{"id":16862,"date":"2026-09-19T11:48:02","date_gmt":"2026-09-19T11:48:02","guid":{"rendered":"https:\/\/www.examlabs.com\/certification\/?p=16862"},"modified":"2026-09-19T11:48:02","modified_gmt":"2026-09-19T11:48:02","slug":"isaca-aaism-practice-test-questions-and-exam-dumps-part10-q181-200","status":"publish","type":"post","link":"https:\/\/www.examlabs.com\/certification\/isaca-aaism-practice-test-questions-and-exam-dumps-part10-q181-200\/","title":{"rendered":"Isaca AAISM Practice Test Questions and Exam Dumps Part10 Q181-200"},"content":{"rendered":"

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View Full <\/b>Isaca AAISM Exam Dumps<\/b><\/a> and Practice Test Dumps<\/b><\/p>\n

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Question 181. Which governance practice best supports consistent AI risk classification across an organization?<\/b><\/h3>\n
    \n
  1. <\/b> Allow each developer to determine the risk level independently<\/span><\/li>\n
  2. <\/b> Use documented risk criteria and classification thresholds that can be applied consistently<\/span><\/li>\n
  3. <\/b> Classify every AI system as high risk<\/span><\/li>\n
  4. <\/b> Base risk classification only on development cost<\/span><\/li>\n<\/ol>\n

    Correct Answer: 2. Use documented risk criteria and classification thresholds that can be applied consistently<\/b><\/p>\n

    Explanation:<\/b><\/p>\n

    Consistent AI risk classification requires documented criteria that allow different teams to evaluate systems using comparable factors. Relevant criteria may include the impact of AI decisions, sensitivity of processed information, degree of automation, regulatory obligations, security exposure, affected populations, operational criticality, and potential consequences of incorrect outputs. Without defined thresholds, different teams may classify similar systems differently, creating inconsistent governance requirements. Classifying every system as high risk can also create unnecessary administrative burden and reduce the value of risk differentiation. A documented framework should therefore provide clear categories, decision criteria, approval requirements, and reassessment triggers. Consistent classification helps organizations apply proportionate controls and direct governance resources toward areas of greater potential exposure.<\/span><\/p>\n

    Question 182. What should happen when an AI system’s risk classification changes from moderate to high?<\/b><\/h3>\n
      \n
    1. <\/b> Continue using the original controls without review<\/span><\/li>\n
    2. <\/b> Delete the previous risk assessment<\/span><\/li>\n
    3. <\/b> Apply the additional governance requirements associated with the higher classification and reassess existing controls<\/span><\/li>\n
    4. <\/b> Allow the system owner to ignore the classification change<\/span><\/li>\n<\/ol>\n

      Correct Answer: 3. Apply the additional governance requirements associated with the higher classification and reassess existing controls<\/b><\/p>\n

      Explanation:<\/b><\/p>\n

      A change in risk classification indicates that the organization’s understanding of the system’s potential impact or exposure has changed. Moving from moderate to high risk should therefore trigger the controls and oversight associated with the higher category. Depending on the organization’s framework, this may involve additional validation, security testing, privacy review, independent assurance, management approval, stronger monitoring, or more frequent reassessment. Existing controls should also be evaluated to determine whether they remain sufficient for the new risk level. The previous assessment should be retained as appropriate for traceability rather than deleted. Risk classification should remain connected to lifecycle governance so that changes in business use, data, model behavior, or regulatory requirements are reflected in the control environment.<\/span><\/p>\n

      Question 183. Which factor is most relevant when determining the level of human oversight required for an AI system?<\/b><\/h3>\n
        \n
      1. <\/b> Potential impact of AI decisions and consequences of incorrect or inappropriate outputs<\/span><\/li>\n
      2. <\/b> Number of characters in the model name<\/span><\/li>\n
      3. <\/b> Color of the application’s interface<\/span><\/li>\n
      4. <\/b> Size of the development team<\/span><\/li>\n<\/ol>\n

        Correct Answer: 1. Potential impact of AI decisions and consequences of incorrect or inappropriate outputs<\/b><\/p>\n

        Explanation:<\/b><\/p>\n

        Human oversight should be proportionate to the potential consequences of AI-generated decisions or recommendations. Systems that support low-impact activities may require limited review, while systems affecting significant financial, employment, legal, safety, or other important outcomes may require stronger human involvement. Governance should consider whether humans can understand relevant outputs, challenge decisions, intervene when necessary, and override the system appropriately. Oversight should also account for model limitations, uncertainty, and the possibility of erroneous or biased outputs. The number of developers or visual design characteristics do not determine the appropriate level of oversight. A risk-based approach helps ensure that human involvement is meaningful rather than merely procedural and that responsibility remains clearly assigned.<\/span><\/p>\n

        Question 184. What is the purpose of defining human override procedures for a high-impact AI system?<\/b><\/h3>\n
          \n
        1. <\/b> To permit users to change any model result without documentation<\/span><\/li>\n
        2. <\/b> To eliminate model testing requirements<\/span><\/li>\n
        3. <\/b> To ensure the AI system can operate without any human involvement<\/span><\/li>\n
        4. <\/b> To provide an authorized mechanism for qualified personnel to intervene when AI output is inappropriate, unsafe, or unreliable<\/span><\/li>\n<\/ol>\n

          Correct Answer: 4. To provide an authorized mechanism for qualified personnel to intervene when AI output is inappropriate, unsafe, or unreliable<\/b><\/p>\n

          Explanation:<\/b><\/p>\n

          Human override procedures provide a controlled method for intervention when an AI system produces an output that should not be followed. For high-impact applications, personnel should understand when intervention is required, who has authority to override the system, how the override should be performed, and what evidence should be recorded. The procedure should not permit arbitrary changes without accountability because undocumented overrides can create new risks and make later investigation difficult. Effective human oversight also requires that personnel have sufficient information, training, authority, and time to intervene meaningfully. An override mechanism complements model validation and monitoring; it does not replace them. Governance should periodically evaluate whether the procedure works effectively under realistic operating conditions.<\/span><\/p>\n

          Question 185. Which governance control best supports accountability for manual overrides of AI decisions?<\/b><\/h3>\n
            \n
          1. <\/b> Permit anonymous overrides<\/span><\/li>\n
          2. <\/b> Record the authorized user, reason, time, affected decision, and relevant supporting information<\/span><\/li>\n
          3. <\/b> Prevent all records from being created<\/span><\/li>\n
          4. <\/b> Automatically approve every override<\/span><\/li>\n<\/ol>\n

            Correct Answer: 2. Record the authorized user, reason, time, affected decision, and relevant supporting information<\/b><\/p>\n

            Explanation:<\/b><\/p>\n

            Manual overrides can be necessary when AI outputs are inaccurate, inappropriate, or inconsistent with established requirements. However, overrides should remain accountable and traceable. Recording the identity or role of the authorized person, the time of the action, the affected decision, the reason for intervention, and relevant supporting information provides evidence for later review. These records can help identify recurring model problems, inappropriate use of override authority, training needs, or weaknesses in the underlying process. Anonymous or undocumented overrides make it difficult to determine whether the intervention was appropriate. Governance should also define who is permitted to override the system and establish monitoring or periodic review of override activity for high-impact applications.<\/span><\/p>\n

            Question 186. Why should AI governance establish criteria for when human review is mandatory?<\/b><\/h3>\n
              \n
            1. <\/b> To ensure that important or high-risk AI outputs receive appropriate human consideration before action is taken<\/span><\/li>\n
            2. <\/b> To require humans to review every low-risk AI output<\/span><\/li>\n
            3. <\/b> To eliminate automation from all business processes<\/span><\/li>\n
            4. <\/b> To allow AI systems to make decisions without accountability<\/span><\/li>\n<\/ol>\n

              Correct Answer: 1. To ensure that important or high-risk AI outputs receive appropriate human consideration before action is taken<\/b><\/p>\n

              Explanation:<\/b><\/p>\n

              Human review requirements should be based on the consequences and risk characteristics of an AI use case. Mandatory review can be appropriate when an AI output affects significant decisions, involves sensitive information, exceeds defined confidence or risk thresholds, or falls into an exception category established by policy. Clear criteria prevent human oversight from becoming inconsistent or merely symbolic. Requiring review of every low-risk output could reduce efficiency without materially improving risk management, while eliminating review entirely can create unacceptable exposure for higher-impact uses. Governance should define who performs the review, what information they need, when they can reject or override the AI output, and how decisions are documented. This makes human oversight operationally meaningful and accountable.<\/span><\/p>\n

              Question 187. Which practice helps determine whether AI training data remains appropriate over time?<\/b><\/h3>\n
                \n
              1. <\/b> Assume the original dataset remains suitable indefinitely<\/span><\/li>\n
              2. <\/b> Review data relevance, quality, provenance, representativeness, and changes in the intended use<\/span><\/li>\n
              3. <\/b> Delete all information about the dataset after training<\/span><\/li>\n
              4. <\/b> Evaluate only the size of the dataset<\/span><\/li>\n<\/ol>\n

                Correct Answer: 2. Review data relevance, quality, provenance, representativeness, and changes in the intended use<\/b><\/p>\n

                Explanation:<\/b><\/p>\n

                Training data can become less appropriate when business conditions, populations, regulations, source systems, or intended uses change. Governance should therefore periodically evaluate whether data remains relevant to the system’s purpose and whether quality, provenance, completeness, and representativeness remain acceptable. Changes in the intended use may require additional assessment even when the underlying dataset has not changed. Data size alone does not demonstrate suitability. Organizations should also maintain sufficient documentation to understand where data originated, how it was transformed, and what limitations exist. Periodic review can help identify outdated information, emerging bias, quality degradation, or new restrictions on data use. These practices support reliable model performance and responsible data governance throughout the AI lifecycle.<\/span><\/p>\n

                Question 188. What is the primary governance concern when training data comes from multiple sources with different quality standards?<\/b><\/h3>\n
                  \n
                1. <\/b> The model will automatically become more accurate<\/span><\/li>\n
                2. <\/b> Source differences can create inconsistent quality, provenance, bias, privacy, or usage restrictions that affect the resulting AI system<\/span><\/li>\n
                3. <\/b> Multiple sources eliminate the need for validation<\/span><\/li>\n
                4. <\/b> Data-source differences are relevant only to storage capacity<\/span><\/li>\n<\/ol>\n

                  Correct Answer: 2. Source differences can create inconsistent quality, provenance, bias, privacy, or usage restrictions that affect the resulting AI system<\/b><\/p>\n

                  Explanation:<\/b><\/p>\n

                  Combining data from multiple sources can increase the usefulness of a training dataset, but it can also introduce differences in quality, collection methods, definitions, representativeness, and permitted uses. Some sources may contain inaccurate or outdated information, while others may have different privacy or licensing restrictions. Uneven representation can also contribute to biased model behavior. Governance should therefore establish requirements for source evaluation, provenance documentation, data-quality assessment, licensing review, privacy analysis, and appropriate preprocessing. Validation should consider the combined dataset rather than assuming that acceptable individual sources will automatically produce an acceptable aggregate dataset. Understanding source characteristics enables the organization to identify limitations and apply controls before the data materially influences model behavior.<\/span><\/p>\n

                  Question 189. Which governance activity is most useful for detecting changes in AI model performance after deployment?<\/b><\/h3>\n
                    \n
                  1. <\/b> Continuous or periodic monitoring against defined performance and risk thresholds<\/span><\/li>\n
                  2. <\/b> Reviewing the model only during initial development<\/span><\/li>\n
                  3. <\/b> Deleting production performance records<\/span><\/li>\n
                  4. <\/b> Assuming that validation results remain permanently valid<\/span><\/li>\n<\/ol>\n

                    Correct Answer: 1. Continuous or periodic monitoring against defined performance and risk thresholds<\/b><\/p>\n

                    Explanation:<\/b><\/p>\n

                    Post-deployment monitoring helps determine whether an AI model continues to operate within expected performance and risk boundaries. Organizations can define metrics appropriate to the use case, such as accuracy, error rates, fairness indicators, false-positive or false-negative rates, availability, latency, or other business and risk measures. Thresholds can identify conditions requiring investigation or escalation. Initial validation remains important but cannot guarantee that model behavior will remain unchanged because data distributions, user behavior, business conditions, and external factors can evolve. Production records and monitoring results therefore provide important evidence for ongoing governance. Monitoring should be connected to defined response procedures so that detected degradation leads to investigation, reassessment, remediation, or controlled model changes.<\/span><\/p>\n

                    Question 190. What should governance teams consider when selecting AI monitoring thresholds?<\/b><\/h3>\n
                      \n
                    1. <\/b> Only the technical preference of the model developer<\/span><\/li>\n
                    2. <\/b> The color scheme of the monitoring dashboard<\/span><\/li>\n
                    3. <\/b> Business impact, acceptable risk levels, baseline performance, regulatory considerations, and consequences of threshold breaches<\/span><\/li>\n
                    4. <\/b> The number of monitoring tools installed<\/span><\/li>\n<\/ol>\n

                      Correct Answer: 3. Business impact, acceptable risk levels, baseline performance, regulatory considerations, and consequences of threshold breaches<\/b><\/p>\n

                      Explanation:<\/b><\/p>\n

                      Monitoring thresholds should be meaningful in relation to the risks and objectives of the AI system. A threshold that is too permissive may fail to detect material degradation, while one that is too sensitive can generate excessive alerts and reduce the ability of teams to focus on important events. Governance should consider baseline performance, business impact, risk tolerance, regulatory requirements, and the consequences of crossing each threshold. Different metrics may require different thresholds and escalation procedures. Thresholds should also be reviewed when the model, data, business process, or risk environment changes. A well-designed monitoring framework connects measurable indicators with defined actions so that threshold breaches result in timely investigation and appropriate governance decisions.<\/span><\/p>\n

                      Question 191. Which control is most useful for identifying unauthorized changes to AI model artifacts?<\/b><\/h3>\n
                        \n
                      1. <\/b> Disable version history<\/span><\/li>\n
                      2. <\/b> Allow unrestricted write access to model repositories<\/span><\/li>\n
                      3. <\/b> Use integrity controls such as access restrictions, versioning, checksums or signatures, and change logging<\/span><\/li>\n
                      4. <\/b> Store models without identifying their versions<\/span><\/li>\n<\/ol>\n

                        Correct Answer: 3. Use integrity controls such as access restrictions, versioning, checksums or signatures, and change logging<\/b><\/p>\n

                        Explanation:<\/b><\/p>\n

                        Model artifacts are important components of an AI system and should be protected against unauthorized or unexplained modification. Access restrictions reduce the number of users who can alter model files, while version control provides a historical record of changes. Cryptographic hashes or signatures can help detect unauthorized modification when implemented appropriately, and change logs provide evidence of who performed an action and when. Together, these controls support integrity and traceability. Simply disabling version history or granting broad write access increases risk. Governance should also establish approval requirements for production changes and ensure that the deployed model can be linked to an approved version. This supports reliable investigations and reduces the likelihood of unauthorized model manipulation.<\/span><\/p>\n

                        Question 192. Why should AI governance address model artifact provenance?<\/b><\/h3>\n
                          \n
                        1. <\/b> To establish where a model originated, which version was used, and whether it was obtained and modified through approved processes<\/span><\/li>\n
                        2. <\/b> To guarantee that the model is unbiased<\/span><\/li>\n
                        3. <\/b> To eliminate all model vulnerabilities<\/span><\/li>\n
                        4. <\/b> To ensure the model requires no validation<\/span><\/li>\n<\/ol>\n

                          Correct Answer: 1. To establish where a model originated, which version was used, and whether it was obtained and modified through approved processes<\/b><\/p>\n

                          Explanation:<\/b><\/p>\n

                          Model provenance provides information about the origin and history of an AI model or model component. This may include the source provider, version, acquisition method, modifications, dependencies, approvals, testing history, and deployment records. Provenance helps organizations understand what they are operating and supports investigation when unexpected behavior occurs. It can also be important for licensing, security, reproducibility, and supply-chain risk management. Provenance alone does not guarantee that a model is unbiased, secure, or accurate, but it provides the traceability needed to evaluate those characteristics more effectively. Governance should therefore establish documentation and controls that preserve provenance throughout the model lifecycle, particularly when external or open-source models are incorporated into production systems.<\/span><\/p>\n

                          Question 193. What is a key governance concern when AI models depend on external software libraries?<\/b><\/h3>\n
                            \n
                          1. <\/b> Dependencies may introduce vulnerabilities, licensing obligations, compatibility problems, or supply-chain risks<\/span><\/li>\n
                          2. <\/b> External libraries automatically improve model security<\/span><\/li>\n
                          3. <\/b> Dependencies never require inventory management<\/span><\/li>\n
                          4. <\/b> Software libraries are unrelated to AI system governance<\/span><\/li>\n<\/ol>\n

                            Correct Answer: 1. Dependencies may introduce vulnerabilities, licensing obligations, compatibility problems, or supply-chain risks<\/b><\/p>\n

                            Explanation:<\/b><\/p>\n

                            AI systems often rely on software frameworks, libraries, packages, APIs, and other components. These dependencies can introduce vulnerabilities, licensing obligations, compatibility issues, or malicious supply-chain risks. Governance should therefore maintain appropriate dependency inventories and establish processes for evaluating security advisories, updates, supported versions, and licensing requirements. Critical dependencies may also require additional monitoring or contingency planning. Organizations should understand which components are essential to system operation and how changes to those components could affect model behavior or security. Treating dependencies as outside the governance boundary can create significant blind spots. A controlled software supply-chain process helps ensure that AI systems remain maintainable and that security or compliance issues can be identified and addressed in a timely manner.<\/span><\/p>\n

                            Question 194. Which practice best supports responsible management of AI model dependencies?<\/b><\/h3>\n
                              \n
                            1. <\/b> Track significant dependencies, versions, owners, known risks, and update requirements<\/span><\/li>\n
                            2. <\/b> Allow dependencies to change without testing<\/span><\/li>\n
                            3. <\/b> Remove all dependency documentation after deployment<\/span><\/li>\n
                            4. <\/b> Permit unsupported components indefinitely without review<\/span><\/li>\n<\/ol>\n

                              Correct Answer: 1. Track significant dependencies, versions, owners, known risks, and update requirements<\/b><\/p>\n

                              Explanation:<\/b><\/p>\n

                              Dependency management helps organizations understand the components required for an AI system to function and the risks associated with those components. Recording versions and ownership makes it easier to identify affected systems when vulnerabilities or compatibility problems are discovered. Governance should also establish processes for evaluating updates, determining whether changes require testing, and addressing unsupported or obsolete components. Not every dependency necessarily requires identical oversight, so organizations can prioritize based on criticality and risk. Allowing components to change without testing can introduce unexpected behavior or operational failures. A maintained dependency record also supports incident response, auditability, lifecycle planning, and supply-chain risk management by providing visibility into the technical elements that support the AI system.<\/span><\/p>\n

                              Question 195. Which governance measure helps ensure that AI systems remain within approved business purposes?<\/b><\/h3>\n
                                \n
                              1. <\/b> Allow users to expand system purposes without review<\/span><\/li>\n
                              2. <\/b> Define approved use cases, prohibited uses, ownership responsibilities, and reassessment requirements for material changes<\/span><\/li>\n
                              3. <\/b> Remove purpose documentation after deployment<\/span><\/li>\n
                              4. <\/b> Allow the AI system to determine its own authorized uses<\/span><\/li>\n<\/ol>\n

                                Correct Answer: 2. Define approved use cases, prohibited uses, ownership responsibilities, and reassessment requirements for material changes<\/b><\/p>\n

                                Explanation:<\/b><\/p>\n

                                AI systems should operate within clearly defined purposes because risk and compliance obligations can change when the system is used differently from its original approval. Governance should document the intended business purpose, authorized users, permitted activities, prohibited uses, relevant data, and responsible owner. If stakeholders propose a material expansion of the use case, the organization should determine whether additional risk assessment, testing, privacy review, or approval is required. Without purpose controls, an AI system may gradually be repurposed for activities that were never evaluated. Clear use-case governance provides boundaries while still allowing legitimate changes through a controlled reassessment process. This supports accountability and reduces the risk of uncontrolled AI deployment.<\/span><\/p>\n

                                Question 196. Why should AI governance define prohibited or restricted use cases?<\/b><\/h3>\n
                                  \n
                                1. <\/b> To identify activities that present unacceptable or specially controlled risks under organizational policy or applicable requirements<\/span><\/li>\n
                                2. <\/b> To prevent all use of AI<\/span><\/li>\n
                                3. <\/b> To eliminate employee responsibility<\/span><\/li>\n
                                4. <\/b> To guarantee that prohibited uses can never occur<\/span><\/li>\n<\/ol>\n

                                  Correct Answer: 1. To identify activities that present unacceptable or specially controlled risks under organizational policy or applicable requirements<\/b><\/p>\n

                                  Explanation:<\/b><\/p>\n

                                  Some AI applications may present risks that an organization has determined are unacceptable or require additional safeguards. Documenting prohibited or restricted uses provides employees and development teams with clear boundaries before systems are designed or deployed. Restrictions may be based on organizational risk appetite, legal obligations, privacy requirements, security concerns, ethical principles, or potential harm associated with particular uses. A prohibition does not guarantee that unauthorized use will never occur, so monitoring, access controls, training, and reporting mechanisms may also be necessary. Governance should communicate these boundaries clearly and establish an exception process where appropriate. This creates predictable expectations and reduces the likelihood that teams unknowingly pursue AI applications outside approved organizational limits.<\/span><\/p>\n

                                  Question 197. What is the governance purpose of an AI control library?<\/b><\/h3>\n
                                    \n
                                  1. <\/b> To provide reusable control requirements that can be selected and applied according to system risk and organizational needs<\/span><\/li>\n
                                  2. <\/b> To force every AI system to implement every possible control<\/span><\/li>\n
                                  3. <\/b> To replace all risk assessments<\/span><\/li>\n
                                  4. <\/b> To document only failed controls<\/span><\/li>\n<\/ol>\n

                                    Correct Answer: 1. To provide reusable control requirements that can be selected and applied according to system risk and organizational needs<\/b><\/p>\n

                                    Explanation:<\/b><\/p>\n

                                    An AI control library can provide a structured collection of control objectives and implementation requirements covering areas such as security, privacy, data quality, model governance, human oversight, monitoring, third-party risk, and lifecycle management. Reusable controls promote consistency and make governance more scalable across multiple AI systems. However, organizations should apply controls proportionately rather than requiring every system to implement every control regardless of risk. Risk assessments determine which controls are relevant and whether additional measures are necessary. A control library can also support testing, evidence collection, mapping to regulatory requirements, and reporting. It therefore acts as a practical bridge between high-level governance policies and specific operational safeguards.<\/span><\/p>\n

                                    Question 198. Which activity helps determine whether AI controls are operating as intended?<\/b><\/h3>\n
                                      \n
                                    1. <\/b> Conduct control testing using defined procedures, evidence, criteria, and documented results<\/span><\/li>\n
                                    2. <\/b> Assume controls work because policies exist<\/span><\/li>\n
                                    3. <\/b> Test only controls that have already failed<\/span><\/li>\n
                                    4. <\/b> Allow system owners to change test results without review<\/span><\/li>\n<\/ol>\n

                                      Correct Answer: 1. Conduct control testing using defined procedures, evidence, criteria, and documented results<\/b><\/p>\n

                                      Explanation:<\/b><\/p>\n

                                      Control testing provides evidence about whether governance requirements are implemented and operating effectively. Testing should use defined criteria and procedures appropriate to the control and risk being evaluated. Evidence may include configuration records, logs, approval records, monitoring reports, access reviews, testing results, or other relevant documentation. Results should be documented so that deficiencies can be assigned, tracked, and remediated. The existence of a policy does not demonstrate that its requirements are actually implemented. Testing should also be sufficiently independent for the level of assurance required. A structured control-testing program allows management to distinguish between controls that are designed appropriately, controls that exist but are inconsistently operated, and controls that require remediation.<\/span><\/p>\n

                                      Question 199. What should happen when an AI control deficiency is identified during assurance testing?<\/b><\/h3>\n
                                        \n
                                      1. <\/b> Remove the control from the governance framework<\/span><\/li>\n
                                      2. <\/b> Document the deficiency, assess its significance, assign remediation responsibility, and track corrective action<\/span><\/li>\n
                                      3. <\/b> Ignore it if the AI system has not experienced an incident<\/span><\/li>\n
                                      4. <\/b> Automatically classify the system as compliant<\/span><\/li>\n<\/ol>\n

                                        Correct Answer: 2. Document the deficiency, assess its significance, assign remediation responsibility, and track corrective action<\/b><\/p>\n

                                        Explanation:<\/b><\/p>\n

                                        A control deficiency should be managed through a structured remediation process. The organization should document what requirement was not satisfied, evaluate the significance and potential impact of the deficiency, identify an accountable owner, establish an appropriate remediation plan, and monitor progress until closure. The response should be proportionate to the risk and may include compensating controls when immediate correction is not practical. Ignoring a deficiency because no incident has occurred can leave an important weakness unresolved. Governance reporting should provide appropriate visibility into significant open issues and overdue remediation. A documented deficiency-management process strengthens accountability and helps management understand whether the AI control environment is operating as intended.<\/span><\/p>\n

                                        Question 200. Which characteristic best supports an effective AI assurance program?<\/b><\/h3>\n
                                          \n
                                        1. <\/b> Assurance activities are performed only when an incident occurs<\/span><\/li>\n
                                        2. <\/b> Every AI system receives identical assurance procedures regardless of risk<\/span><\/li>\n
                                        3. <\/b> Assurance focuses only on technical model accuracy<\/span><\/li>\n
                                        4. <\/b> Assurance is risk-based, evidence-driven, appropriately independent, and connected to remediation and governance decisions<\/span><\/li>\n<\/ol>\n

                                          Correct Answer: 4. Assurance is risk-based, evidence-driven, appropriately independent, and connected to remediation and governance decisions<\/b><\/p>\n

                                          Explanation:<\/b><\/p>\n

                                          An effective AI assurance program provides confidence that governance requirements and controls are appropriately designed and operating as expected. Risk-based assurance directs greater attention toward systems with higher potential impact or exposure rather than applying identical procedures to every system. Evidence-driven testing allows conclusions to be supported by objective information, while appropriate independence reduces conflicts of interest and strengthens credibility. Assurance should examine relevant areas such as security, privacy, model governance, data management, human oversight, monitoring, and compliance rather than focusing exclusively on technical accuracy. Findings should result in documented remediation, escalation when necessary, and follow-up verification. Connecting assurance results to governance decisions enables management to use evidence when addressing AI risks and improving the control environment.<\/span><\/p>\n

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                                          View Full Isaca AAISM Exam Dumps and Practice Test Dumps   Question 181. Which governance practice best supports consistent AI risk classification across an organization? Allow each developer to determine the risk level independently Use documented risk criteria and classification thresholds that can be applied consistently Classify every AI system as high risk Base risk […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[1648,1647],"tags":[],"_links":{"self":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/16862"}],"collection":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/comments?post=16862"}],"version-history":[{"count":1,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/16862\/revisions"}],"predecessor-version":[{"id":16927,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/16862\/revisions\/16927"}],"wp:attachment":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/media?parent=16862"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/categories?post=16862"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/tags?post=16862"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}