MCQs

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Overall MCQs for Reference

1. Which framework is widely recognized for managing AI and machine learning risks in organizations?
   a. NIST AI RMF
   b. GDPR
   c. PCI DSS
   d. HIPAA
   Answer: a. NIST AI RMFibm​
2. The purpose of the NIST AI Risk Management Framework is to:
   a. Promote trustworthy and responsible AI systems
   b. Restrict data sharing in organizations
   c. Eliminate all cybersecurity vulnerabilities
   d. Guarantee profitability
   Answer: a. Promote trustworthy and responsible AI systemsibm​
3. Which international standards emphasize transparency and accountability in AI risk management?
   a. ISO/IEC
   b. HIPAA
   c. PCI DSS
   d. SOX
   Answer: a. ISO/IECibm​
4. What is the primary regulatory challenge when implementing machine learning in compliance processes?
   a. Data quality and explainability
   b. Shortage of computing resources
   c. High cost of cloud storage
   d. Lack of data privacy laws
   Answer: a. Data quality and explainabilityrapidinnovation+1​
5. Which of the following is a critical component of risk governance in machine learning?
   a. Human oversight
   b. Fully automated systems
   c. Cost reduction alone
   d. Ignoring regulations
   Answer: a. Human oversightthecompliancedigest​
6. In risk and regulatory reporting, machine learning models must be:
   a. Interpretable to regulators and investigators
   b. Always black-box and opaque
   c. Only used for financial calculations
   d. Free from any human review
   Answer: a. Interpretable to regulators and investigatorsfacctum​
7. Regular model validation in ML compliance is necessary to:
   a. Prevent model drift
   b. Increase internet speed
   c. Minimize hardware cost
   d. Reduce employee training time
   Answer: a. Prevent model driftkpmg+1​
8. In regulatory reporting, machine learning is used to:
   a. Flag users or behaviors outside the norm
   b. Only calculate payroll taxes
   c. Build websites
   d. Design user interfaces
   Answer: a. Flag users or behaviors outside the normregnology​
9. The “GOVERN” function in the AI RMF framework is applied to:
   a. All stages of AI risk management
   b. Only the evaluation stage
   c. Only the deployment stage
   d. Only maintenance
   Answer: a. All stages of AI risk managementnvlpubs.nist+1​
10. GDPR primarily addresses which regulatory concern in ML?
    a. Data privacy
    b. Hardware compatibility
    c. Market trends
    d. Algorithm speed
    Answer: a. Data privacythecompliancedigest​
11. What is the main reason for using hybrid models in compliance AI applications?
    a. To balance rules-based and machine learning outputs
    b. To increase power consumption
    c. To guarantee only manual reviews
    d. To ignore data governance
    Answer: a. To balance rules-based and machine learning outputsfacctum​
12. Which activity falls under risk assessment in ML compliance?
    a. Predicting potential compliance violations
    b. Improving video resolution
    c. Creating images
    d. Running marketing campaigns
    Answer: a. Predicting potential compliance violationsanalystprep+1​
13. A risk register in machine learning risk management is used to:
    a. Identify, assess, and track risks
    b. Schedule employee holidays
    c. Increase ad spending
    d. Automate social media posts
    Answer: a. Identify, assess, and track risksexamradar​
14. What does continuous monitoring of ML models help prevent?
    a. Inaccurate compliance decisions
    b. Network congestion
    c. HR conflicts
    d. Inventory shortages
    Answer: a. Inaccurate compliance decisionsthecompliancedigest+1​
15. In risk management, “risk tolerance” is influenced by:
    a. Legal or regulatory requirements
    b. Computer processor brand
    c. Video card type
    d. Operating system
    Answer: a. Legal or regulatory requirementsnvlpubs.nist​
16. Which function in the AI RMF is responsible for evaluating risk measurement?
    a. MEASURE
    b. GOVERN
    c. EXPLAIN
    d. TRAIN
    Answer: a. MEASUREnvlpubs.nist+1​
17. What is a major drawback of AI in compliance?
    a. Dependency on high-quality data
    b. Low internet bandwidth
    c. Expensive electricity
    d. Overuse of robotics
    Answer: a. Dependency on high-quality datarapidinnovation+1​
18. In risk-focused reporting, what must organizations document and disclose?
    a. Definitions of harm and relevant incidents
    b. Number of printers used
    c. Office space layout
    d. Entertainment expenses
    Answer: a. Definitions of harm and relevant incidentsnvlpubs.nist​
19. The primary benefit of automating compliance tasks with AI is:
    a. Lower time and cost for manual work
    b. Increasing the number of manual forms
    c. Guaranteeing no need for specialist staff
    d. Doubling the length of reports
    Answer: a. Lower time and cost for manual workrapidinnovation​
20. Explainability tools in ML compliance are mainly designed for:
    a. Investigators and regulators
    b. Salespeople
    c. Marketing agents
    d. Graphic designers
    Answer: a. Investigators and regulatorsfacctum​
21. Regulation for risk management in machine learning often requires:
    a. Robust data protection and privacy controls
    b. Removal of all encryption
    c. Unlimited employee access to data
    d. Ignoring user consent
    Answer: a. Robust data protection and privacy controlsthecompliancedigest​
22. Which regulatory act in Europe primarily impacts ML data processing?
    a. GDPR
    b. GLBA
    c. Sarbanes-Oxley
    d. HIPAA
    Answer: a. GDPRthecompliancedigest​
23. Regular staff training for compliance in ML use is needed in order to:
    a. Understand model decisions and reporting
    b. Build faster computers
    c. Reduce model explainability
    d. Lower internet bills
    Answer: a. Understand model decisions and reportingfacctum​
24. In ML-based regulatory reporting, APIs and middleware are often used to:
    a. Integrate ML models with existing frameworks
    b. Increase battery life
    c. Improve transportation networks
    d. Adjust monitor brightness
    Answer: a. Integrate ML models with existing frameworksthecompliancedigest​
25. A major reason for continuous model validation and retraining in compliance is:
    a. To prevent biases and inaccuracies
    b. Increase file download speeds
    c. Optimize webpage design
    d. Improve gaming performance
    Answer: a. To prevent biases and inaccuraciesfacctum+1​
26. The Basel II Accord’s Pillar 1 covers risks such as:
    a. Credit, Operational, and Market
    b. Regulatory, Ethical, Environmental
    c. Climate, Strategic, Digital
    d. Networking, Hardware, Software
    Answer: a. Credit, Operational, and Markettestbook​
27. Which of the following is NOT usually part of regulatory risk reporting in ML?
    a. Office floor plans
    b. Model governance
    c. Algorithmic explainability
    d. Data handling practices
    Answer: a. Office floor plansrapidinnovation+1​
28. What is required to demonstrate compliance in ML decision-making?
    a. Model documentation and audit trails
    b. Disabling all logs
    c. Avoiding model retraining
    d. Minimizing stakeholder involvement
    Answer: a. Model documentation and audit trailskpmg​
29. A practical challenge in ML-driven compliance is:
    a. Managing computational resources
    b. Having too many company vehicles
    c. Too much free office space
    d. Overuse of desktop wallpapers
    Answer: a. Managing computational resourcesthecompliancedigest​
30. Effective risk management in ML should align with:
    a. Existing laws and sector-specific norms
    b. Personal leisure interests
    c. TV advertising schedules
    d. Car parking policies
    Answer: a. Existing laws and sector-specific normsnvlpubs.nist​
31. Machine learning can help identify which type of risks in institutions?
    a. Non-compliance and operational risks
    b. Physical hardware defects
    c. Vacation scheduling conflicts
    d. Printer usage errors
    Answer: a. Non-compliance and operational risksrapidinnovation+1​
32. What is the role of risk appetite statements in ML compliance management?
    a. Define acceptable levels of risk
    b. Choose graphic design templates
    c. Create logos
    d. Manage plant watering schedules
    Answer: a. Define acceptable levels of riskexamradar​
33. An example of data governance practice in ML compliance is:
    a. Ensuring quality, lineage, and auditability
    b. Increasing server room temperature
    c. Buying larger desks
    d. Disabling network logs
    Answer: a. Ensuring quality, lineage, and auditabilityfacctum​
34. Which approach is used for operational risk in Basel II?
    a. Basic Indicator Approach
    b. Quantum Algorithmic Approach
    c. Blockchain Audit
    d. Physical Plant Review
    Answer: a. Basic Indicator Approachtestbook​
35. Which regulatory report might flag anomalous user behavior using ML?
    a. Transaction monitoring report
    b. Cafeteria usage log
    c. Building access form
    d. Maintenance checklist
    Answer: a. Transaction monitoring reportregnology​
36. Adhering to sector-specific documentation and disclosure is required by:
    a. Regulatory authorities
    b. Marketing firms
    c. Event organizers
    d. Game developers
    Answer: a. Regulatory authoritiesnvlpubs.nist​
37. The risk management framework for AI should be:
    a. Flexible and adaptable
    b. Rigid and unchangeable
    c. Marketing only
    d. Without human input
    Answer: a. Flexible and adaptableibm+1​
38. The EU AI Act is an example of:
    a. Sector-specific regulatory legislation for AI
    b. TV licensing rules
    c. Corporate branding
    d. Desktop publishing
    Answer: a. Sector-specific regulatory legislation for AIibm​
39. What is critical in preventing bias in ML regulatory decisions?
    a. Diverse and representative training data
    b. Higher monitor brightness
    c. Faster printers
    d. Lower water temperature
    Answer: a. Diverse and representative training datakpmg+1​
40. A function of MEASURE in the AI RMF is to:
    a. Assess effectiveness of controls
    b. Clean the office
    c. Repair hardware
    d. Manage travel budgets
    Answer: a. Assess effectiveness of controlsibm+1​
41. ML model transparency helps organizations:
    a. Demonstrate regulatory compliance
    b. Schedule cleaning staff
    c. Paint office walls
    d. Arrange parking permits
    Answer: a. Demonstrate regulatory compliancerapidinnovation+1​
42. Value at Risk (VaR) is a standard approach for:
    a. Market risk quantification
    b. Employee attendance
    c. Office lighting
    d. Internet connectivity
    Answer: a. Market risk quantificationtestbook​
43. In regulatory AI frameworks, systemic risk is addressed under:
    a. Pillar 2 supervisory review
    b. Human Resource Policy
    c. Employee wellness programs
    d. Social media guidelines
    Answer: a. Pillar 2 supervisory reviewtestbook​
44. Which is a model risk in ML regulatory compliance?
    a. Model drift and bias
    b. Air conditioning failure
    c. Overpolished floors
    d. Poor lighting
    Answer: a. Model drift and biaskpmg​
45. An important characteristic for regulatory reporting tools in ML:
    a. Auditability
    b. Animation design
    c. Desk ergonomics
    d. Floral arrangements
    Answer: a. Auditabilitykpmg+1​
46. The concept of risk appetite in ML means:
    a. Amount of risk an organization is willing to accept
    b. Number of lunches provided
    c. Size of meeting rooms
    d. Popularity of logos
    Answer: a. Amount of risk an organization is willing to acceptexamradar+1​
47. AI risk management frameworks are typically:
    a. Voluntary but widely adopted
    b. Mandated for all businesses globally
    c. Reserved for academic use only
    d. Outlawed in Europe
    Answer: a. Voluntary but widely adoptedibm​
48. What is a crucial consideration for reporting ML-driven compliance?
    a. Model explainability and documentation
    b. Choosing office colors
    c. Growing indoor plants
    d. Music system setup
    Answer: a. Model explainability and documentationkpmg+1​
49. In terms of compliance, maintaining which is crucial for ML applications?
    a. Data lineage and quality
    b. Staff birthday lists
    c. Wall calendars
    d. Printer cartridge levels
    Answer: a. Data lineage and qualityfacctum​
50. Regulatory compliance in machine learning requires regular:
    a. Model testing and validation
    b. Coffee breaks
    c. Team-building outings
    d. Wall repainting
    Answer: a. Model testing and validationkpmg+1​

1. https://www.ibm.com/think/insights/ai-risk-management
2. https://www.rapidinnovation.io/post/ai-machine-learning-for-regulatory-compliance
3. https://thecompliancedigest.com/how-governance-risk-and-compliance-are-embracing-machine-learning/
4. https://www.facctum.com/terms/machine-learning
5. https://assets.kpmg.com/content/dam/kpmgsites/in/pdf/2024/10/effective-model-risk-management-framework-for-ai-ml-based-models.pdf
6. https://www.regnology.net/en/resources/insights/future-of-suptech-ai-and-machine-learning-in-regulatory-reporting/
7. https://nvlpubs.nist.gov/nistpubs/ai/nist.ai.100-1.pdf
8. https://analystprep.com/study-notes/frm/machine-learning-and-ai-for-risk-management/
9. https://examradar.com/risk-assessment-methodologies-mcqs-multiple-choice-questions-with-answer/
10. https://testbook.com/objective-questions/mcq-on-risk–5f916a517cc3ca44254118f9
11. https://www.sanfoundry.com/1000-machine-learning-questions-answers/
12. https://www.scribd.com/document/512977404/MCQ-of-Machine-Learning
13. https://www.studocu.com/in/document/dr-apj-abdul-kalam-technical-university/machine-learning-techniques/ml-mcq-all-5-machine-learning-mcqs/16412586
14. https://joinmytutor.com/mcq/risk-management.php
15. https://www.scribd.com/document/450826064/Risk-Mgmt-MCQ-s
16. https://www.sciencedirect.com/science/article/pii/S0925753518311184
17. https://www.360factors.com/blog/how-does-machine-learning-impact-the-future-of-risk-and-compliance-management/
18. https://www.iif.com/portals/0/Files/private/32370132_van_liebergen_-_machine_learning_in_compliance_risk_management.pdf
19. https://www.sciencedirect.com/science/article/pii/S0893395224001893
20. https://ai.wharton.upenn.edu/white-paper/artificial-intelligence-risk-governance/