200 QUESTIONS · ALL 24 WEEKS

Complete question bank

The full 200-question bank covering all 24 weeks. Untimed, comprehensive and intended for deep revision. Questions are original practice material aligned to this course syllabus; they are not copied from, endorsed by or represented as questions from an official certification exam.

WEEK 24 · Controls, operations & judgement · Mock Exam, Capstone Evidence Pack and Final Viva

Which actor role fits 'the organisation putting a system into use under its own authority, without further marketing it'?

DeployerDeveloper onlyAuthorised representativeProvider

WEEK 20 · Controls, operations & judgement · COSO Internal Control Foundations for AI

What is COSO, in one sentence?

A synonym for ISO/IEC 42001A UK data protection regulatorAn AI-specific certification bodyA widely used internal control framework originally built for financial reporting, now applied to AI risk

WEEK 6 · NIST AI RMF · NIST GOVERN

What's a realistic consequence of poor supplier governance?

Supplier governance only affects cost, not riskSupplier governance is optional for SaaS-based AI toolsNo consequence — suppliers are always trustworthyA supplier could change a model with no notice, degrading performance without the organisation knowing

WEEK 16 · ISO AI management · ISO/IEC 42001 Clauses 7–10 and Annex A Controls

What is the purpose of 'monitoring' under Clause 9, distinct from a one-off pre-launch test?

Monitoring replaces the need for internal audit entirelyThere is no distinct purpose — one test is sufficient foreverMonitoring tracks ongoing performance and risk indicators after launch, catching issues that only emerge over time (like drift)Monitoring only applies before a system goes live

WEEK 20 · Controls, operations & judgement · COSO Internal Control Foundations for AI

What does 'AI control maturity' generally describe?

The number of AI systems deployedThe size of the AI teamHow advanced the underlying model architecture isHow consistently, reliably and verifiably an organisation's AI controls actually operate over time

WEEK 3 · Foundations, harm & law · Governance, Risk, Compliance & Assurance Basics

Which best distinguishes governance from risk management?

Governance sets direction and accountability; risk management identifies and treats uncertainty within that directionRisk management is done only by the boardGovernance only applies after an incidentThey are identical activities

WEEK 22 · Controls, operations & judgement · Integrated AI Governance Operating Model

What is the purpose of an 'exceptions and waivers' process?

Waivers remove the need for any future reviewTo let anyone bypass governance permanentlyTo allow a documented, time-limited departure from standard process, with a compensating control and named approverExceptions should never be granted under any circumstance

WEEK 1 · Foundations, harm & law · AI Foundations for Governance

What is a 'foundation model'?

A synonym for a spam filterAny model under 1GB in sizeA large model trained on broad data that can be adapted as a base for many downstream applicationsA model that only works offline

WEEK 14 · ISO AI management · ISO/IEC 42001 Clause 4: Context of the Organisation

Clause 4 requires understanding which two categories of issues?

Internal and external issues affecting the AIMSOnly external issuesOnly issues raised by regulatorsFinancial and marketing issues

WEEK 9 · NIST AI RMF · NIST MANAGE and Risk Treatment

A 'localisation/geographic restriction policy' addresses:

Only applies to hardwareOnly translation qualityRestricting where data or system operation occurs, often for legal or risk reasonsOnly marketing localisation

WEEK 17 · AI law & privacy · EU AI Act Foundations and Global AI Law Landscape

What is a shared theme across South Korea, US state-level and Canadian AI governance approaches, per this course?

No common ground exists at allConvergence around risk-based thinking, transparency and accountabilityAll have adopted the EU AI Act verbatimNone regulate AI in any form

WEEK 11 · GenAI & agent security · Agentic AI, Prompt Injection and AI Security Controls

What defines an 'AI agent' as distinct from a standard chatbot?

An agent can take actions via tools, not just generate text responsesChatbots always use larger models than agentsThere is no meaningful distinctionAgents are always faster than chatbots

WEEK 14 · ISO AI management · ISO/IEC 42001 Clause 4: Context of the Organisation

What is the purpose of 'AIMS processes' as referenced in Clause 4, beyond simply listing issues and parties?

Processes are optional once context is documentedThey have no defined purposeThey are the repeatable processes the organisation puts in place to actually manage AI within the scoped boundary, building on the context establishedAIMS processes only apply after certification

WEEK 4 · Foundations, harm & law · AI and the Wider Legal Landscape

A supplier's model was trained on scraped web content without clear licensing. This primarily raises a concern under:

Only health and safety lawCopyright and training data lawOnly employment lawOnly procurement law

WEEK 19 · AI law & privacy · UK GDPR, DUAA, DPIA and AI Privacy

What does DPIA screening determine?

Whether the model is generative or classic AIWhether the vendor is profitableWhether the processing is likely high risk and needs a full DPIAWhether consent is always required

WEEK 14 · ISO AI management · ISO/IEC 42001 Clause 4: Context of the Organisation

Which is the best way to describe the relationship between Clause 4 scoping work and later Clause 8 operational controls?

They are entirely disconnected activitiesClause 4 scope defines what Clause 8 operational controls actually need to cover — controls should map back to the defined scopeClause 8 controls should be defined before Clause 4 scopeScope has no bearing on which systems need operational controls

WEEK 24 · Controls, operations & judgement · Mock Exam, Capstone Evidence Pack and Final Viva

What's the risk of treating '87% accuracy' as sufficient evidence of fairness in an exam answer?

There is no risk — overall accuracy is always sufficientIt repeats a classic distractor pattern: overall accuracy can hide unequal subgroup performanceAccuracy figures are irrelevant to fairness questions entirelyThis concern only applies to generative AI systems

WEEK 18 · AI law & privacy · EU AI Act High-Risk AI, GPAI, Timeline and Compliance Evidence

Why does 'regulatory evidence preservation' matter even years after a high-risk system was first placed on the market?

Evidence can always be safely discarded after conformity assessment passesRegulators or courts may need to review historical evidence well after initial deployment, particularly following an incident or complaintEvidence preservation obligations only apply during the first month of deploymentThis obligation applies only to prohibited practices

WEEK 8 · NIST AI RMF · NIST MEASURE

What's the risk of setting acceptance criteria only after seeing initial test results?

This is the recommended standard approachNo risk — criteria can always be adjusted freelyCriteria set after seeing results can be unconsciously or deliberately shaped to pass, undermining their purposePost-hoc criteria are always stricter

WEEK 10 · GenAI & agent security · GenAI, RAG and LLM Application Risk

'Source quality' as a RAG risk refers to:

How fast a document loadsWhether the underlying documents being retrieved are accurate, current and trustworthyThe visual design of the source documentOnly the file format used

WEEK 20 · Controls, operations & judgement · COSO Internal Control Foundations for AI

Segregation of duties in AI governance most directly reduces the risk of:

Slower model trainingOne person having unchecked control over both building and approving a systemHigher cloud hosting costsLonger documentation

WEEK 17 · AI law & privacy · EU AI Act Foundations and Global AI Law Landscape

What is an 'authorised representative' under the EU AI Act?

A government regulator roleAn affected person's legal counselAn EU-established entity appointed by a non-EU provider to carry out specified compliance tasksA synonym for a deployer

WEEK 17 · AI law & privacy · EU AI Act Foundations and Global AI Law Landscape

What should drive EU AI Act risk classification first?

Intended purpose, actor role and use contextThe developer's headquarters location aloneWhether the UI looks conversationalModel size in parameters

WEEK 7 · NIST AI RMF · NIST MAP

'Deployment environment' in MAP includes:

Only the programming language usedThe operational, physical and organisational setting in which the system is actually usedOnly the vendor's headquarters locationOnly the marketing channel

WEEK 23 · Controls, operations & judgement · Sector Case Studies and Practitioner Judgement

In financial services AI, what does 'model risk management' specifically address?

Employee satisfaction with financial toolsMarketing effectiveness of financial productsThe risk that a model used in decision-making is flawed, misused or poorly understood, and how that's controlledOnly IT infrastructure uptime

WEEK 18 · AI law & privacy · EU AI Act High-Risk AI, GPAI, Timeline and Compliance Evidence

What does the Act's 'cybersecurity' obligation for high-risk systems primarily address?

Cybersecurity obligations apply only to GPAI models, not high-risk systemsCybersecurity is addressed solely by a separate, unrelated regulation with no AI Act linkPhysical building security onlyProtecting the AI system against attempts to exploit vulnerabilities that could compromise its behaviour or data

WEEK 23 · Controls, operations & judgement · Sector Case Studies and Practitioner Judgement

A recruitment vendor's fairness testing only used labour-market data from one country. What's the practitioner concern if the tool is deployed elsewhere?

This only matters for public-sector recruitmentNo concern — fairness testing is universally generalisableThe testing evidence may not generalise to a different population or labour-market contextTesting location is irrelevant to fairness conclusions

WEEK 20 · Controls, operations & judgement · COSO Internal Control Foundations for AI

A control says data will be 'reviewed periodically'. What's missing to make this a real control?

A defined frequency, named owner, and criteria for what 'reviewed' actually checksThe involvement of external auditorsA synonym for the risk statementNothing — this is sufficiently specific

WEEK 8 · NIST AI RMF · NIST MEASURE

'Human oversight testing' evaluates:

Whether human review is genuinely effective at catching errors and being able to change outcomesOnly staff satisfaction with the toolOnly the number of reviewers employedWhether a human technically exists in the workflow

WEEK 15 · ISO AI management · ISO/IEC 42001 Clause 5 and Clause 6: Leadership and Planning

How does this week's syllabus explicitly frame the alignment between ISO Clause 5/6 and NIST AI RMF?

As explicitly aligned — Clause 5/6 leadership and planning correspond conceptually to NIST's GOVERN and MAP functionsAs mutually exclusive frameworks that cannot be used togetherAs requiring a strict legal hierarchy between the twoAs entirely separate with no acknowledged relationship

WEEK 3 · Foundations, harm & law · Governance, Risk, Compliance & Assurance Basics

Why is 'governance vs management' a common source of confusion in practice?

They are truly identical and confusion is unwarrantedBecause the same people sometimes wear both hats, but the roles — setting direction vs executing decisions — are conceptually distinctBecause governance never actually happens in practiceBecause management only exists in large organisations

WEEK 4 · Foundations, harm & law · AI and the Wider Legal Landscape

'Credit and lending fairness' as a legal concern most directly relates to:

Only data retention periodsOnly marketing contentEqual and fair treatment of applicants in automated credit or lending decisionsOnly cybersecurity practices

WEEK 2 · Foundations, harm & law · Responsible AI, Trustworthy AI & Harm

Which best distinguishes accuracy from fairness?

High overall accuracy can still hide unequal error rates across groupsFairness only matters for generative AIAccuracy always implies fairnessThey mean the same thing

WEEK 3 · Foundations, harm & law · Governance, Risk, Compliance & Assurance Basics

What does 'evidence' mean in a GRC context?

Something inspectable that demonstrates a control operated or a risk was properly assessedOnly relevant to court proceedingsA requirement only for third-party auditsA synonym for opinion

WEEK 23 · Controls, operations & judgement · Sector Case Studies and Practitioner Judgement

An AI triage tool helps decide which emergency department patients are seen first. What's the key practitioner question about its accuracy failures?

Whether false negatives (missing a genuinely urgent case) have been specifically tested and controlled, given the safety consequenceOnly whether average accuracy is above 90%Whether the tool is cheaper than adding staffWhether the interface looks modern

WEEK 21 · Controls, operations & judgement · AI Control Testing, Assurance and Audit Readiness

What should an 'assurance dashboard' primarily show?

Control health, open findings, trend and risk indicators at a glanceOnly the AI model's technical architectureThe org chartMarketing metrics

WEEK 14 · ISO AI management · ISO/IEC 42001 Clause 4: Context of the Organisation

How should 'procured' versus 'internally built' AI systems be treated differently, if at all, in Clause 4 scoping?

Both should generally be considered in scope where the organisation deploys or uses them, since accountability for use doesn't depend on who built the systemOnly internally built systems need governanceProcurement status is irrelevant to any governance questionProcured systems should always be excluded from AIMS scope entirely

WEEK 6 · NIST AI RMF · NIST GOVERN

What is the purpose of 'AI literacy and training' as a GOVERN activity?

Only developers need AI literacyIt's a nice-to-have with no real functionPolicies and controls only work if the people applying them understand the underlying AI riskTraining replaces the need for any policy

WEEK 4 · Foundations, harm & law · AI and the Wider Legal Landscape

'Professional negligence' is most relevant to AI when:

Only when no AI is involved at allOnly applies to unpaid workOnly applies to open-source AIA professional (e.g. adviser) relies on AI output without appropriate care, causing harm through a failure of professional duty

WEEK 23 · Controls, operations & judgement · Sector Case Studies and Practitioner Judgement

Why is 'clinical decision support' distinguished from 'diagnosis' in healthcare AI governance?

Diagnosis tools are always safer than decision-support toolsThis distinction only matters for veterinary AIThere is no meaningful distinctionDecision-support tools inform a clinician's judgement; diagnostic tools may carry different regulatory and liability implications

WEEK 21 · Controls, operations & judgement · AI Control Testing, Assurance and Audit Readiness

What is the difference between a control test and a walkthrough?

Walkthroughs are always more rigorous than testingControl tests never involve inspecting recordsThey are identical activitiesA walkthrough traces one case to confirm the process works as described; a control test usually samples more broadly to assess consistency

WEEK 10 · GenAI & agent security · GenAI, RAG and LLM Application Risk

In LLM application architecture, what's the difference between a system prompt and a user prompt?

The system prompt sets persistent behaviour/instructions set by the application; the user prompt is the live input from the person using itUser prompts are always hidden from the modelSystem prompts are only used in RAG systemsThey are identical and interchangeable

WEEK 10 · GenAI & agent security · GenAI, RAG and LLM Application Risk

'Overreliance' as an LLM risk means:

A synonym for prompt injectionThe model is used too rarelyUsers trusting AI-generated output without appropriate scrutiny or verificationThe model requires too much compute

WEEK 11 · GenAI & agent security · Agentic AI, Prompt Injection and AI Security Controls

Why do 'agent permissions' and 'tool permissions' need to be scoped narrowly?

Narrow scoping has no security benefitLimiting what an agent and its tools can do reduces the impact if the agent behaves unexpectedlyBroader permissions are always saferPermissions only matter for external-facing agents

WEEK 6 · NIST AI RMF · NIST GOVERN

Why does 'AI ownership' need to be distinct from general IT ownership?

AI systems raise specific risks (bias, hallucination, autonomy) that general IT ownership models may not coverIT ownership is always sufficientAI ownership is a redundant conceptAI systems carry no distinct risk from other software

WEEK 11 · GenAI & agent security · Agentic AI, Prompt Injection and AI Security Controls

What is the main risk of an agent being given broad, standing permissions 'just in case' rather than scoped, task-specific ones?

No risk — broad permissions are always more efficientIf manipulated or malfunctioning, the agent can cause far more damage than if permissions were tightly scoped to its actual taskBroad permissions always improve agent accuracyScoped permissions always slow the agent down unacceptably

WEEK 17 · AI law & privacy · EU AI Act Foundations and Global AI Law Landscape

A non-EU company's AI product is never sold in the EU, but its output is used by an EU-based customer who receives services generated by it. Could EU AI Act obligations still be relevant?

This scenario is explicitly exempted by the ActNo, obligations can never extend this wayPotentially yes — market connection can arise through the use of the system's output within the EU, not only through direct sales thereOnly physical hardware sales create market connection

WEEK 23 · Controls, operations & judgement · Sector Case Studies and Practitioner Judgement

Which sector is most associated with statutory 'equality impact' duties in this course's coverage?

Financial servicesInternal copilotsPublic sectorInsurance

WEEK 24 · Controls, operations & judgement · Mock Exam, Capstone Evidence Pack and Final Viva

What should a 'final improvement plan' contain to be useful, rather than a generic statement?

A restatement of the entire syllabusNothing, since the programme has already concludedA vague intention to 'know more about AI governance'A specific capability gap, a concrete action to close it, and a target date

WEEK 19 · AI law & privacy · UK GDPR, DUAA, DPIA and AI Privacy

Why are 'vulnerable groups' given specific attention in AI and privacy guidance?

Only children count as a vulnerable group under any circumstancesVulnerability is irrelevant to privacy risk assessmentVulnerable groups require no special consideration under UK GDPRThey may be less able to understand, exercise rights over, or challenge how an AI system processes their data, warranting extra protective consideration

WEEK 20 · Controls, operations & judgement · COSO Internal Control Foundations for AI

How should 'information and communication' differ for staff versus the board?

It shouldn't differ at allStaff typically need operational detail; the board needs a summarised risk and decision viewThe board needs more raw technical detail than staffOnly staff need any information at all

WEEK 13 · ISO AI management · ISO/IEC 42001 Foundations

What's the difference between certification and implementation?

They're identicalImplementation is building the AIMS practices; certification is an optional external verification against the standardCertification must happen before any implementation workCertification guarantees fair AI outcomes

WEEK 4 · Foundations, harm & law · AI and the Wider Legal Landscape

An AI product giving unsafe advice (e.g. on medication dosage) primarily engages which combination of legal domains?

Only database rightsProduct liability and health and safety law togetherOnly trade secretsOnly procurement law

WEEK 1 · Foundations, harm & law · AI Foundations for Governance

Why does AI governance exist at all?

To slow down technology adoption for its own sakeTo manage the risks and responsibilities AI systems create for people and organisationsBecause all AI is illegal without itOnly to satisfy IT departments

WEEK 9 · NIST AI RMF · NIST MANAGE and Risk Treatment

Why might 'geographic restriction' be a more proportionate response than full deactivation?

Geographic restriction and deactivation are the same actionGeographic restriction is never a valid optionIt can address a region-specific legal or risk issue without disrupting service where the issue doesn't applyFull deactivation is always the only valid response to any issue

WEEK 16 · ISO AI management · ISO/IEC 42001 Clauses 7–10 and Annex A Controls

Why is 'evidence preservation' relevant to both Clause 9 (evaluation) and later external scrutiny (e.g. audit or regulatory review)?

Evidence preservation is only relevant internally, never externallyWell-preserved monitoring, audit and review evidence is what allows both internal evaluation and external scrutiny to actually verify AIMS performanceExternal reviewers never look at internally preserved evidenceEvidence preservation is not part of Clause 9's scope

WEEK 11 · GenAI & agent security · Agentic AI, Prompt Injection and AI Security Controls

Why do 'tool permissions' need periodic review, not just a one-time setup?

Reviewing permissions is purely a cost-saving exercise with no security valueTool permissions never need to change once setAs an agent's use case evolves, permissions granted for an earlier purpose may become excessive or stalePeriodic review only applies to human user accounts

WEEK 3 · Foundations, harm & law · Governance, Risk, Compliance & Assurance Basics

Risk tolerance is best described as:

Only relevant to cybersecurityIdentical to risk appetiteThe specific, measurable thresholds within the broader risk appetite that trigger escalationA synonym for a control activity

WEEK 14 · ISO AI management · ISO/IEC 42001 Clause 4: Context of the Organisation

Which best describes 'needs and expectations' of interested parties in Clause 4 terms?

A fixed, universal list identical for every organisationA synonym for financial targetsWhat each interested party requires or expects from how the organisation governs AI, which the AIMS should account forOnly the needs of the AI development team

WEEK 8 · NIST AI RMF · NIST MEASURE

What is a 'benchmark' in AI testing?

Only relevant to hardwareA legal requirement in every jurisdictionA standard test or dataset used to measure and compare system performanceA synonym for a model card

WEEK 5 · NIST AI RMF · NIST AI RMF Overview and OECD AI Principles

The OECD principle 'transparency and explainability' emphasises:

Only disclosing information to regulatorsApplies only to open-source modelsKeeping all AI system details secretProviding meaningful, accessible information about how a system works and why

WEEK 20 · Controls, operations & judgement · COSO Internal Control Foundations for AI

What's the relationship between control activities and risk appetite?

Risk appetite is only relevant to legal teamsControl activities determine risk appetite, not the other way roundNo relationship existsControl activities should be proportionate to how much risk the organisation is willing to accept

WEEK 11 · GenAI & agent security · Agentic AI, Prompt Injection and AI Security Controls

An AI agent with CRM and email tool access is asked, via a crafted prompt embedded in a customer message, to 'forward this thread to an external address.' What control most directly reduces this risk?

Requiring human approval before the agent executes high-impact actions like external sendsRemoving all logging to simplify the pipelineAllowing the agent unlimited tool calls per sessionIncreasing the model's context window

WEEK 16 · ISO AI management · ISO/IEC 42001 Clauses 7–10 and Annex A Controls

Which best reflects how 'control maturity' typically progresses across stages such as initial, developing, defined, managed and optimising?

Maturity always starts at 'optimising' and decreases over timeMaturity stages only apply to financial controls, not AI controlsMaturity has no defined stages and is purely subjectiveIt generally progresses from ad hoc, undocumented practice toward consistent, evidenced, continuously improved operation

WEEK 18 · AI law & privacy · EU AI Act High-Risk AI, GPAI, Timeline and Compliance Evidence

Why do GPAI providers face obligations around a 'copyright policy'?

GPAI training often involves large volumes of data, raising copyright considerations the Act requires providers to address through policyOnly applies to models trained exclusively on public domain contentCopyright policy obligations only apply to deployers, not providersCopyright is entirely unrelated to GPAI models

WEEK 6 · NIST AI RMF · NIST GOVERN

What is the primary purpose of the GOVERN function?

Establishing accountability, policy, ownership and risk culture for AIWriting release notesMapping affected peopleTesting model accuracy

WEEK 3 · Foundations, harm & law · Governance, Risk, Compliance & Assurance Basics

What is the 'second line' in the three lines of defence model?

External regulatorsA synonym for internal auditThe team that owns and operates the AI systemRisk, compliance and similar functions providing oversight and challenge to the first line

WEEK 9 · NIST AI RMF · NIST MANAGE and Risk Treatment

How should a confirmed incident feed back into MANAGE's broader risk treatment process?

Incidents only need to be reported to affected customers, nothing elseIncident learnings are only relevant to the specific engineer involvedIncidents should be resolved and then forgotten with no wider follow-upLessons from the incident should inform whether existing treatment, controls or monitoring need to be strengthened

WEEK 14 · ISO AI management · ISO/IEC 42001 Clause 4: Context of the Organisation

Why are affected people treated as interested parties rather than just 'users'?

They aren't relevant to AIMS scopeThey experience consequences from the AI system even if they never interact with it directlyOnly paying customers count as interested partiesAffected people are the same as suppliers

WEEK 22 · Controls, operations & judgement · Integrated AI Governance Operating Model

What belongs in the 'retirement' stage of the AI lifecycle?

Nothing further once a system stops being usedData handling, supplier exit, evidence retention and communication to affected people, as relevantImmediate deletion of all records with no retentionTransferring accountability to the AI system itself

WEEK 2 · Foundations, harm & law · Responsible AI, Trustworthy AI & Harm

An umbrella (protection against a likely, low-severity event — rain) is often used to illustrate which risk concept?

That treatment should match the probability and severity of the specific risk, not be one-size-fits-allThat all risks should always be acceptedData minimisationRisk transfer only

WEEK 2 · Foundations, harm & law · Responsible AI, Trustworthy AI & Harm

A model quietly moving from 'decision support' to being treated as an automatic decision-maker illustrates a bias/harm risk at which lifecycle stage?

Data and labelsDeployment and useModel architecture selectionNone — this isn't a risk

WEEK 24 · Controls, operations & judgement · Mock Exam, Capstone Evidence Pack and Final Viva

Why should review time be deliberately reserved at the end of a timed exam, not just used for the last question?

Review time should be used to change already-confident correct answersTimed exams don't allow any review by designReserved review time is never usefulIt allows catching skipped questions, inconsistent answers or rushed reasoning before time runs out

WEEK 13 · ISO AI management · ISO/IEC 42001 Foundations

What does 'AIMS' stand for and mean?

AI Management System — a structured, repeatable way to govern AI responsiblyAutomated Inference Monitoring ServiceA synonym for a risk registerAI Marketing System

WEEK 1 · Foundations, harm & law · AI Foundations for Governance

Who is the 'provider' in the AI actor model?

The organisation that develops or places the system on the market under its nameOnly the end userOnly a data supplierThe person affected by the system's output

WEEK 17 · AI law & privacy · EU AI Act Foundations and Global AI Law Landscape

Why does US AI governance activity, per this course, include both federal and state-level components?

AI governance in the US has developed through a mix of federal initiatives and a growing patchwork of individual state lawsOnly federal law is relevant; states have no role in AI regulationUS states are legally barred from regulating AIThe US has no AI-specific governance activity at any level

WEEK 10 · GenAI & agent security · GenAI, RAG and LLM Application Risk

Why might an outdated document being used in a chatbot's answer be worse than the chatbot admitting it doesn't know?

It isn't worse — outdated confident answers are always preferableA confidently outdated answer can mislead a user into acting on wrong information without any signal that verification is neededUsers always check the document date themselvesStale information is never actually a real risk

WEEK 23 · Controls, operations & judgement · Sector Case Studies and Practitioner Judgement

Why does 'access control' matter specifically for enterprise RAG copilots?

Access control is irrelevant if the model is proprietaryWithout proper filtering, a copilot could surface documents to staff who shouldn't have access to themAccess control only matters for external-facing systemsRAG systems have no access to internal documents by design

WEEK 9 · NIST AI RMF · NIST MANAGE and Risk Treatment

What is a realistic early-warning 'risk indicator' MANAGE might monitor after release?

Only the company's stock priceA rising override or exception rate that could signal declining model reliabilityOnly office attendance figuresOnly marketing engagement metrics

WEEK 9 · NIST AI RMF · NIST MANAGE and Risk Treatment

'Release gates' in MANAGE function as:

Only relevant to hardware releasesA synonym for a risk register entryOptional suggestions with no real effectDefined checkpoints that must be passed before a system can be released or scaled

WEEK 22 · Controls, operations & judgement · Integrated AI Governance Operating Model

Which trigger would most likely require a DPIA screening at intake?

The system has no connection to any personal dataThe system processes no personal data at allThe system profiles individuals at scale for a decision with potential significant effectThe system is only used internally by two people

WEEK 18 · AI law & privacy · EU AI Act High-Risk AI, GPAI, Timeline and Compliance Evidence

Which is a provider obligation for a high-risk system, distinct from a deployer obligation?

Providing deployer-specific input dataUsing the system per instructionsEstablishing a risk management system and technical documentation before market placementMonitoring within the deployer's own organisation

WEEK 13 · ISO AI management · ISO/IEC 42001 Foundations

What is the risk of an organisation writing an AI policy that simply copies another company's publicly available policy verbatim?

This is explicitly recommended by the standardVerbatim copying guarantees certification successNo risk — policies are meant to be genericIt likely won't reflect the organisation's actual context, risks and AI use, undermining its usefulness and its Clause 5 intent

WEEK 17 · AI law & privacy · EU AI Act Foundations and Global AI Law Landscape

Which best reflects why 'scope' matters as a foundational EU AI Act concept before classification questions?

Classification should always happen before considering scopeScope and classification are the same questionScope is irrelevant until obligations are being fulfilledWhether the Act applies at all (e.g. via market connection) must be established before spending effort on detailed risk classification

WEEK 4 · Foundations, harm & law · AI and the Wider Legal Landscape

Why does this course emphasise that 'AI law is broader than privacy'?

Because other legal domains are always less important than privacyBecause privacy law is the only relevant domain in practiceBecause IP, equality, consumer, contract, liability and sector law can all independently create AI risk, beyond data protectionBecause privacy law doesn't apply to AI at all

WEEK 12 · ISO AI management · ISO AI Standards: ISO/IEC 22989, ISO/IEC 42001 and ISO/IEC 42005

Why does 'how ISO 42001 supports governance system design' matter for a multi-site organisation?

It doesn't — every site should govern independently with no shared structureIt provides a common structure so different sites or business units can be governed consistently rather than ad hocIt only applies to single-site organisationsIt replaces the need for site-level ownership

WEEK 16 · ISO AI management · ISO/IEC 42001 Clauses 7–10 and Annex A Controls

Clause 7 (Support) primarily covers:

Resources, competence, awareness, communication and documented informationOnly IT infrastructureExternal auditsFinancial reporting

WEEK 19 · AI law & privacy · UK GDPR, DUAA, DPIA and AI Privacy

Which lawful basis is most likely appropriate for processing necessary to decide whether to enter a loan contract a customer has requested?

ContractPublic taskLegitimate interests only, never contractConsent

WEEK 24 · Controls, operations & judgement · Mock Exam, Capstone Evidence Pack and Final Viva

What should a well-written approval recommendation include?

No reasoning, since the decision speaks for itselfOnly the word 'approve' or 'block'A clear decision, the reasoning tied to evidence, conditions if applicable, and the accountable ownerThe longest possible list of every risk ever considered

WEEK 4 · Foundations, harm & law · AI and the Wider Legal Landscape

Why does 'record keeping' matter as a distinct legal concern for AI systems?

Certain legal and regulatory obligations require retaining specific records or evidence of decisions and processesRecord keeping is purely a matter of internal preference with no external requirement everOnly applies to financial services firmsRecords are never legally relevant

WEEK 21 · Controls, operations & judgement · AI Control Testing, Assurance and Audit Readiness

What does 'inspection' mean as an evidence-gathering method?

Watching someone perform a taskAsking someone a questionExamining records, documents or system configurations directlyRedoing the control yourself

WEEK 19 · AI law & privacy · UK GDPR, DUAA, DPIA and AI Privacy

What would a 'right of access' request typically entitle someone to, regarding an AI-scored decision about them?

Confirmation that their data is being processed and access to relevant information about that processing, including relevant automated decision-making detailsOnly the right to complain to a regulator, not to see their own dataThe right to delete the AI model itselfNothing related to the AI processing

WEEK 17 · AI law & privacy · EU AI Act Foundations and Global AI Law Landscape

A retailer in the supply chain makes a high-risk AI system available on the EU market without being the original provider or importer. Which role does this describe?

Affected personProduct manufacturerDeployerDistributor

WEEK 24 · Controls, operations & judgement · Mock Exam, Capstone Evidence Pack and Final Viva

What's the best first step when reading a long scenario question?

Count the number of words in the questionSkip directly to the explanation if unsureImmediately pick an answer that sounds confidentIdentify the actual decision or judgement being tested before evaluating the options

WEEK 18 · AI law & privacy · EU AI Act High-Risk AI, GPAI, Timeline and Compliance Evidence

Under the updated timeline enacted via the EU's 'Digital Omnibus' in 2026, when do Annex III high-risk use case obligations generally apply?

2 August 2026 as originally planned, unchanged2 December 20272 August 2028There is no defined date

WEEK 10 · GenAI & agent security · GenAI, RAG and LLM Application Risk

Between a small, single-purpose model and a large, general-purpose model, which factor most matters for a narrow, well-defined task?

A smaller, purpose-built model can often perform as well or better on a narrow task with lower cost and easier evaluationSmall models cannot be evaluated for accuracyLarge models are always cheaper to runAlways choose the largest model regardless of task

WEEK 19 · AI law & privacy · UK GDPR, DUAA, DPIA and AI Privacy

What should a plain-language AI privacy notice for an automated decision include?

The model's source codeA guarantee of accuracyOnly the vendor's nameRelevant data use, the decision's effect, safeguards and how to challenge it

WEEK 11 · GenAI & agent security · Agentic AI, Prompt Injection and AI Security Controls

What does an 'audit trail' for an agent enable that a simple output log does not?

Audit trails only apply to financial transactionsAudit trails replace the need for monitoringNothing additionalReconstructing the full sequence of an agent's decisions and actions, supporting investigation after an incident

WEEK 19 · AI law & privacy · UK GDPR, DUAA, DPIA and AI Privacy

How does 'fairness' as a UK GDPR principle differ from simply having a lawful basis for processing?

Only lawfulness matters; fairness is not a separate UK GDPR principleThey are identical requirementsFairness concerns whether processing is reasonably expected and not unduly detrimental to the individual, which is a distinct question from whether a lawful basis technically existsFairness has no independent meaning beyond lawfulness

100

WEEK 22 · Controls, operations & judgement · Integrated AI Governance Operating Model

Which vendor contract term most directly addresses the supplier's right to inspect or verify compliance claims?

Export restrictionsCompliance supportIndemnityAudit rights

101

WEEK 4 · Foundations, harm & law · AI and the Wider Legal Landscape

Sector regulation for a financial services AI tool would most likely add requirements around:

Only physical workplace safetyOnly employment contractsOnly general marketing practices with no sector specificityFinancial-services-specific obligations, such as fair lending or model risk management practices

102

WEEK 5 · NIST AI RMF · NIST AI RMF Overview and OECD AI Principles

Why might an organisation use NIST AI RMF even where it isn't legally required?

It provides a structured, credible way to manage AI risk that can also support other legal obligationsIt is required to use any AI system anywhereIt replaces the need for any risk assessmentIt has no practical benefit

103

WEEK 16 · ISO AI management · ISO/IEC 42001 Clauses 7–10 and Annex A Controls

Relying entirely on a supplier's unverified claim of 'no bias' is a gap in which Annex A theme?

Customer relationshipsThird-party relationshipsResources for AI systemsAI policies

104

WEEK 17 · AI law & privacy · EU AI Act Foundations and Global AI Law Landscape

Which best describes Canada's general direction in AI governance, as covered by this course's global landscape material?

Its own developing direction for AI governance, distinct from but thematically related to other jurisdictions covered in this courseAn exact adoption of the EU's risk-tier categoriesA framework limited exclusively to financial services AINo engagement with AI governance at all

105

WEEK 16 · ISO AI management · ISO/IEC 42001 Clauses 7–10 and Annex A Controls

Which is a practical example of 'documented information' required under Clause 7 for an operating AI system?

Documented information is not actually required under Clause 7A verbal agreement with no written recordA recorded risk assessment, control evidence log, or training record that can be inspected laterOnly the original sales brochure from the AI vendor

106

WEEK 22 · Controls, operations & judgement · Integrated AI Governance Operating Model

What's a reasonable trigger for a 'security review' at intake?

The system integrates with external tools, APIs or handles sensitive dataSecurity review is only needed after a breachSecurity review only applies to open-source systemsThe system has no external connections and processes no sensitive data

107

WEEK 15 · ISO AI management · ISO/IEC 42001 Clause 5 and Clause 6: Leadership and Planning

Which is a leadership responsibility under Clause 5, not a purely operational task?

Setting AI policy and assigning roles, authorities and resourcesWriting the model's codeResponding to individual support ticketsManually reviewing every AI output

108

WEEK 8 · NIST AI RMF · NIST MEASURE

What is the difference between a false positive and a false negative in a fraud-detection model, and why does MEASURE care about the distinction?

Only false positives matter for testingThe distinction is irrelevant to acceptance criteriaThey are the same error typeA false positive wrongly flags a legitimate case; a false negative misses a real one — the acceptable balance between them should be a deliberate, tested decision

109

WEEK 14 · ISO AI management · ISO/IEC 42001 Clause 4: Context of the Organisation

Why is 'documenting scope' considered a defensibility issue, not just an administrative one?

Documentation is purely for internal filing purposes with no external relevanceScope documentation is optional under Clause 4Defensibility has nothing to do with documentationA documented, reasoned scope can be explained and justified to auditors, regulators or affected people; an undocumented one cannot

110

WEEK 21 · Controls, operations & judgement · AI Control Testing, Assurance and Audit Readiness

A control's override log exists but has never been reviewed. What does this most likely indicate?

A detective control exists on paper but isn't operating as intended, since no one is acting on itThis is not a control weaknessThe override log should be deletedThe control is fully effective

111

WEEK 2 · Foundations, harm & law · Responsible AI, Trustworthy AI & Harm

The 'green apple bias' example is typically used to illustrate:

A pricing algorithm errorA cybersecurity vulnerabilityA copyright issueA model performing worse on cases that look different from its dominant training examples

112

WEEK 21 · Controls, operations & judgement · AI Control Testing, Assurance and Audit Readiness

Which best distinguishes design effectiveness from operating effectiveness?

They are the same testDesign asks if the control could work; operating asks if it actually did work consistentlyOperating effectiveness is assessed before designDesign effectiveness only applies to automated controls

113

WEEK 22 · Controls, operations & judgement · Integrated AI Governance Operating Model

What's the difference between 'risk acceptance' and simply ignoring a risk?

They are the same thing in practiceRisk acceptance is a deliberate, documented decision by an authorised role; ignoring is an absence of any decisionIgnoring a risk is always the safer optionRisk acceptance means the risk no longer exists

114

WEEK 7 · NIST AI RMF · NIST MAP

'External dependencies' in MAP is broader than 'supplier dependencies' because it also includes:

Other environmental factors like data sources, infrastructure or partner systems the AI system relies onOnly competitor productsOnly regulatory bodiesNothing broader — they are identical

115

WEEK 9 · NIST AI RMF · NIST MANAGE and Risk Treatment

What is the primary purpose of the MANAGE function?

Prioritising risk and deciding treatment, release controls and monitoringWriting test scriptsBoard induction trainingDefining intended use

116

WEEK 16 · ISO AI management · ISO/IEC 42001 Clauses 7–10 and Annex A Controls

Why would 'supplier controls' appear within Clause 8 operation rather than only in Annex A's third-party theme?

Because managing suppliers is an ongoing operational activity (not just a one-time control selection), it belongs in Clause 8's operational scope tooClause 8 and Annex A never overlap in subject matterSupplier controls are exclusively a Clause 5 leadership matterSupplier controls have no place in Clause 8 at all

117

WEEK 5 · NIST AI RMF · NIST AI RMF Overview and OECD AI Principles

Which of these is one of NIST's listed trustworthy AI characteristics?

Cheapest to operateProfitableValid and reliableFastest to market

118

WEEK 22 · Controls, operations & judgement · Integrated AI Governance Operating Model

What's a key governance trade-off between hosted and self-hosted deployment?

Self-hosting always eliminates the need for security controlsHosted deployment is always illegal under UK GDPRNo meaningful trade-off existsHosted deployment can mean less operational burden but less control over data location; self-hosting reverses that

119

WEEK 22 · Controls, operations & judgement · Integrated AI Governance Operating Model

What is the purpose of an integrated AI governance lifecycle?

To slow down every AI project equallyTo connect intake, risk tiering, specialist review, release, monitoring and retirement into one coherent processTo replace the need for any specialist legal or privacy reviewTo apply only to generative AI systems

120

WEEK 15 · ISO AI management · ISO/IEC 42001 Clause 5 and Clause 6: Leadership and Planning

How should planning under Clause 6 treat a newly identified AI risk discovered mid-cycle, rather than at the original planning point?

Only Clause 9 can introduce new risks into planningIt should be ignored until the next full planning cycleThe 'planning changes' element of Clause 6 allows for controlled incorporation of newly identified risks outside the original planning cycleNew risks can never be added once a planning cycle starts

121

WEEK 7 · NIST AI RMF · NIST MAP

Which best reflects the relationship between MAP and MEASURE?

MAP replaces the need for MEASUREThey are unrelated and sequential-only with no feedbackMAP identifies what to test for; MEASURE then tests against that mapped context and riskMEASURE happens before MAP in every case

122

WEEK 18 · AI law & privacy · EU AI Act High-Risk AI, GPAI, Timeline and Compliance Evidence

Why does the Act distinguish 'training data', 'validation data' and 'testing data' as separate governance concerns?

Testing data requirements were removed by the 2026 delayThe distinction is purely academic with no governance relevanceEach serves a different role in building and verifying a model, so data-quality and representativeness obligations need to be considered for each separatelyOnly training data is ever relevant to compliance

123

WEEK 5 · NIST AI RMF · NIST AI RMF Overview and OECD AI Principles

A leader says: 'We just follow the law, we don't need frameworks.' What's the issue with this view?

There is no issue; law is always sufficientLaws often set outcomes without prescribing how to achieve them; frameworks give practical structure to meet those outcomesFrameworks are illegal to use alongside lawFrameworks always conflict with legal requirements

124

WEEK 21 · Controls, operations & judgement · AI Control Testing, Assurance and Audit Readiness

What should a management action plan for a finding always include?

Only the severity ratingThe auditor's personal opinionOnly a general statement of intentA specific action, an accountable owner, and a completion date

125

WEEK 2 · Foundations, harm & law · Responsible AI, Trustworthy AI & Harm

An 84% 'overall accuracy' figure for a learner engagement score says what about fairness across student groups?

Accuracy and fairness are unrelated concepts entirelyIt proves the system is fair to all groupsNothing on its own — subgroup performance must be separately testedIt proves the system is unfair

126

WEEK 15 · ISO AI management · ISO/IEC 42001 Clause 5 and Clause 6: Leadership and Planning

Which best distinguishes the role of an AI governance board from an operational AI ethics working group, where both exist?

They are always the same body under a different nameThe governance board typically holds ultimate oversight and approval authority; a working group may develop recommendations and detailed guidance for the board's considerationThe working group always outranks the governance boardOnly one of the two bodies is ever needed, never both

127

WEEK 6 · NIST AI RMF · NIST GOVERN

What distinguishes 'model ownership' from 'product ownership' in AI governance?

They are identical rolesModel ownership concerns the technical model itself; product ownership concerns the overall business offering built on itProduct ownership only applies to hardwareModel ownership is always more senior

128

WEEK 5 · NIST AI RMF · NIST AI RMF Overview and OECD AI Principles

What does 'NIST' stand for?

National Institute of Standards and TechnologyNational Information Security TaskforceNew International Standards TrustNational Institute for Software Testing

129

WEEK 23 · Controls, operations & judgement · Sector Case Studies and Practitioner Judgement

A hospital AI tool recommends a treatment priority but a clinician always follows it without independent judgement. What sector-specific issue does this raise?

This is a purely IT infrastructure issueThis only matters if the hospital is publicly fundedNo issue — following AI recommendations is always appropriateA patient-safety and decision-support boundary issue, since the tool may be functioning as a de facto diagnostic decision-maker

130

WEEK 23 · Controls, operations & judgement · Sector Case Studies and Practitioner Judgement

Which two sectors in this week's coverage most explicitly require attention to how automated decisions affect vulnerable or differently-situated groups?

Internal copilots and recruitment onlyInsurance and public sectorOnly financial servicesOnly healthcare

131

WEEK 3 · Foundations, harm & law · Governance, Risk, Compliance & Assurance Basics

What is an audit trail for, in practical terms?

To make reports longerTo let someone reconstruct what decision was made, by whom, on what evidenceOnly required for financial systemsA synonym for a risk register

132

WEEK 1 · Foundations, harm & law · AI Foundations for Governance

Which best orders these from broadest to narrowest?

AI > machine learning > deep learning > generative AIGenerative AI > AI > machine learningDeep learning > AI > generative AIMachine learning > AI > deep learning

133

WEEK 19 · AI law & privacy · UK GDPR, DUAA, DPIA and AI Privacy

Why is 'human review vs rubber-stamping' a recurring theme across both ISO/IEC 42001 oversight controls and UK GDPR ADM safeguards?

Only ISO 42001 addresses this concern; GDPR does notIt's coincidental and has no shared underlying principleBoth frameworks recognise that nominal human presence without real engagement fails to provide genuine protection against automated harmGDPR and ISO 42001 define 'meaningful review' completely differently with no overlap

134

WEEK 5 · NIST AI RMF · NIST AI RMF Overview and OECD AI Principles

Which RMF function establishes intended use, context and risk scenarios?

GovernMapMeasureManage

135

WEEK 7 · NIST AI RMF · NIST MAP

What does MAP's 'context' work primarily protect against?

Model training costsMarketing miscommunication onlyNothing — it's a formalityBuilding controls and tests for the wrong problem because the system's real use and risk weren't properly understood first

136

WEEK 12 · ISO AI management · ISO AI Standards: ISO/IEC 22989, ISO/IEC 42001 and ISO/IEC 42005

A company documents its AIMS scope, policy and objectives but has never run an impact assessment on any deployed system. Which standard's practice is missing?

None — impact assessment isn't part of any of these standardsISO/IEC 22989ISO/IEC 42001ISO/IEC 42005

137

WEEK 24 · Controls, operations & judgement · Mock Exam, Capstone Evidence Pack and Final Viva

Why does an evidence-pack index need to state each artefact's relevance to the final decision, not just its title?

This only matters for artefacts marked 'incomplete'Relevance statements are unnecessary paddingIt shows the reviewer why each artefact matters to the actual conclusion, not just that it existsTitles alone are always sufficient for an index

138

WEEK 14 · ISO AI management · ISO/IEC 42001 Clause 4: Context of the Organisation

How should 'business context' under Clause 4 reflect a new strategic AI initiative the organisation is about to launch?

Business context is fixed once and never updatedStrategic initiatives are outside Clause 4's remit entirelyIt should be ignored until after launchContext documentation should be updated to reflect new AI-related strategic initiatives as they emerge, keeping scope current

139

WEEK 1 · Foundations, harm & law · AI Foundations for Governance

Which best distinguishes a hosted open-source model from a paid vendor API model?

Vendor APIs are always cheaperOpen-source models cannot be evaluated for biasOpen-source hosting gives more control and data residency but more operational responsibility; vendor APIs offload infrastructure but reduce controlThey are functionally identical in every respect

140

WEEK 18 · AI law & privacy · EU AI Act High-Risk AI, GPAI, Timeline and Compliance Evidence

Which best reflects the relationship between 'quality management system' (QMS) obligations and technical documentation?

They are unrelated requirementsA QMS provides the organisational process discipline that supports consistently producing and maintaining accurate technical documentation and compliance evidenceTechnical documentation replaces the need for a QMS entirelyQMS obligations only apply to GPAI providers

141

WEEK 12 · ISO AI management · ISO AI Standards: ISO/IEC 22989, ISO/IEC 42001 and ISO/IEC 42005

What does 'AI actor' terminology, as covered under 22989-style vocabulary, help clarify?

Only who owns the intellectual propertyOnly the identity of the end customerNothing useful for governanceWho is responsible at each stage — developer, provider, deployer, user, affected person — reducing accountability confusion

142

WEEK 6 · NIST AI RMF · NIST GOVERN

Which is the best example of a 'developer' role, distinct from 'provider'?

The organisation marketing the finished AI product to customersThe team or organisation that builds the underlying model or system componentsThe end customer using the toolThe regulator overseeing the sector

143

WEEK 18 · AI law & privacy · EU AI Act High-Risk AI, GPAI, Timeline and Compliance Evidence

Why does timely 'serious incident reporting' matter beyond simply satisfying a legal box-ticking requirement?

Prompt reporting enables faster investigation, containment and potentially prevents recurrence or wider harmIncident reporting timelines were removed by the 2026 delaySerious incidents only need to be reported after a full year has passedTiming has no real consequence

144

WEEK 6 · NIST AI RMF · NIST GOVERN

In the developer/provider/deployer/user responsibility mapping, who is the 'deployer'?

Only the affected personThe software vendor's marketing teamThe person who trains the original modelThe organisation putting the AI system into use within its own operations

145

WEEK 10 · GenAI & agent security · GenAI, RAG and LLM Application Risk

A cited source doesn't actually support the claim made in the answer. This is a failure of:

Database rightsPurpose limitationData minimisationGrounding and citation traceability

146

WEEK 10 · GenAI & agent security · GenAI, RAG and LLM Application Risk

A vector database returns technically similar but contextually wrong content for a query. What RAG risk does this best illustrate?

Toxic outputFine-tuning failureAccess-control failureIncorrect retrieval, where similarity doesn't guarantee true relevance

147

WEEK 2 · Foundations, harm & law · Responsible AI, Trustworthy AI & Harm

When should a harm block a launch?

Never — harms are always accepted with monitoringWhen it is severe and/or probable enough that available controls can't reduce it to an acceptable levelOnly if a regulator has already fined someone for itOnly for generative AI systems

148

WEEK 7 · NIST AI RMF · NIST MAP

Why does higher autonomy typically require more attention in MAP?

Autonomy only matters for hardware robotsIt doesn't — autonomy has no bearing on riskGreater independent action increases the consequence of an error occurring without a human catching itHigher autonomy always means lower risk

149

WEEK 13 · ISO AI management · ISO/IEC 42001 Foundations

What is the role of internal audit within an AIMS?

It only applies once certification has been achievedTo replace management's own responsibility for AI riskTo independently check whether the AIMS is being followed and is effective, feeding into management reviewIt's optional and rarely relevant

150

WEEK 20 · Controls, operations & judgement · COSO Internal Control Foundations for AI

Why do control gaps need a severity rating, not just a list?

Severity is only relevant for financial controlsSeverity ratings are decorativeSeverity helps prioritise limited remediation resources toward what matters mostAll gaps are equally urgent by definition

151

WEEK 2 · Foundations, harm & law · Responsible AI, Trustworthy AI & Harm

What is the relationship between transparency and explainability?

Transparency only concerns pricingThey are identical conceptsTransparency is about making information about a system available; explainability is about understanding why a specific output occurredExplainability only applies to generative AI

152

WEEK 19 · AI law & privacy · UK GDPR, DUAA, DPIA and AI Privacy

Which of these is a 'special category' of data under UK GDPR?

A person's postcodeData concerning healthA person's job titleA person's employer name

153

WEEK 20 · Controls, operations & judgement · COSO Internal Control Foundations for AI

What role does 'management oversight' play in an AI control environment?

Oversight only applies after a regulatory fineNone — controls run themselvesManagement is responsible for ensuring controls are resourced, monitored and acted uponOnly the board needs to be involved, never middle management

154

WEEK 5 · NIST AI RMF · NIST AI RMF Overview and OECD AI Principles

What does the OECD AI Principles' relationship to human rights and democratic values mean in practice?

This principle has no operational meaningAI must never be used in democraciesAI systems should be designed and used consistent with fundamental rights and democratic norms, not just efficiencyOnly government AI systems need to respect human rights

155

WEEK 5 · NIST AI RMF · NIST AI RMF Overview and OECD AI Principles

Which best describes the combination of using both OECD Principles and NIST AI RMF together?

They contradict each other on every pointOnly one can be legally referenced at a timeRedundant and should never be combinedOECD gives the values-based direction; NIST gives the practical risk-management structure to act on them

156

WEEK 24 · Controls, operations & judgement · Mock Exam, Capstone Evidence Pack and Final Viva

When classifying a scenario under the EU AI Act, what should a strong answer state alongside the conclusion?

Nothing — a bare classification label is sufficientThe key assumption or fact the classification depends on, and any residual uncertaintyA guarantee of complete legal certaintyOnly the technology used, not the use case

157

WEEK 7 · NIST AI RMF · NIST MAP

What is the primary purpose of the MAP function?

Establishing context: intended use, affected people, misuse and risk scenariosBoard reportingSetting risk appetite policyWriting release evidence

158

WEEK 2 · Foundations, harm & law · Responsible AI, Trustworthy AI & Harm

Why might 'monitor' be an acceptable treatment for a low-probability, low-severity harm, when it wouldn't be for a high-severity one?

All harms require the same treatment regardless of severityMonitoring replaces the need to ever assess severityMonitoring is never an acceptable treatment for any harmTreatment should be proportionate to the risk level; lower risks can sometimes be managed with monitoring alone

159

WEEK 8 · NIST AI RMF · NIST MEASURE

How does a 'system card' differ from a 'model card'?

System cards only apply to hardwareModel cards replace the need for testingThey are identical documentsA system card covers the broader AI system (architecture, data, safeguards); a model card focuses on a specific model

160

WEEK 20 · Controls, operations & judgement · COSO Internal Control Foundations for AI

What best describes 'control environment' in COSO terms?

The tone, culture, accountability structures and competence that underpin all other controlsA synonym for a firewallThe physical office layoutThe server infrastructure hosting the model

161

WEEK 22 · Controls, operations & judgement · Integrated AI Governance Operating Model

What is an 'external communication plan' for AI incidents meant to define?

Who is told what, when, by whom, and how uncertainty is acknowledged if a real-world impact occursOnly the internal engineering responseA synonym for the model cardNothing — communication should be improvised

162

WEEK 12 · ISO AI management · ISO AI Standards: ISO/IEC 22989, ISO/IEC 42001 and ISO/IEC 42005

How does ISO/IEC 42005 relate to NIST AI RMF's MAP function?

MAP is a certifiable standard like 42005They are unrelatedBoth involve understanding context and impacts on affected people, though structured differently42005 replaces MAP entirely

163

WEEK 21 · Controls, operations & judgement · AI Control Testing, Assurance and Audit Readiness

What's a reasonable assurance conclusion when a control's operating effectiveness could not be evidenced at all?

No conclusion is required in this caseReasonable assuranceLimited or insufficient assurance, since the operating claim can't be supportedFull assurance, since the design looked fine on paper

164

WEEK 13 · ISO AI management · ISO/IEC 42001 Foundations

How should an organisation treat a nonconformity discovered during a management review?

Nonconformities found internally don't need any actionIgnore it until the next external auditAddress it through correction and, where needed, root-cause corrective action, consistent with Clause 10Immediately decertify itself

165

WEEK 12 · ISO AI management · ISO AI Standards: ISO/IEC 22989, ISO/IEC 42001 and ISO/IEC 42005

How do the three standards relate to each other?

They are unrelated22989 gives shared terminology, 42001 is the management-system structure, 42005 gives impact-assessment guidance within it42005 replaces the other two42001 is only a glossary

166

WEEK 18 · AI law & privacy · EU AI Act High-Risk AI, GPAI, Timeline and Compliance Evidence

What does 'data governance' cover in the EU AI Act's high-risk obligations?

Marketing approval of the dataset nameOnly applies to GPAI models, not high-risk systemsOnly where the servers are physically locatedPractices for training, validation and testing data, including relevance, representativeness and quality

167

WEEK 8 · NIST AI RMF · NIST MEASURE

What is the primary purpose of the MEASURE function?

Testing, evaluating and capturing evidence against defined criteriaMapping affected peopleWriting the final approval decisionSetting governance policy

168

WEEK 16 · ISO AI management · ISO/IEC 42001 Clauses 7–10 and Annex A Controls

Why is 'competence' under Clause 7 relevant specifically to people operating AI systems, not just general staff training?

Competence under Clause 7 only concerns senior leadershipAI-specific competence has no bearing on control effectivenessCompetence requirements are identical regardless of rolePeople need AI-specific understanding (e.g. of limitations, bias, oversight practices) to operate systems and controls effectively

169

WEEK 8 · NIST AI RMF · NIST MEASURE

A good acceptance criterion should be set:

After testing, once results are knownBefore testing, as a measurable threshold with a rule for unacceptable failureOnly by the AI vendorAs a vague aspiration

170

WEEK 17 · AI law & privacy · EU AI Act Foundations and Global AI Law Landscape

Why does the Act treat certain manipulative AI techniques as prohibited rather than merely high-risk?

Manipulation is considered low-risk and barely regulatedTechniques designed to materially distort a person's behaviour in a way that causes significant harm are judged to pose an unacceptable risk, warranting an outright banAll persuasive AI techniques are automatically prohibited, including ordinary marketingProhibited practices are determined solely by company size

171

WEEK 15 · ISO AI management · ISO/IEC 42001 Clause 5 and Clause 6: Leadership and Planning

How should 'objectives and measurement' under Clause 6 be reviewed after a significant AI incident?

Incidents are irrelevant to Clause 6 objective-settingOnly Clause 10 addresses incidents, with no link back to Clause 6 objectivesObjectives should never be revisited after an incidentThe incident should prompt a review of whether existing objectives and measures adequately addressed the risk that materialised, adjusting them if not

172

WEEK 3 · Foundations, harm & law · Governance, Risk, Compliance & Assurance Basics

What's the practical consequence of an organisation having no defined risk appetite for AI?

Undefined appetite means every risk must be escalated to the boardNothing — appetite is a purely theoretical conceptDecisions about what risk to accept get made inconsistently, without a shared reference pointUndefined appetite automatically defaults to zero risk tolerance

173

WEEK 15 · ISO AI management · ISO/IEC 42001 Clause 5 and Clause 6: Leadership and Planning

What role does the 'governance board' typically play under Clause 5, as distinct from day-to-day system owners?

System owners report to no one under Clause 5It has no distinct role from system ownersIt provides senior oversight, sets direction and approves policy, while system owners handle operational management of individual AI systemsThe governance board should approve every individual AI output

174

WEEK 23 · Controls, operations & judgement · Sector Case Studies and Practitioner Judgement

In public sector AI, why does 'equality impact' carry particular weight?

Public bodies typically have statutory duties to consider equality impacts on the communities they serveEquality impact assessments are optional paperwork with no legal basisThis concern is identical to general fairness testingEquality impact only matters in the private sector

175

WEEK 12 · ISO AI management · ISO AI Standards: ISO/IEC 22989, ISO/IEC 42001 and ISO/IEC 42005

What is a 'management system' in the ISO sense, as distinct from a single policy document?

A structured, repeatable set of interrelated processes an organisation uses to achieve objectives and manage risk, including planning, operation, evaluation and improvementOnly the software used to track complianceA one-time audit checklistA synonym for a policy

176

WEEK 21 · Controls, operations & judgement · AI Control Testing, Assurance and Audit Readiness

What is a 'release-readiness review' meant to confirm?

That the marketing plan is finalisedThat the evidence, controls and testing results support release, before launch happensThat the model has the most parameters possibleThat legal sign-off is optional

177

WEEK 10 · GenAI & agent security · GenAI, RAG and LLM Application Risk

Why is 'source traceability' particularly important in a compliance or legal RAG use case?

Traceability only matters for marketing contentTraceability is irrelevant to compliance workBeing able to trace a stated conclusion back to a specific authoritative source is often essential for defensibilityCompliance use cases never require citations

178

WEEK 13 · ISO AI management · ISO/IEC 42001 Foundations

What is the practical risk of treating 42001 implementation as a one-off project rather than an ongoing system?

One-off projects are explicitly required by Clause 10This approach is what the standard recommendsNo risk — a one-off effort is equally effectiveThe AIMS becomes outdated and disconnected from how AI is actually being used as the organisation's AI footprint grows

179

WEEK 12 · ISO AI management · ISO AI Standards: ISO/IEC 22989, ISO/IEC 42001 and ISO/IEC 42005

Which of these questions is best answered using ISO/IEC 42005 rather than 42001?

'How do we handle nonconformities under Clause 10?''Who has authority to approve our AI policy?''What adverse effects could this system have on tenants with limited English proficiency?''What does our internal audit programme look like?'

180

WEEK 3 · Foundations, harm & law · Governance, Risk, Compliance & Assurance Basics

How does a 'risk' differ from an 'incident'?

Incidents are always less serious than risksRisks only exist after an incident has happenedThey are identical termsA risk is a possible future event with uncertain outcome; an incident is something that has already occurred

181

WEEK 11 · GenAI & agent security · Agentic AI, Prompt Injection and AI Security Controls

Why does giving an AI agent tool access (like sending email) raise the stakes versus a plain chatbot?

Tool access removes the need for loggingIt doesn't — agents are governed identically to chatbotsThe agent can take real-world, potentially irreversible actionsAgents cannot hallucinate

182

WEEK 15 · ISO AI management · ISO/IEC 42001 Clause 5 and Clause 6: Leadership and Planning

Why does 'senior management responsibilities' under Clause 5 typically include approving risk acceptance, not just policy?

Risk acceptance is always delegated to the most junior available staffPolicy approval and risk acceptance are the same single decisionSenior management has no role in risk acceptanceBecause accepting residual AI risk carries real organisational consequence, it needs authority matched to that level of decision, often senior leadership

183

WEEK 1 · Foundations, harm & law · AI Foundations for Governance

Which is a foreseeable example of 'basic AI risk thinking' applied to a fruit-recognition app misclassifying a fruit's ripeness?

Only high-stakes systems need risk thinkingRisk thinking only applies to generative AIThis scenario carries zero risk since it's a simple appEven a low-stakes system can still cause a bad outcome (e.g. wasted produce) if the consequence and likelihood of misclassification aren't considered

184

WEEK 4 · Foundations, harm & law · AI and the Wider Legal Landscape

Why is a privacy review alone insufficient for a generative marketing tool?

IP, consumer protection, equality and contract risks can arise independently of data protectionMarketing content can't create legal riskOnly cybersecurity mattersPrivacy law covers everything already

185

WEEK 19 · AI law & privacy · UK GDPR, DUAA, DPIA and AI Privacy

Processing criminal offence data for an AI-driven vetting tool requires:

No additional condition beyond a standard lawful basisAn additional specific condition under UK data protection law, beyond the standard lawful basis and special category rulesThis type of data cannot be processed under any circumstancesOnly consent is ever a valid basis for this data type

186

WEEK 7 · NIST AI RMF · NIST MAP

Why should 'assumptions and constraints' be explicitly documented in MAP?

Assumptions are the same as confirmed factsConstraints only matter for budget planningThey don't need documenting since they're obviousUndocumented assumptions can silently become false without anyone noticing, undermining the risk picture

187

WEEK 8 · NIST AI RMF · NIST MEASURE

What should happen if a system fails its defined acceptance criteria during MEASURE?

Release anyway since testing is just a formalityThe failure should block release or trigger further treatment, per the pre-agreed criteriaQuietly lower the threshold to pass itIgnore it if the vendor disagrees with the result

188

WEEK 13 · ISO AI management · ISO/IEC 42001 Foundations

Which best reflects the relationship between leadership commitment (Clause 5) and Annex A control selection?

They are entirely unrelated activitiesLeadership commitment sets the direction and resourcing that makes meaningful Annex A control selection and operation possibleAnnex A controls are selected before any leadership involvementLeadership has no role in control selection

189

WEEK 11 · GenAI & agent security · Agentic AI, Prompt Injection and AI Security Controls

Why does 'audit trail' design for agents typically need to capture the reasoning or plan, not just the final action taken?

Only the final output ever needs reviewCapturing reasoning is technically impossible for any agentReasoning is irrelevant to understanding what happenedUnderstanding why an agent chose a particular action helps diagnose whether it was manipulated, malfunctioning, or behaving as intended

190

WEEK 7 · NIST AI RMF · NIST MAP

What's the risk of skipping 'assumptions and constraints' documentation in MAP?

No risk — assumptions rarely matterKey unverified beliefs (e.g. about data quality or supplier practices) could quietly undermine the whole risk assessmentAssumptions are always self-evidently trueThis step is purely administrative

191

WEEK 12 · ISO AI management · ISO AI Standards: ISO/IEC 22989, ISO/IEC 42001 and ISO/IEC 42005

Why do ISO AI standards matter to a practitioner, beyond academic interest?

They give a shared vocabulary, a governance-system structure and an impact-assessment method that make AI governance consistent and auditableThey are only relevant to software engineersThey replace the need for any legal analysisThey have no practical use

192

WEEK 1 · Foundations, harm & law · AI Foundations for Governance

A 'small model' versus a 'large model' distinction primarily refers to:

The price of the laptop it runs onThe scale of the model's parameters and, generally, its training dataWhether it can be used for textWhether it is open-source

193

WEEK 9 · NIST AI RMF · NIST MANAGE and Risk Treatment

Which best reflects a 'reduce' treatment, as distinct from 'avoid'?

Reduce means keeping the system but adding controls that lower the risk to an acceptable levelAvoid and reduce are identical strategiesReduce is only used for low-severity risksReduce means not building the system at all

194

WEEK 6 · NIST AI RMF · NIST GOVERN

How should 'approval gates' differ for a low-risk internal tool versus a high-risk customer-facing system?

Approval gates only apply to customer-facing systemsThey should be identical regardless of riskGates should be proportionate — more rigorous scrutiny and higher-level sign-off for higher-risk systemsLow-risk tools should have more gates than high-risk ones

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WEEK 15 · ISO AI management · ISO/IEC 42001 Clause 5 and Clause 6: Leadership and Planning

How does Clause 5/6 planning relate to NIST's GOVERN and MAP functions?

No relationshipLeadership commitment and risk-based planning parallel NIST's GOVERN and MAP functionsNIST functions replace ISO clauses entirelyOnly MEASURE relates to Clause 6

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WEEK 13 · ISO AI management · ISO/IEC 42001 Foundations

What does 'Clause 4 to 10 overview' most usefully give a first-time reader of ISO/IEC 42001?

A mental map of how the requirements flow from context-setting through to continual improvementA list of every possible AI riskA pricing guide for certificationA legal history of the standard

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WEEK 9 · NIST AI RMF · NIST MANAGE and Risk Treatment

A rollback plan is important because:

It's never actually neededIt lets the organisation revert to a safe prior state if a release causes harmIt replaces the need for testingIt's only relevant to database migrations

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WEEK 24 · Controls, operations & judgement · Mock Exam, Capstone Evidence Pack and Final Viva

How should 'wider legal issues' (beyond privacy) be handled in a capstone-style scenario?

Actively screened for — IP, equality, consumer protection, liability and sector duties can all be relevant depending on the factsAssumed to never apply to AI systemsOnly relevant for public-sector scenariosIgnored unless the question is explicitly about privacy

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WEEK 1 · Foundations, harm & law · AI Foundations for Governance

Compared to fine-tuning, a key advantage of RAG is:

RAG always produces more creative writingRAG eliminates the need for any governanceRAG requires no data at allSource documents can be updated and cited without retraining the model

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WEEK 21 · Controls, operations & judgement · AI Control Testing, Assurance and Audit Readiness

What is the primary purpose of assurance planning before testing begins?

To define the objective, scope, criteria and evidence strategy before any testing startsTo guarantee a positive conclusion in advanceTo remove the need for independenceTo skip defining scope and jump straight to testing

Self-check centre

Confidence