AIGP Exam Practice Questions and Answers

Artificial Intelligence is exploding into every corner of business and society, and that means governing AI is a top priority. A recent IAPP report finds that nearly half of organizations list AI governance as a strategic priority. In other words, companies need experts who can oversee AI safely and ethically. The new IAPP Artificial Intelligence Governance Professional (AIGP) certification fills this need by training professionals to manage AI risk. Passing the AIGP exam shows you have the skills to create transparent, fair, and accountable AI systems. In this article, we’ll explore key AI governance concepts and walk through 20 AIGP exam practice questions (with answers and explanations) to help you master the AIGP exam.

By studying these questions and answers, you’ll get a feel for the exam format and sharpen your AI governance know-how.

Top 20 AIGP Exam Practice Questions and Answers

1. Which practice best ensures accountability in an AI project?

1. Determining the business objectives and success criteria for the AI system.
2. Performing due diligence on any third-party training and testing data.
3. Defining and documenting clear roles and responsibilities for all AI stakeholders.
4. Ensuring all AI outputs comply with legal and regulatory requirements.

Answer: C. Defining and documenting clear roles and responsibilities for all AI stakeholders.

Explanation: Having clear, documented roles for team members and decision-makers is crucial for accountability. When everyone’s role is specified (for example, who is responsible for data quality, who approves models, who monitors outcomes), it creates a built-in accountability framework. Each aspect of the AI lifecycle, data collection, model development, deployment, and monitoring, has a person or team “on the hook” for it. This means issues can be traced to the responsible party and fixed systematically.

2. Which statement best describes machine learning (ML)?

1. Systems that can mimic human intelligence to replace humans.
2. Systems that can automatically improve from experience by finding patterns in data.
3. Statistical methods that predict human behavior without actual training data.
4. Algorithms that discover unknown features in data and then redesign the data itself.

Answer: B. Systems that can automatically improve from experience by finding patterns in data.

Explanation: Machine learning is a subset of AI where software learns from data rather than following explicit rules. In ML, an algorithm analyzes data, recognizes patterns (for example, customer behavior patterns), and then makes predictions or decisions based on those patterns. Over time and with more data, the system “learns”. Its performance can improve without further programming.

3. Which of the following is an example of a high-risk AI application under the EU AI Act?

1. A resume-scanning tool that ranks job applicants.
2. An AI-powered inventory management system for retail stores.
3. A government-run social credit or social scoring system.
4. A customer service chatbot on an e-commerce website.

Answer: C. A government-run social credit or social scoring system.

Explanation: The EU AI Act defines “high-risk” systems as those likely to affect people’s rights or safety in important areas (e.g., education, employment, essential services, law enforcement). A social scoring system (which assesses citizens and influences how they are treated) can profoundly impact privacy, fairness, and individual rights. Therefore, it is explicitly listed as high-risk under the Act. By contrast, a resume screener or chatbot (A and D) is generally not automatically classified as high-risk by the Act (they might fall under general rules if at all). An internal inventory tool (B) is also low-risk.

4. Under the EU AI Act, providers of which AI systems must register with an EU oversight agency before placing them on the market.

1. AI systems that have harmful side effects in some legal calculations.
2. AI systems that claim to be “strong” or artificial general intelligence.
3. AI systems that are trained on sensitive personal data.
4. AI systems that are classified as high-risk.

Answer: D. AI systems that are classified as high-risk.

Explanation: The EU AI Act establishes a registration requirement specifically for high-risk AI systems. High-risk systems (like those used in healthcare, critical infrastructure, employment decisions, etc.) must be registered with an EU database so regulators can monitor compliance. This is part of the Act’s risk-based framework. The Act does not use terms like “strong general intelligence” (B) or penalize systems merely for using sensitive data (C) in this registration sense, and A is not how the Act is phrased.

5. Which of the following actions would not directly support fairness testing of an AI model?

1. Checking that the model’s decisions are statistically consistent across different demographic groups.
2. Telling end users or applicants about the model’s capabilities and limitations.
3. Identifying whether more training data is needed for any underrepresented group.
4. Using diagnostic tools to uncover factors causing differences in decisions.

Answer: B. Telling end users or applicants about the model’s capabilities and limitations.

Explanation: Fairness testing is about evaluating the model for bias and ensuring equitable decisions across groups. Actions A, C, and D involve analyzing the model or its data (checking consistency across groups, augmenting data for fairness, using tools to identify bias factors); these steps directly test or improve fairness. Option B, on the other hand, is about transparency to users: telling applicants about the model. While informing users is generally good practice, it does not itself test or improve the fairness of the model’s outputs.

6. What term describes an algorithm that greedily makes the best immediate choice at each step, without regard to the long-term best solution?

1. Single-lane
2. Optimized
3. Efficient
4. Greedy

Answer: D. Greedy

Explanation: In algorithm design, a greedy algorithm always takes the locally optimal choice at each step to quickly reach a solution. It does not consider the overall long-term outcome. This can work well for some problems (if it happens that local best choices lead to a global best solution), but not always. The other terms (single-lane, optimized, efficient) are not standard names for this method. Greedy algorithms focus on immediate gains, for example, choosing the largest coin first when making change, and are often discussed in AI and optimization contexts (e.g., greedy feature selection, greedy search).

7. Which stakeholder is most important in an AI project team when selecting the specific algorithm?

1. The cloud infrastructure provider.
2. The external consulting firm.
3. The organization’s own data science team.
4. The AI governance committee.

Answer: C. The organization’s own data science team.

Explanation: Choosing the right algorithm is a highly technical decision that requires a deep understanding of the data, the problem domain, and AI methods. The data science team or the technical AI Developers are the ones with this expertise. They know what models fit the data and use case (for example, whether to use a convolutional network, decision tree, etc.), and they can experiment to compare performance. By contrast, a cloud provider (A) just supplies infrastructure, and a Consultant (B) may advise but doesn’t know the day-to-day data as well as the in-house team. The governance committee (D) provides oversight and policy, but does not usually choose specific algorithms.

8. When integrating external partner data into your AI solution, what is the most important first step?

1. Apply privacy-enhancing techniques (like de-identification) to all data.
2. Identify and map fits and gaps between the partner’s data and your own.
3. Ensure all data is already labeled and formatted for your model.
4. Check the country of origin of each data source.

Answer: B. Identify and map fits and gaps between the partner’s data and your own.

Explanation: Before you merge or use new data, you must understand how well it aligns with your existing data and project goals. This means identifying “fits” (where the data matches and is useful) and “gaps” (where the data may be missing fields, have inconsistencies, or not cover needed cases). This step is foundational: it tells you if the combined dataset is coherent and sufficient for training. If you skip this, you might train a model on flawed or mismatched data. Privacy techniques (A), data formatting (C), and legal checks (D) are also important, but come after you know what data you have.

9. When collecting third-party data for an AI system, which step is most critical from a legal/compliance perspective?

1. Conducting a formal privacy impact assessment.
2. Using only fully anonymized data.
3. Keeping data sets physically separate (segregation).
4. Reviewing and complying with the data provider’s terms of use.

Answer: D. Reviewing and complying with the data provider’s terms of use.

Explanation: Any third-party data will come with contractual or licensing terms that specify how the data can be used. Before using the data, you must carefully review these terms to ensure your use case (training an AI model) is permitted. This helps avoid legal issues later. While doing a DPIA (A) and anonymizing data (B) are good practices for privacy, they assume you are already authorized to use the data. If the terms of use forbid certain uses or require consent, that’s the first hurdle. Segregation (C) is a technical control that doesn’t address legality.

10. Which measure best mitigates the risk of discrimination before training an AI model?

1. Procuring more data from partners or diverse sources.
2. Hiring an External Auditor to check your algorithms.
3. Conducting an AI/Algorithmic Impact Assessment (like a DPIA).
4. Creating a bug bounty program for AI bias.

Answer: C. Conducting an AI/algorithmic impact assessment (like a DPIA).

Explanation: An Algorithmic Impact Assessment (or Data Protection Impact Assessment in privacy terms) is a formal process that identifies and evaluates potential biases and privacy risks in an AI project before the system goes live. By performing this assessment early, you can proactively spot discrimination issues in your data or design. It involves analyzing how the model might treat different groups and putting controls in place.

11. What distinguishes supervised learning from unsupervised learning in machine learning?

1. Supervised learning uses labeled data (with known outputs); unsupervised learning finds patterns in unlabeled data.
2. Supervised learning must happen in a controlled lab environment; unsupervised learning can be in real-world settings.
3. Supervised learning always outputs a category; unsupervised learning always outputs a number.
4. Supervised learning improves from experience; unsupervised learning does not learn over time.

Answer: A. Supervised learning uses labeled data (with known outputs); unsupervised learning finds patterns in unlabeled data.

Explanation: In supervised learning, each training example includes the “right answer” (label), and the algorithm learns to map inputs to these labeled outputs. In unsupervised learning, there are no labels; the algorithm identifies structure or groupings on its own (for example, clustering customer profiles). The key difference is labels: “supervised” means the model is taught with correct answers, whereas “unsupervised” means the model finds patterns without guidance.

12. Which of the following is not a core principle of responsible AI?

1. Fairness (ensuring no unfair bias).
2. Transparency (being able to explain decisions).
3. Accountability (having responsible oversight).
4. Profit Maximization (ensuring the AI increases profits).

Answer: D. Profit Maximization (ensuring the AI increases profits).

Explanation: Responsible AI principles focus on ethical and societal values such as fairness, transparency, accountability, privacy, and safety. All of the first three options (A, B, C) are commonly cited principles of trustworthy AI. Profit is a business outcome, not an ethical principle, and it is not part of the ethics frameworks or guidelines for AI governance. In fact, if profit maximization conflicts with fairness or privacy, ethical governance would prioritize the latter.

13. The EU AI Act is described as a “risk-based” regulation. What does “risk-based” mean here?

1. It imposes the same rules for all AI systems, regardless of use.
2. It bans all AI that carries any potential risk.
3. It classifies AI systems into risk categories (like “high-risk”) and applies stricter rules to higher-risk uses.
4. It leaves enforcement completely up to individual member states.

Answer: C.  It classifies AI systems into risk categories (like “high-risk”) and applies stricter rules to higher-risk uses.

Explanation: A risk-based approach means that not all AI is treated equally. Instead, the Act defines different levels of risk (for example, unacceptable, high-risk, or low-risk) and tailors obligations accordingly. High-risk AI applications (like those affecting health or legal rights) face stringent requirements (e.g., conformity assessments, registration, transparency duties). Lower-risk AI has lighter touch rules (or general fairness rules), and only certain uses (like “systemic risk”) are banned outright. So the EU AI Act is risk-based in that it regulates heavily where the risk to individuals is greatest.

14. Why is human oversight considered an important principle in AI governance?

1. To ensure that a human can take responsibility if the AI behaves unexpectedly or biases appear.
2. Because AI systems always require manual correction to function correctly.
3. To allow humans to improve the AI by giving feedback in real time.
4. To minimize the cost of AI by having humans do redundant work.

Answer: A. To ensure that a human can take responsibility if the AI behaves unexpectedly or biases appear.

Explanation: Human oversight means that humans monitor or can intervene in AI systems, especially for critical decisions. The main reason is to maintain control and responsibility. If an AI system starts making unfair or harmful decisions, a human overseer can correct the course. This oversight helps catch issues like bias or mistakes early on. Options B, C, and D are either false (B – well-designed AI shouldn’t always need manual fixes; D – oversight isn’t about cost-saving) or incomplete (C – while feedback loops can exist, the core reason is accountability). In ethical AI, keeping a “human-in-the-loop” ensures that someone is ultimately accountable for the outcomes.

15. What is the purpose of a Data Protection Impact Assessment (DPIA) in the context of an AI project?

1. To schedule project milestones and costs for privacy compliance.
2. To identify and mitigate privacy risks of an automated decision system before it is deployed.
3. To encrypt all personal data used in AI training.
4. To automatically delete data once the model is trained.

Answer: B. To identify and mitigate privacy risks of an automated decision system before it is deployed.

Explanation: A DPIA is a formal process (often required by GDPR) that assesses how an AI project might impact an individual’s privacy. In other words, before an AI system that processes personal data goes live, a DPIA evaluates risks (like sensitive data exposure or unfair automated decisions) and specifies steps to reduce those risks (for example, anonymizing data or adding safeguards). It’s not about scheduling or encryption itself (A, C) or automatic deletion (D), although deletion could be a mitigation. The goal is proactive risk management, catching privacy issues early, and building controls, which are essential for lawful and ethical AI deployment.

16. What does “privacy by design” mean for an AI development process?

1. Collect all data first, and then decide later how to protect privacy.
2. Build privacy protections into the AI system from the start of the project.
3. Only use AI systems that are marketed as privacy-friendly.
4. Allow each user to design their own privacy settings.

Answer: B. Build privacy protections into the AI system from the start of the project.

Explanation: Privacy by design is a principle that says privacy should not be an afterthought. In practice, it means considering privacy throughout the AI development lifecycle, for example, minimizing data collection, using pseudonymization when possible, and designing the system to protect personal data. Option A is backward (reactive instead of proactive), C is not practical (vendors claim privacy, but it’s about your design process), and D is a user control that helps but doesn’t guarantee design-level protection.

17. In machine learning, what is “concept drift”?

1. A sudden change in the AI’s source code.
2. A technique for speeding up model training.
3. The situation where a model’s performance degrades over time because real-world data distribution changes.
4. A form of data encryption.

Answer: C. The situation where a model’s performance degrades over time because real-world data distribution changes.

Explanation: Concept drift occurs when the underlying patterns in the data evolve after a model is deployed. For example, a fraud detection model might see new types of fraud strategies that were not present in the training data. As a result, the model’s accuracy can drop because it was tuned to old patterns. It’s a well-known issue in AI governance: organizations must monitor and update models as needed to handle drift.

18. How can explainability be improved in AI models?

1. By using the most complex, high-accuracy model available.
2. By using simpler models or model-agnostic explanation tools (like LIME or SHAP).
3. By avoiding any documentation of model behavior.
4. By keeping the model’s algorithms secret from users.

Answer: B. By using simpler models or model-agnostic explanation tools (like LIME or SHAP).

Explanation: Explainability means making a model’s decisions understandable to humans. One common approach is to use simpler models (like decision trees) that are inherently easier to interpret. Another approach is to use techniques (like LIME/SHAP) that analyze a complex model’s behavior on specific predictions to show which features influenced its decisions.

19. Who should be involved in an organization’s AI governance committee?

1. Only the Chief Information Officer (CIO).
2. A cross-functional team (e.g., IT, privacy, legal, compliance, and business stakeholders).
3. External Auditors only.
4. All employees, to crowdsource governance.

Answer: B. A cross-functional team (e.g., IT, privacy, legal, compliance, and business stakeholders).

Explanation: Effective AI governance requires multiple perspectives. A governance committee typically includes representatives from IT/AI development, legal and compliance, privacy, risk management, and business units. Each brings expertise: for example, privacy professionals understand data protection laws, while business stakeholders know how the AI will be used.

20. Which of these is an example of a fairness metric that can help detect bias in an AI model?

1. Statistical parity or equalized odds (comparing outcomes across groups).
2. Counting the total number of features in the model.
3. Measuring average training time.
4. Checking that the code comments are all in English.

Answer: A. Statistical parity or equalized odds (comparing outcomes across groups).

Explanation: Fairness metrics are quantitative ways to check if a model treats groups equally. For example, statistical parity checks if different demographic groups get positive outcomes at the same rate, and equalized odds checks if groups have equal error rates. These metrics directly reveal if a model may be biased against a certain group.

AIGP Exam Study Tips

• Set a Study Schedule:
  Aim for 30 minutes daily. Consistent short sessions beat last-minute cramming.
• Use Active Recall:
  After studying, write down key points or quiz yourself instead of rereading.
• Leverage Flashcards:
  Use tools like Quizlet or Anki to memorize terms (e.g., supervised learning) and principles (e.g., privacy by design).
• Apply the Pomodoro Technique:
  Study in 25-minute focused blocks, followed by a 5-minute break to avoid burnout.
• Practice in Exam-Like Conditions:
  Use a timer when doing sample questions. Explain your answers out loud—even the correct ones—to solidify understanding.
• Focus on Conceptual Understanding:
  Know the “why” behind key practices (e.g., why DPIAs are essential before AI deployment).

AIGP Training with InfosecTrain

Mastering AI governance means understanding both technical AI concepts and the ethical/legal frameworks that surround them. By working through questions like these, you’ll reinforce key ideas—from what machine learning really means to how global regulations like the EU AI Act work. Remember to apply the study tips above (schedule study time, use flashcards, practice actively) as you prepare. With focused preparation and hands-on practice, you’ll be well on your way to acing the AIGP exam and becoming a certified AI governance professional.

InfosecTrain’s AIGP Training Course is designed to help you build that well-rounded knowledge, covering everything from AI fundamentals to critical global regulations. With expert-led sessions, practical resources, and proven study strategies, you’ll gain the confidence and skills needed to succeed.

Ready to lead in the era of responsible AI? Join InfosecTrain’s AIGP training and take the smart step toward certification.

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