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IAPP Artificial Intelligence Governance Professional AIGP Exam

Attempt the IAPP Certification Programs practice test and solve real exam-like AIGP questions to prepare efficiently and increase your chances of success. Our IAPP AIGP practice questions match the actual Artificial Intelligence Governance Professional exam format, helping you enhance confidence and improve performance. With our AIGP practice exam software, you can analyze your performance, identify weak areas, and work on them effectively to boost your final IAPP Certification Programs exam score.

Vendor:	IAPP
Exam Name:	Artificial Intelligence Governance Professional
Registration Code:	AIGP
Related Certification:	IAPP Certification Programs Certification
Exam Audience:	AI project managers, AI governance professionals,

Total Questions

194

Last Updated

20-08-2026

Exam Duration

180 MINUTES

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Question: 1

Scenario:

A public sector agency is reviewing proposed AI use cases for improving services. It wants to prioritize implementations that deliver value butminimize unintended negative consequences.

When evaluating which AI use cases to implement, an organization should consider all of the following EXCEPT:

ARelated TEVV (test, evaluate, verify, validate) and system metrics

BThe users and their expectations

CEquitable access to the AI tool

DPotential positive and negative impacts of the system

Answer: C

Explanation:

The correct answer isA. While TEVV is important inlater lifecycle phases, it isnot the primary considerationwhen evaluating and prioritizinguse cases.

From the AIGP Body of Knowledge -- Use Case Assessment Module:

''Use case evaluation focuses on value, impact, fairness, and accessibility---technical testing considerations come later.''

ILT Guide confirms:

''Organizations should first assess whether the AI system provides equitable outcomes and aligns with stakeholder expectations. TEVV is part of implementation, not initial prioritization.''

Thus,Ais not a top-level consideration during use caseselection.

Question: 2

A company developing and deploying its own AI model would perform all of the following steps to monitor and evaluate the model's performance EXCEPT?

APublicly disclosing data with forecasts of secondary and downstream harms to stakeholders.

BSetting up automated tools to regularly track the model's accuracy, precision and recall rates in real-time.

CImplementing a formal incident response plan to address incidents that may occur during system operation.

DEstablishing a regular schedule for human evaluation of the model's performance, including qualitative assessments.

Answer: A

Explanation:

While transparency is encouraged,publicly disclosing forecasts of secondary harmsisnot a required or standard practicefor internal performance evaluation. Risk assessments and reporting typically remaininternal or shared with regulators.

From theAI Governance in Practice Report 2025:

''Organizations must assess secondary risks... but disclosure is subject to context, regulatory requirements, and risk management discretion.'' (p. 30)

Question: 3

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?

AAudit the model.

BRetrain the model.

CSeek input from data scientists.

DCommunicate necessary updates to your users.

Answer: A

Explanation:

When anew versionof a third-party model is released, the deployer must ensure it still meets safety, performance, and compliance requirements --- which calls for aformal audit.

From theAI Governance in Practice Report 2025:

''Any updates or changes to AI systems should trigger a re-evaluation to ensure continued compliance and performance.'' (p. 12)

''Post-market monitoring includes reassessing the impact of updated models or retraining.'' (p. 35)

Question: 4

CASE STUDY

Please use the following to answer the next question:

A mid-size US healthcare network has decided to develop an AI solution to detect a type of cancer that is most likely to 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 to a radiologist for secondary review pursuant to agreed-upon criteria such as a confidence score below a threshold.

To date, the healthcare network has:

Defined its AI ethical principles

Conducted discovery to identify the intended uses and success criteria for the system

Established an AI risk committee

Assembled a cross-functional team with clear roles and responsibilities

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. It also intends to retain a large consulting firm to supplement its small data science team and 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 AI solution?

AProcure more data from clinical research partners.

BEngage a third party to perform an audit.

CPerform an impact assessment.

DCreate a bias bounty program.

Answer: C

Explanation:

The correct answer is C because performing an impact assessment is the most effective proactive measure to identify and mitigate discrimination risks before model training and testing. AI governance frameworks emphasize early-stage risk identification, particularly for high-stakes domains like healthcare, where bias can lead to harmful or inequitable outcomes. Impact assessments, such as algorithmic or data protection impact assessments, evaluate potential harms, affected populations, and fairness risks prior to development. This enables organizations to design appropriate safeguards, adjust data selection, and implement mitigation strategies before biases become embedded in the model. Options like audits and bias bounty programs are reactive or post-development controls, while simply acquiring more data does not guarantee reduced bias without proper analysis. A structured impact assessment aligns with risk-based governance and supports fairness, accountability, and regulatory compliance.

Question: 5

Which of the following disclosures is NOT required for an EU organization that developed and deployed a high-risk Al system?

AThe human oversight measures employed.

BHow an individual may contest a decision.

CThe location(s) where data is stored.

DThe fact that an Al system is being used.

Answer: C

Explanation:

Under the EU AI Act, organizations that develop and deploy high-risk AI systems are required to provide several key disclosures to ensure transparency and accountability. These include the human oversight measures employed, how individuals can contest decisions made by the AI system, and informing individuals that an AI system is being used. However, there is no specific requirement to disclose the exact locations where data is stored. The focus of the Act is on the transparency of the AI system's operation and its impact on individuals, rather than on the technical details of data storage locations.

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