Download AIGP Exam Dumps Questions to get 100% Success in IAPP [Q119-Q133]

Download AIGP Exam Dumps Questions to get 100% Success in IAPP [Q119-Q133]

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Download AIGP Exam Dumps Questions to get 100% Success in IAPP

100% Accurate Answers! AIGP Actual Real Exam Questions

IAPP AIGP Exam Syllabus Topics:

Section	Objectives
AI Lifecycle Governance Implementation	– Operational governance controls 1. Model development oversight 2. Deployment and monitoring controls
Regulatory and Legal Frameworks for AI	– Global AI governance regulations 1. NIST AI Risk Management Framework 2. EU AI Act principles
AI Risk Management and Impact Assessment	– Risk identification and mitigation 1. AI risk categories and classification 2. Bias, fairness, and transparency risks
Foundations of AI Systems and Governance	– AI system concepts, lifecycle, and terminology 1. AI definitions and taxonomy 2. AI system lifecycle overview – Governance principles and accountability 1. Organizational governance roles 2. Responsible AI principles

QUESTION 119
All of the following are reasons to deploy a challenger Al model in addition a champion Al model EXCEPT to?

Provide a framework to consider alternatives to the champion model.

Automate real-time monitoring of the champion model.

Perform testing on the champion model.

Retrain the champion model.

Deploying a challenger AI model alongside a champion model is a strategy used to compare the performance of different models in a real-world environment. This approach helps in providing a framework to consider alternatives to the champion model, automating real-time monitoring of the champion model, and performing testing on the champion model. However, retraining the champion model is not a reason to deploy a challenger model. Retraining is a separate process that involves updating the champion model with new data or techniques, which is not related to the use of a challenger model.

QUESTION 120
Which of the following are subjects covered by a typical impact assessment?

Datasets, behavior and tooling.

Third-party risk, model risk and legal risk.

Toxicity, accuracy and development.

Fundamental rights, data protection and safety.

QUESTION 121
All of the following apply to enforcement of the EU AI Act EXCEPT:

Warnings may be imposed for violations rather than fines.

Fines for SMEs and startups will be proportionally capped.

Rules on General Purpose AI will be enforced 6 months after the Act comes into force.

Fines for violations involving banned AI applications may be up to €35 million or 7% global annual turnover (whichever is higher).

Under the EU AI Act, the enforcement timeline for different provisions varies. While certain rules on prohibited AI practices took effect on 2 February 2025, the rules for General Purpose AI (GPAI) models are set to be enforced 12 months after the Act’s entry into force, which is 2 August
2025.

QUESTION 122
All of the following issues are unique for proprietary AI model deployments EXCEPT?

The acquisition of training data.

The cost of AI chips.

The potential for bias.

The necessity of performing conformity assessments.

Biasis a common risk acrossboth proprietary and open-source models, andnot uniqueto proprietary deployments. All AI systems – regardless of origin – require evaluation for fairness, accuracy, and representativeness.
From theAI Governance in Practice Report2025:
“Bias, discrimination and fairness challenges are present in both open and closed models, regardless of how the model is sourced.” (p. 41)

QUESTION 123
An organization establishes a cross-functional AI governance committee including legal, engineering, compliance, and HR teams. What is the primary purpose of this approach?

To accelerate AI deployment timelines

To centralize technical decision-making

To ensure diverse expertise and risk perspectives

To reduce the need for external audits

QUESTION 124
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.
When prioritizing the updates to its policies, rules and procedures to include the new AI system for user authentication, the organization should:

Update third-party data sharing policies

Update security controls for sensitive data

Ensure that any personal data used is only processed for a specific and lawful purpose

Reduce the complexity of the policy to make it easier for non-technical employees to understand

The correct answeris.CThis action ties directly into principlesof dataminimization, purpose limitation,and lawfulnessof processing, which are central to privacy and AI governance.
From the AIGP Body of Knowledge, Section on Privacy Considerations:
“Personal data must only be processed for specified and lawful purposes. Organizations must consider whether they have a legal basis for processing such data under data protection laws like the GDPR or CCPA.” Additionally, AI Governance in Practice Report2025emphasizes:
“One of the most significant challenges when designing and developing AI systems is ensuring the data used is appropriate for the intended purpose… Managing unnecessary data, especially data that may contain sensitive attributes, can increase risk.”

QUESTION 125
What is the most important factor when deciding whether or not to select a proprietary AI model?

What business purpose it will serve.

How frequently it will be updated.

Whether its training data is disclosed.

Whether its system card identifies risks.

Theprimary considerationin selecting any AI system, especially aproprietary model, is itsfit for business purpose. Whether it serves the intended goals is foundational before evaluating technical or governance features.
From theAI Governance in Practice Report 2024:
“AI governance starts with defining the corporate strategy for AI… and aligning systems with business purpose and operational context.” (p. 11)
* B, C, Dare relevant for evaluation, but onlyafterconfirming business applicability.

QUESTION 126
CASE STUDY
Please use the following to answer the next question:
A small local flower delivery company operates a few stores and has a limited IT budget. To reduce the time spent on customer service, which is a major drain on employee time, the company plans to implement a simple but high-performance generative AI chatbot on its website. The chatbot will provide real-time, fact- based responses to customer inquiries and assist with order processing, helping to reduce incoming phone calls.
The company currently uses a single on-premises server for its order database and email system and has not yet partnered with a cloud provider.
An internal data privacy policy governs the use of customer data. This policy, written before the adoption of AI, allows the use or transfer of customer data beyond name and contact information but requires that all such data be deleted after the order is completed. The company prefers to maintain this policy without changes.
What deployment option is the simplest, quickest, and most cost-effective option across the various potential AI models for this particular company?

On-premises, running on the company ‘ s existing server.

Hosting on rented servers via a private cloud.

Software-as-a-Service (SaaS) accessed via an API.

Edge computing running on each customer ‘ s hardware.

Software-as-a-Service accessed through an API is the most practical deployment approach for a small organization with limited IT infrastructure and budget. SaaS allows the company to consume a provider- managed application without purchasing and maintaining the substantial computing infrastructure normally needed for modern generative AI. Under NIST ‘ s cloud-computing model, SaaS consumers use provider applications while the provider manages the underlying network, servers, operating systems, storage, and much of the supporting infrastructure. This makes implementation comparatively rapid and scalable. Running the model on the existing on-premises server could require hardware upgrades and specialized maintenance.
A private-cloud deployment introduces additional infrastructure and administrative complexity. Edge deployment on customers ‘ devices would also create substantial technical and compatibility challenges.
Therefore, considering the company ‘ s resources and desired speed of deployment, C is the strongest option.

QUESTION 127
Your organization is searching for a new way to help accurately forecast sales predictions by various types of customers.
Which of the following is the best type of model to choose if your organization wants to customize the model and avoid lock-in?

A free large language model.

A classic machine learning model.

A proprietary generative AI model.

A subscription-based, multimodal model.

Forcustomizable, interpretable modelsthat allow organizations toretain control and avoid vendor lock-in, classic ML models(e.g., regression, decision trees, random forests) are optimal.
From theAI Governance in Practice Report2025:
“Organizations seeking transparency, customizability, and control often prefer classic ML models due to their flexibility and ease of governance.” (p. 33)
* AandCmay have limited transparency and are often tied to specific providers.
* Dinvolves ongoing costs and limited model control.

QUESTION 128
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 to apply during the testing?

The test data should be representative of the expected operational data.

Testing should minimize human involvement to the extent practicable.

The test data should be anonymized to the extent practicable.

Testing should be performed specific to the intended uses.

Minimizing human involvement during testing is not a best practice; human oversight is critical to validate model behavior and ensure ethical and accurate performance.

QUESTION 129
All of the following are elements of establishing a global Al governance infrastructure EXCEPT?

Providing training to foster a culture that promotes ethical behavior.

Creating policies and procedures to manage third-partyrisk.

Understanding differences in norms across countries.

Publicly disclosing ethical principles.

Establishing a global AI governance infrastructure involves several key elements, including providing training to foster a culture that promotes ethical behavior, creating policies and procedures to manage third-party risk, and understanding differences in norms across countries. While publicly disclosing ethical principles can enhance transparency and trust, it is not a core element necessary for the establishment of a governance infrastructure. The focus is more on internal processes and structures rather than public disclosure. Reference: AIGP Body of Knowledge on AI Governance and Infrastructure.

QUESTION 130
If it is possible to provide a rationale for a specific output of an Al system, that system can best be described as?

Accountable.

Transparent.

Explainable.

Reliable.

If it is possible to provide a rationale for a specific output of an AI system, that system can best be described as explainable. Explainability in AI refers to the ability to interpret and understand the decision-making process of the AI system. This involves being able to articulate the factors and logic that led to a particular output or decision. Explainability is critical for building trust, enabling users to understand and validate the AI system’s actions, and ensuring compliance with ethical and regulatory standards. It also facilitates debugging and improving the system by providing insights into its behavior.

QUESTION 131
After initially deploying a third-party AI model, you learn the developer has released a new version. As deployer of this third-party model, what should you do?

Audit the model.

Retrain the model.

Seek input from data scientists.

Communicate necessary updates to your users.

When a third-party developer releases a new model version, the deployer must first audit the updated model to determine whether the changes introduce new risks, alter performance, or affect compliance obligations before considering deployment or communicating updates.

QUESTION 132
In procuring an AI system from a vendor, which of the following would be important to include in a contract to enable proper oversight and auditing of the system?

Liability for mistakes.

Ownership of data and outputs.

Responsibility for improvements.

Appropriate access to data and models.

Ensuringoversight and auditabilityrequires that the organization hassufficient access to data, documentation, and model internalsor outputs necessary for evaluation.
From theAI Governance in Practice Report 2024:
“Access to technical documentation and system internals is essential to enable effective auditing, conformity checks, and accountability mechanisms.” (p. 11, 34)
* Ais about liability, not auditability.
* Bmatters for IP rights, not oversight.
* Crelates to lifecycle responsibility but doesn’t guarantee audit access.

QUESTION 133
Scenario:
A U.S.-based AI governance professional is evaluating resources from the National Institute of Standards and Technology (NIST) to guide the organization’s AI risk assessment strategy. They are particularly interested in programs focused on assessing AI-specific impacts.
The main purpose of NIST’sAssessing Risks and Impacts of AI (ARIA)program is to:

Provide a suite of resources to manage risks

Pilot new standards for AI red-teaming

Promote interoperability across AI systems

Offer a regulatory sandbox for risk reporting

The correct answer isA. TheARIA programby NIST is explicitly designed tosupport stakeholders in understanding and managing the risks and impactsof AI systems.
From the AIGP ILT Guide – U.S. Risk Frameworks Module:
“NIST’s ARIA program develops and pilots assessment tools for AI risks and impacts, aimed at improving organizational capacity for responsible AI use.” Also cited in the AI Governance in Practice Report2025(Frameworks Section):
“ARIA supports and aligns with the AI Risk Management Framework by helping organizations assess AI harms, safety concerns, and societal implications.” ARIA isnota red-teaming or sandbox program-it’s anassessment and governance resource.

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