Candidates must be able to select and apply Azure-native tools to discover shadow AI, enforce governance policies, assess third-party compliance, and monitor model drift. The most important thing is aligning controls with the organization's AI governance policy and risk tolerance.
Start practicing
AI Governance And Risk — choose a session length
Free · No account required
Domain overview
This domain covers establishing and auditing AI governance and risk management. It tests identifying shadow AI, enforcing policy compliance, securing third-party AI services, and monitoring model drift and data quality. Questions are scenario-based, requiring selection of effective controls and tools, often within Microsoft Azure environments.
Exam objectives
Use Azure Monitor and Log Analytics to detect unauthorized AI services.
Apply Microsoft Purview for data governance and compliance policy enforcement.
Audit third-party AI via Azure Policy and compliance certifications.
Monitor model drift with Azure Machine Learning and Application Insights.
Assuming network firewalls alone can detect shadow AI; they miss cloud API calls and embedded AI features.
Treating non-compliance as a technical bug rather than a governance failure requiring policy enforcement and escalation.
Overlooking third-party AI risks; contracts and certifications are critical, not just technical integration.
Click any question to see the full explanation and answer options, or start a focused practice session above.
When auditing an AI system for regulatory compliance, you find that data lineage is broken between the feature store and the training pipeline. Which control is most likely deficient?
2A firm uses a third-party AI service. Which control is most critical to ensure compliance with the firm's own AI governance policy?
3You are auditing a 'Model Monitoring' dashboard. Which metric is most indicative of a potential degradation in the model's reliability over time?
4You are assessing a company's 'AI Governance Committee'. Which action best demonstrates effective board-level oversight?
5Which component of an AI policy is most important for establishing organizational accountability?
6During an AI risk assessment, you notice that the 'Model Risk Management' (MRM) framework does not mandate independent validation for models categorized as 'High Impact'. What is the most significant consequence?
7A board of directors asks you to define the 'AI Risk Appetite'. Which approach best satisfies the governance requirement?
8Which TWO of the following are primary components of an AI model risk management inventory?
9Which THREE factors must be evaluated when assessing the 'Fairness' of an AI model?
10A data scientist proposes using a 'black-box' model for a high-stakes loan approval process. Which action does your AI audit framework require?
11What is the goal of a 'Human-in-the-loop' (HITL) control in an AI system?
12Which document is the primary source for establishing the scope of an AI audit?
13During an audit, you find that the AI training data includes personally identifiable information (PII) that was not anonymized. Which regulation does this most likely violate?
14You are reviewing the 'AI Incident Response Plan'. Which element is essential for compliance?
15Which technique is most appropriate for mitigating 'Concept Drift' in a production model?
16You are assessing 'Model Security'. Which attack vector specifically exploits the AI training phase?
17Which artifact should an auditor request to verify 'Model Governance' during the model life cycle?
18What is the primary function of an 'AI Ethics Board'?
19In the context of 'AI Compliance', what is a 'Model Card'?
20Which THREE components are critical for a robust 'AI Governance' framework?
21Which TWO of the following are essential when performing an 'AI Risk Assessment'?
22You are auditing a model's 'Data Pre-processing' step. Which practice is a major concern regarding data privacy?
23Which role is primarily responsible for ensuring that the AI governance framework is followed in daily operations?
24When auditing an 'AI Model Lifecycle', which phase occurs immediately after the 'Model Training' phase?
25What is the purpose of 'Model Versioning' in an AI audit context?
26You find that the 'Inference API' for a model allows 'Prompt Injection' attacks. Which governance failure is most likely?
27Which AI risk is best mitigated by conducting a 'bias audit' on the training dataset?
28A firm uses a 'Federated Learning' approach. Which governance benefit does this provide?
29What is the main role of the 'AI Risk Assessment' document in a project lifecycle?
30Which TWO of the following are typical 'Model Risk Management' (MRM) pillars?
31Which THREE criteria are necessary for an AI system to be deemed 'Trustworthy' under most governance frameworks?
32What is the key difference between 'AI Ethics' and 'AI Compliance'?
33What is the primary function of an 'AI Audit Log'?
34What is the purpose of an 'AI Policy' within an organization?
35A company's AI governance policy requires 'Model explainability'. Which tool or technique best satisfies this requirement for complex deep learning models?
36You are auditing a firm using LLMs (Large Language Models). Which risk is most specific to generative AI compared to traditional predictive models?
37Which 'AI Risk Mitigation' strategy is most effective for reducing 'Data Leakage' in training sets?
38Which action is required when a model is found to be 'Non-compliant' with the internal AI Governance policy?
39You are auditing a model's 'Robustness' against adversarial attacks. Which test is most appropriate?
40Which TWO of the following are examples of AI governance 'Inputs'?
41Which THREE risks should be explicitly managed in an 'AI Governance' program?
42What is the primary objective of 'Board-level AI Governance'?
43During an audit of an AI system, you notice that 'Model validation' is performed by the same team that developed the model. Why is this a concern?
44You are auditing a 'Model Lifecycle' and find that the decommissioning process is not defined. What is the impact?
45You are auditing an organization's AI deployment in Microsoft Azure. The Chief Risk Officer requires a central dashboard to monitor AI model performance drift and data quality metrics across all deployed services. Which tool should you confirm is configured to provide these insights?
46An organization uses AWS SageMaker. As an auditor, you need to verify that model lineage and versioning are being captured to satisfy the AI risk assessment requirement for reproducibility. Which feature ensures that every model training job is recorded with its data sources?
47A board of directors requests a quarterly report on AI risk exposure. Which component of the NIST AI Risk Management Framework should you prioritize to demonstrate that the board's AI policy is being operationally enforced?
48You are reviewing a model risk management (MRM) framework for an AI system using NVIDIA NeMo Guardrails. What is the primary purpose of this tool in a risk mitigation strategy?
49When auditing model fairness in a production environment using IBM Watson OpenScale, which metric would you specifically validate to ensure the model is not violating protected class regulations?
50An organization is scaling its AI governance program. You need to automate the identification of 'Shadow AI' in the corporate network. Which tool would be the most effective to gain visibility into unauthorized AI model API usage?
51As part of an AI audit, you are checking the 'AI Transparency' requirement. Which documentation artifact is essential to provide to external regulators to explain the model's design, intended use, and limitations?
52In a financial services firm, you are auditing the 'Explainability' of a credit scoring model. The model uses complex non-linear features. Which technique should you verify is being used to provide local explanations for individual credit decisions?
53You are reviewing the AI Incident Response Plan. Which scenario constitutes a 'High' severity AI incident that requires immediate board notification under most enterprise risk frameworks?
54You are assessing the risk of 'Data Poisoning' in a retail AI model. Which control should you implement during the data ingestion pipeline to mitigate this risk?
55When drafting an AI policy, you need to define the 'Human-in-the-Loop' (HITL) requirement. Which of the following represents a best-practice control for HITL?
56An auditor is asked to review the AI model inventory. What information must be present in the inventory for every model to satisfy basic regulatory requirements?
57Which THREE of the following are core components of a robust AI Governance and Risk Assessment Framework?
58Which TWO of the following are examples of 'Model Risk Management' (MRM) controls that must be validated during an audit?
59Which THREE of the following should be included in an AI Policy document to ensure compliance and ethical alignment?
60Which THREE of the following are potential risks associated with AI model 'Drift' that an auditor must account for?
61Which TWO of the following are core components of an AI incident response strategy?
62You are auditing a financial institution's AI governance framework. The organization has a documented AI risk appetite statement, but you find that individual AI use cases are approved without any reference to it. Which action should you recommend to remediate this gap?
63During an audit of an AI-based fraud detection system, you find that the model's performance is monitored only through overall accuracy. The system processes highly imbalanced data with a fraud rate of 0.5%. Which risk does this monitoring approach most likely fail to detect?
64An AI governance committee is reviewing a proposal to deploy a generative AI chatbot for customer service. The committee wants to ensure that the chatbot's responses do not violate company policies or regulatory requirements. Which control should be implemented to provide the highest level of assurance?
65You are auditing an organization's AI governance framework and find that AI systems are being developed by various business units without any central oversight. Which governance structure would best address this issue?
66An auditor is reviewing an organization's AI risk management process. The organization has identified several AI risks but has not prioritized them. Which TWO factors should the auditor recommend be used to prioritize AI risks? (Choose two.)
67A global retailer has deployed an AI-powered dynamic pricing engine across 14 countries. The internal audit team discovers that the system adjusts prices based on inferred customer gender and ethnicity derived from browsing behavior. The organization has a formal AI ethics policy that prohibits discriminatory pricing. Which action should the lead AI auditor recommend FIRST?
68An AI auditor is evaluating a natural language processing model used to screen job applicants. The model was trained on five years of historical hiring data. The auditor finds that the model's selection rate for female candidates is 72% of the rate for male candidates. According to the EEOC Uniform Guidelines, what is the auditor's PRIMARY concern?
69An organization is deploying an AI system that makes autonomous decisions about credit limit increases. The board asks the AI auditor to explain the concept of 'human in the loop' as it applies to this system. Which statement best describes the auditor's explanation?
70During an audit of an AI-based fraud detection system at a bank, the auditor discovers that the model's performance has degraded significantly over the past quarter. The data science team reports that the model was trained on transaction patterns from two years ago and has not been retrained. Which risk does this situation PRIMARILY represent?
71An AI auditor is assessing the governance framework for a machine learning model that predicts patient readmission risk in a hospital network. The model informs decisions about post-discharge care. Which TWO of the following are the most critical governance controls the auditor should verify? (Choose two.)
Candidates must be able to select and apply Azure-native tools to discover shadow AI, enforce governance policies, assess third-party compliance, and monitor model drift. The most important thing is aligning controls with the organization's AI governance policy and risk tolerance.
The Courseiva AAIA question bank contains 71 questions in the AI Governance And Risk domain. Click any question to see the full explanation and answer breakdown.
Start with a 10-question focused session to identify your baseline accuracy in this domain. Read every explanation — even for questions you answer correctly — to understand the reasoning. Once you score consistently above 80%, move to a 20–30 question session to confirm depth before moving to the next domain.
Yes — the session launcher on this page draws questions exclusively from the AI Governance And Risk domain. Choose 10, 20, 30, or 50 questions for a focused session, or click individual questions to review them one by one.
Save your results, see per-domain analytics, and get readiness scores — free, for every certification.
Sign Up FreeFree forever · Every certification included