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Guide for Artificial Intelligence Governance Professional (AIGP)

Quiz Artificial Intelligence Governance Professional (AIGP)

Updated on 2026/05/05

PDF|

Guide for Artificial Intelligence Governance Professional (AIGP)

Updated on 2026/05/05

Randomized | 10 Questions per Test | 20 Minutes | 70% to pass

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4.7

Quiz

Question 1/101/10

Foundations of AI Governance

Which of the following best describes artificial intelligence in the context of AI governance?

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1 correct answer

Systems that can perform tasks typically requiring human intelligence, such as learning, reasoning and decision-making

Software that exclusively uses rule-based logic to automate repetitive tasks

Hardware devices designed to replicate human brain structure at the cellular level

Statistical models that only operate on structured numerical datasets

Quiz

Question 2/102/10

Organizational AI Governance Programs

Which role is MOST commonly responsible for providing executive-level oversight of an organization's AI governance program?

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1 correct answer

Chief AI Officer or equivalent senior executive with board-level accountability for AI governance

Junior data analyst

External marketing consultant

IT helpdesk supervisor

Quiz

Question 3/103/10

AI Lifecycle Policies and Procedures

Which of the following stages should be included in an organization's AI lifecycle governance policies?

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1 correct answer

All stages including use case assessment, risk management, data acquisition, model development, training and testing, deployment, monitoring, documentation and incident management

Only the deployment and monitoring stages

Only the initial development and training stages

Only the stages required by a single regulation

Quiz

Question 4/104/10

Data Privacy Laws and AI

How do transparency requirements under data privacy laws apply to AI systems?

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1 correct answer

Organizations must only disclose that they use computers in their operations

Organizations are only required to publish their privacy policy on their website

Organizations must inform individuals when AI is used to make decisions about them, explain the logic involved and disclose the significance and consequences

Transparency requirements do not apply to AI systems because they are automated

Quiz

Question 5/105/10

Existing Laws Applicable to AI

How do intellectual property and copyright laws apply to AI training data and models?

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1 correct answer

AI systems can be trained on any content without copyright restrictions

Copyright and intellectual property laws restrict use of copyrighted works for AI training unless appropriate licenses or fair use exceptions apply

Only original AI-generated content is protected by copyright; training data has no IP protections

Quiz

Question 6/106/10

AI-Specific Laws and Regulations

What is the risk-based approach of the EU AI Act and how does it classify AI systems?

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1 correct answer

The EU AI Act treats all AI systems the same with identical requirements

The EU AI Act uses a risk-based approach classifying AI into prohibited, high-risk, limited-risk and minimal-risk categories with corresponding regulatory requirements

The EU AI Act only applies to government use of AI

The EU AI Act prohibits all AI systems

Quiz

Question 7/107/10

AI Standards and Frameworks

What are the OECD AI Principles and what principles do they encompass?

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1 correct answer

The OECD has not published AI principles

OECD AI Principles only apply to government

OECD AI Principles provide non-binding recommendations covering human-centered values, fairness, transparency, robustness and accountability

OECD principles are legally binding

Quiz

Question 8/108/10

Governing AI System Design and Development

How should organizations approach use case definition and governance assessment at the start of AI development?

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1 correct answer

Use case definition is not necessary for AI development

Organizations should document use cases, identify stakeholders, conduct governance assessments and determine whether adequate safeguards can be implemented

Use case definition is only a marketing activity

Governance assessment should occur only after development is complete

Explanation: Effective governance begins before AI development starts. Use case definition clearly articulates what problem the AI system will solve, who will use it and how success will be measured. Clear use case definition prevents scope creep and misalignment of stakeholder expectations. Use case documentation should specify the business objective, how the AI system will achieve the objective, what data is needed and how success will be measured. Stakeholder identification involves identifying who will be affected by the AI system including users, customers, employees, affected communities and regulators. Understanding stakeholders' perspectives helps organizations identify governance concerns. Governance assessments should determine whether the intended use case can be implemented with appropriate safeguards. High-risk use cases such as automated hiring decisions or medical diagnosis require more extensive safeguards and governance. Some use cases might be assessed as too risky to implement without substantial additional development. Assessment should consider whether adequate data exists, whether the problem is well-defined and whether performance standards can be achieved. Privacy assessments should identify what personal data is needed and whether appropriate legal bases exist for processing. Discrimination risk assessments should evaluate whether the AI system might produce discriminatory outcomes. Security assessments should determine whether the system can be protected against attacks. Use case refinement should address governance concerns. If an assessment identifies significant risks, the use case might be refined to reduce risks. Risk mitigation strategies should be developed before development begins. Governance commitment at use case definition stage ensures that governance is built in rather than added later. Leadership commitment to responsible AI development is more likely to succeed if commitment occurs before substantial investment. Use case governance documentation should be maintained throughout development and should guide development decisions.

Right Answer: B

Quiz

Question 9/109/10

Data Governance for AI Training and Testing

What are the core components of a data governance framework for AI systems?

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1 correct answer

Data governance should include data quality standards, lineage tracking, access controls and retention policies

AI systems do not need data governance

Data governance only applies to privacy data

Governance of data processing is separate from governance of AI systems

Explanation: Data governance frameworks establish policies and controls ensuring that data used in AI meets quality standards and is handled appropriately. Data quality standards establish expectations for accuracy, completeness and representativeness. Data should be assessed against these standards before use in AI training. Lineage tracking documents where data came from, how it was processed and how it is used. Complete lineage enables understanding of data provenance and enables tracing problems to their source. Access controls establish who can access data. Different people need access to different data; granular controls enable appropriate access. Data purpose restriction requires that data only be used for specified purposes. Using data for unauthorized purposes violates governance. Data retention policies establish how long data is kept. Data should be retained only as long as necessary; excessive retention creates privacy and security risks. Metadata management tracks information about data including schema, data types, valid value ranges and known quality issues. Metadata enables understanding of data characteristics. Master data management maintains authoritative definitions; data definitions should be consistent across systems. Data classification schemes categorize data based on sensitivity and governance requirements. Different classifications require different protections. Sensitive personal data, health data and biometric data require heightened protections. Data quality metrics measure compliance with quality standards. Metrics help identify data quality problems. Data quality assessment should occur regularly. Governance documentation should record policies and should explain governance requirements. Governance should include clear procedures for requesting data access and for understanding data quality. Governance should establish roles responsible for various data governance functions including data quality assessment, access control and retention management. Regular reviews should assess whether governance is achieving intended outcomes.

Right Answer: A

Quiz

Question 10/1010/10

AI System Release, Monitoring, and Maintenance

What governance processes should be implemented before releasing AI systems to production?

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1 correct answer

No governance is needed before deployment

Organizations should conduct readiness reviews, final testing and establish monitoring before release

Release is purely a technical activity

Governance only applies after deployment

Explanation: Production release is a critical governance point where systems transition from development to operational use. Readiness reviews should assess whether systems are ready for deployment. Technical readiness including testing completion and bug fixes should be verified. Operational readiness including documentation and training should be confirmed. Governance readiness including compliance with requirements should be verified. Final testing should confirm that systems work as expected in production-like environments. Staging environments should closely match production to surface integration issues. Smoke testing should verify that basic functionality works. Load testing should verify that systems handle expected traffic. Security testing should verify vulnerability fixes. Data verification should confirm that production data is appropriate for deployment. Final approval should occur before deployment. Sign-off from technical, operations and governance leadership should confirm readiness. Change management procedures should be followed. Production environment preparation should ensure that infrastructure is ready. Database configuration should be correct. Monitoring infrastructure should be in place. Failover mechanisms should be tested. Documentation should be completed including user documentation, operational procedures and escalation contacts. Training for operational staff should prepare them for system monitoring and incident response. Rollback procedures should be prepared and tested. If deployment causes problems, rapid rollback should be possible. Release notes should document what changed, known limitations and any special instructions. Deployment scheduling should minimize disruption. Off-peak deployment can reduce impact if problems occur. Phased rollout can start with limited deployment and expand gradually. Monitoring setup should establish baselines for expected system behavior. Alerts should be configured to detect anomalies. Incident response contacts should be established. Governance should require completion of readiness processes before production release.

Right Answer: B

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Quiz name:Artificial Intelligence Governance Professional (AIGP)
Total number of questions:516
Number of questions for the test:50
Pass score:80%

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