AAISM Domain 1: AI Governance, Program Management Practice Test

These questions are part of the practice quiz. Start practicing

• In governance terms, which statement describes model performance drift?
• IEEE 7000-2021 focuses on embedding human values during AI system design, including privacy, transparency, and fairness.
• Which area focuses on handling sensitive information and ensuring compliance in AI programs?
• Which term describes the manipulation of data inputs to degrade the AI model's accuracy?
• What governance considerations apply to cloud vs on-prem AI deployments?
• Why is data lineage critical in AI governance?
• Which term describes a branch of AI that translates human language into data for computer systems?
• What are the core pillars of data governance necessary for AI programs?
• Under what condition should you retrain a model with updated datasets?
• What is the primary purpose of an AI program charter in governance?
• What is the purpose of an AI governance maturity model?
• What is the primary aim of AI governance?
• Which diagram category focuses on horizontal data movement across an enterprise and is suitable for high-level modeling of data flows?
• Which principle emphasizes integrating security considerations from the start of AI development rather than as an afterthought?
• Senior leader roles such as CIO, CTO, or CDO exemplify which governance term?
• How does AI strategy alignment ensure AI initiatives deliver business value?
• Which component is responsible for applying a model to live production data and returning inference results?
• Which governance strategy is essential for ensuring ongoing risk oversight when scaling AI?
• Which term stores files as vectors with associated metadata?
• Which of the following is not typically included in governance-focused AI program training?
• Which term describes the overall approach of classifying and protecting data based on its sensitivity and regulatory requirements?
• Which set of data security levels is commonly used to categorize data by sensitivity?
• Which component translates principles into actionable governance measures?
• What information is typically included in a model card?
• Which statement best describes how MLOps supports AI governance?
• Which considerations govern data retention for AI systems?
• Which stakeholder group is primarily responsible for ensuring data used in AI meets governance and quality standards?
• What role would likely be responsible for interpreting anomalous AI outputs during model development?
• Which concept builds trust, ensures fairness, protects privacy, and reduces reputational and legal risks?
• How should AI intellectual property be treated in governance structures?
• What elements belong in an AI ethics and governance policy?
• Which option describes the contents of an AI incident post-mortem?
• Which practice involves injecting prompts to cause the AI to reveal restricted outputs or behave unexpectedly?
• Which term describes the ability to understand and interpret how an AI system makes decisions?
• Which statement describes AI governance budgeting with milestones?
• What process evaluates the overall design, development, and deployment of AI systems to assess risk before and after release?
• A Responsible AI governance framework primarily defines which of the following and translates them into policies and controls?
• What does AI asset inventory track to support risk and compliance management?
• What does a Privacy Impact Assessment (PIA) address?
• How should an AI governance risk register be structured?
• Which elements are included in an AI governance change management plan?
• Which term describes the stage of deploying models to production and producing inference results from live data?
• Which data quality attribute means data is uniform and standard across datasets (formats, lengths, metadata)?
• Explainability in AI governance is used to assess what aspect of model decisions?
• Which KPI category would specifically track whether data used for modeling meets predefined quality standards?
• Capacity planning in AI program governance focuses on ensuring what?
• What is the primary purpose of model cards in AI governance?
• Why is exit/transition planning included in vendor governance?
• The PDPC Model AI Governance Framework was developed by which country?
• What ensures ongoing improvement in AI governance after deployment?
• Which statement best describes the role of data readiness in prioritizing AI initiatives?
• How should AI program budgets be structured for governance?
• How does AI program governance differentiate between program management and project management?
• What procurement considerations are unique to AI capabilities?
• Which documentation is essential for AI governance audits?
• Which term refers to a repository that stores files as vectors and metadata for similarity search?
• What is the purpose of an ethical risk taxonomy in AI governance?
• OECD AI Principles originated from which organization?
• IEEE 7000-2021 focuses on embedding which values into AI during system design?
• Which mechanisms involve data validation, cleaning, and anomaly detection to prevent data poisoning?
• Which standard is most closely associated with describing AI systems using machine learning, as mentioned in the material?
• Data lineage in AI asset inventory means:
• Which elements constitute an effective AI incident response plan?
• Which data quality attribute indicates data is up-to-date and available when needed?
• How should bias be assessed and mitigated in AI models?
• Which term describes a data repository that can aggregate structured and unstructured data while preserving access controls from the source?
• What term describes data considered highly effective to the enterprise for data quality efforts, identified using BIAs?
• ISO/IEC 42001 defines what kind of standard?
• Which organization published AI Principles that include transparency, inclusiveness, sustainability, and accountability?
• Which assessment aims to evaluate fundamental rights impacts of an AI system?
• Which data quality attribute ensures data adheres to defined business and technical logic and is considered valid?
• Which standard is described as a voluntary framework focusing on ethical use, transparency, and accountability in AI governance?
• What term describes compromising AI by entering prompts that cause it to behave in unintended ways?
• Which statement best describes the role of governance in managing AI risks?
• How should data quality be measured for AI training data?
• Which concept focuses on promoting ethical AI to build trust and protect privacy?
• What is a key purpose of an effective data management strategy in governance?
• Why is diversity and inclusion important in AI governance teams?
• Why is model versioning important in AI governance?
• Which role includes AI/data science leaders, security experts, developers, and architects?
• Which roles are commonly involved in AI governance?
• TEVV in AI system development refers to which of the following expansions?
• Which concept involves prioritizing data quality efforts on the most critical processes identified by BIAs?
• Which set of training should AI program teams receive?
• Which data quality attribute indicates data contains all the necessary fields and records?
• Which term is most closely associated with safeguarding personal data and ensuring compliance during AI incidents?
• NIST AI RMF helps organizations with what objective?
• Which collection of activities is identified as critical for AI governance-related change management?
• Which statement best describes data retention governance for AI systems?
• Which role includes legal or ethical advisors to ensure AI governance?
• In data governance, what is the role of a business glossary?
• Which items are examples of emerging AI risks?
• What is an adversarial attack in AI?
• In AI security programs, what is the difference between KPIs and KRIs?
• How should governance address talent and data resources for AI programs?
• Which of the following is one of the Five Vs of Big Data?
• Which domain is associated with managing assets and defining metadata management responsibilities?
• Which data quality attribute ensures a dataset has no duplicative or redundant records?
• How do audit trails support AI governance?
• What governance controls help manage AI vendors effectively?
• What is the purpose of business continuity planning for AI systems?
• Which governance mechanism helps ensure accountability for AI initiatives?
• Which of the following is a primary ethical concern in AI risk management?
• In governance context, what is a RACI matrix and why is it essential for AI programs?
• ISO/IEC 23053 describes a framework for describing a generic AI system using machine learning.
• Which KPI category would include tracking cycle time in governance?
• What is the purpose of Model Risk Management (MRM) in an AI program?
• What are common elements included in AI risk assessments that may not be in traditional IT risk assessments?
• Which diagram emphasizes a vertical view of data movement through procedures and is best for illustrating detailed workflow to implement, monitor, and control data according to policies?
• How should data localization requirements influence AI governance?
• Which term describes patterns in data that suggest someone is systematically testing the model's boundaries?
• What is the purpose of a business glossary in data governance?
• What term describes deliberately feeding incorrect data to an AI to generate incorrect results?
• Why are service level agreements important for AI services?
• Which term describes the set of indicators used to monitor potential AI risks and detect emerging threats?
• Which term refers to the sources from which data are collected, which may be restricted and data may be encrypted to ensure data confidentiality?
• Which metrics should governance require for AI models in production?
• What is the role of the AI program steering committee?
• Which service provides guidelines for securing the infrastructure housing AI systems?
• What is a DPIA and when is it required?
• An AI incident post-mortem should include?
• Which of the following is NOT a data modeling approach described?
• Which term describes the senior leader role such as CIO, CTO, or CDO in AI governance?
• Which category of stakeholders are trained in and responsible for safe and ethical use of AI?
• What characterizes an effective AI governance operating model?
• Which practice involves simulating real-world adversaries to identify vulnerabilities in AI applications?
• What does privacy by design imply across the lifecycle?
• Which group guides incident responses to minimize impact on personal data and privacy rights?
• Which governance framework mandates that technology strategies, including AI, adhere to ethical standards?
• In AI governance reporting to executives, which metric indicates ongoing system performance over time?
• Which documents are essential for AI governance audits?
• Identifies the data and users involved in each step.
• What is the primary purpose of classifying data into categories?
• Which term covers the environment that brings data from Data Lake into notebooks like Jupyter or SageMaker for exploration and training?
• What is privacy by design in AI governance?
• Which professionals are involved in data preprocessing and training and interpret anomalous outputs?
• Which term describes a data repository that aggregates structured and unstructured data while preserving access controls at the data source?
• In AI governance, which approach provides human oversight to mitigate bias and errors in critical decisions?
• Which term describes a branch of artificial intelligence that converts human language into data usable by computer systems?
• What is a key benefit of implementing data lineage in an enterprise?
• What is the role of explainability in AI governance?
• What governance strategies support scaling AI across an enterprise?
• What are the key stages of the AI lifecycle that governance must oversee?
• What is the role of ongoing governance approvals in bias mitigation?
• What issue does data lineage help identify in downstream applications?
• What is the role of incident response in an AI ethics and governance policy?
• Singapore's PDPC Model AI Governance Framework is designed to help organizations do what?
• Which concept encompasses the comprehensive Testing, Evaluation, Verification, and Validation activities for AI systems?
• Which framework aids in enforcing security protocols?
• Which term best describes subject matter experts who oversee the datasets used to train an AI model?
• Which term refers to a platform that enables data exploration and model training via notebooks or managed services?
• In AI governance structures, which role typically funds or champions the project?
• Which category focuses on ethical considerations and societal impact of AI deployment?
• Which regulations are most impactful for AI governance in multinational programs?
• What term describes anomalous patterns that could indicate systematic adversarial testing?
• Which of the following are typical go/no-go criteria for AI deployment gates?
• What criteria are used to prioritize AI initiatives in governance?
• In AI governance, how do policies differ from procedures?
• Representatives from key business units such as operations, marketing, HR are best described as which role?
• Aligning the ethical development, deployment, and use of AI with the enterprise's direction, goals, and objectives aims to...
• How should third-party AI vendors be governed?
• Which concept highlights that poor or biased data can cause incorrect outputs, vulnerabilities, and compliance risks?
• Which domain corresponds to managing data and data governance aspects?
• What governance considerations are essential for AI-related business continuity planning?
• What information should AI program governance reporting to executives include?
• Which concept is used to describe attempts to evaluate AI security by identifying unusual behavior patterns?
• Which of the following are typical governance KPIs for AI initiatives?
• How should stakeholders be categorized in an AI governance program to ensure effective oversight?
• Which approach enables human oversight in critical AI decisions to prevent errors and bias?
• Which practice helps ensure accountability for AI decisions across teams?
• How does governance address ethical risk in AI?
• Which data quality attribute ensures there are no duplicative or redundant records in the dataset?
• What is data provenance and why does it matter in AI governance?
• In AI governance, which action best addresses ethical risk by ensuring ongoing accountability?
• Which practice focuses on data validation, cleaning, and anomaly detection to guard against data poisoning?
• Under the EU AI Act, which assessment is required for high-risk AI solutions?
• What metrics demonstrate AI program ROI beyond financial returns?
• Explainability in AI governance focuses on:
• Which of the following is commonly included as governance indicators for AI models in production?
• Which data quality attribute means data is free from errors and representative of real-world situations?