meaningful insight into how the AI contributes to underwriting decisions without exposing proprietary details.


NEW QUESTION # 79
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