{"id":16865,"date":"2026-09-19T11:47:18","date_gmt":"2026-09-19T11:47:18","guid":{"rendered":"https:\/\/www.examlabs.com\/certification\/?p=16865"},"modified":"2026-09-19T11:47:18","modified_gmt":"2026-09-19T11:47:18","slug":"isaca-aaism-practice-test-questions-and-exam-dumps-part13-q241-260","status":"publish","type":"post","link":"https:\/\/www.examlabs.com\/certification\/isaca-aaism-practice-test-questions-and-exam-dumps-part13-q241-260\/","title":{"rendered":"Isaca AAISM Practice Test Questions and Exam Dumps Part13 Q241-260"},"content":{"rendered":"

<\/h1>\n

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

 <\/p>\n

Question 241. What should an organization do before approving an AI system for a high-impact business process?<\/b><\/h3>\n
    \n
  1. <\/b> Conduct a risk-based assessment and confirm required controls are in place<\/span><\/li>\n
  2. <\/b> Approve the system based only on expected financial benefits<\/span><\/li>\n
  3. <\/b> Skip testing because the system uses an established model<\/span><\/li>\n
  4. <\/b> Transfer approval responsibility entirely to the vendor<\/span><\/li>\n<\/ol>\n

    Correct Answer: 1. Conduct a risk-based assessment and confirm required controls are in place<\/b><\/p>\n

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

    High-impact AI systems can affect important business decisions, individuals, financial outcomes, or regulatory obligations, so approval should be based on a structured risk assessment. The organization should identify relevant risks, determine whether controls are appropriate, review validation evidence, confirm accountability, and establish monitoring and human oversight requirements before production use. Business benefits may be considered, but they should not replace risk analysis. Using an established model also does not automatically make a particular implementation safe because data, configuration, purpose, users, and operating environment can introduce different risks. Vendors may provide evidence, but the organization remains responsible for ensuring that its governance requirements are satisfied.<\/span><\/p>\n

    Question 242. Which practice best helps ensure AI governance requirements remain aligned with changing regulations?<\/b><\/h3>\n
      \n
    1. <\/b> Reviewing regulations only after receiving a violation notice<\/span><\/li>\n
    2. <\/b> Maintaining a process to monitor regulatory developments and assess their impact<\/span><\/li>\n
    3. <\/b> Applying the same legal requirements to every jurisdiction<\/span><\/li>\n
    4. <\/b> Allowing each developer to interpret regulations independently<\/span><\/li>\n<\/ol>\n

      Correct Answer: 2. Maintaining a process to monitor regulatory developments and assess their impact<\/b><\/p>\n

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

      Regulatory requirements affecting AI can evolve as governments and regulators respond to emerging technologies and risks. Organizations should maintain a structured process for identifying relevant changes, determining which AI systems may be affected, assigning responsibility for interpretation, and updating policies or controls when necessary. Waiting for an enforcement action is reactive and may expose the organization to unnecessary risk. Applying identical requirements everywhere may also overlook jurisdiction-specific obligations. Individual developers should not independently determine the organization\u2019s legal position. A regulatory monitoring process should connect identified changes to governance activities such as risk assessments, contracts, documentation, training, control updates, and reassessment of affected AI systems.<\/span><\/p>\n

      Question 243. What is the purpose of documenting assumptions made during an AI risk assessment?<\/b><\/h3>\n
        \n
      1. <\/b> To eliminate the need for validation<\/span><\/li>\n
      2. <\/b> To make the assessment appear more complex<\/span><\/li>\n
      3. <\/b> To provide transparency about conditions and judgments underlying the risk conclusions<\/span><\/li>\n
      4. <\/b> To prevent future reassessment<\/span><\/li>\n<\/ol>\n

        Correct Answer: 3. To provide transparency about conditions and judgments underlying the risk conclusions<\/b><\/p>\n

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

        Risk assessments often depend on assumptions about data quality, system behavior, user populations, business processes, controls, regulatory conditions, or expected operating environments. Documenting these assumptions helps reviewers understand how conclusions were reached and identify circumstances that could invalidate those conclusions. If assumptions change, the organization can determine whether reassessment is necessary. This improves transparency and supports auditability, governance review, and decision-making. Documentation should distinguish verified facts from assumptions and judgments so that stakeholders understand the level of certainty involved. Assumptions do not replace testing or validation; rather, they make the basis of the assessment more explicit and help identify areas requiring additional evidence.<\/span><\/p>\n

        Question 244. Why should AI governance distinguish between inherent risk and residual risk?<\/b><\/h3>\n
          \n
        1. <\/b> To determine how much risk remains after controls are applied<\/span><\/li>\n
        2. <\/b> To eliminate the need for controls<\/span><\/li>\n
        3. <\/b> To classify every AI system as high risk<\/span><\/li>\n
        4. <\/b> To ensure all risks are transferred to vendors<\/span><\/li>\n<\/ol>\n

          Correct Answer: 1. To determine how much risk remains after controls are applied<\/b><\/p>\n

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

          Inherent risk represents the level of risk associated with an AI activity before considering the effect of controls, while residual risk represents the remaining exposure after controls and risk treatments are applied. Distinguishing these concepts helps management determine whether controls have reduced risk to an acceptable level. It also supports decisions about additional treatment, monitoring, escalation, or formal risk acceptance. Without this distinction, governance teams may confuse the original exposure with the risk that actually remains. The assessment should consider both the effectiveness of controls and any limitations or uncertainties. Residual risk should then be compared with the organization\u2019s approved risk appetite and relevant requirements.<\/span><\/p>\n

          Question 245. Which information should be included when communicating a significant AI risk to senior management?<\/b><\/h3>\n
            \n
          1. <\/b> Only the technical architecture of the model<\/span><\/li>\n
          2. <\/b> Only the number of employees using the AI system<\/span><\/li>\n
          3. <\/b> The risk, potential business impact, affected objectives, current controls, and recommended actions<\/span><\/li>\n
          4. <\/b> A list of unrelated AI projects<\/span><\/li>\n<\/ol>\n

            Correct Answer: 3. The risk, potential business impact, affected objectives, current controls, and recommended actions<\/b><\/p>\n

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

            Senior management needs risk information that supports decisions rather than excessive technical detail without business context. A significant AI risk report should explain what the risk is, why it matters, which objectives or processes could be affected, the likelihood and potential impact where appropriate, existing controls, residual exposure, and actions being considered. The information should be concise enough for decision-makers while retaining sufficient evidence to support accountability. Technical details may be included when relevant, but they should be connected to business consequences. Clear communication helps management determine whether additional resources, risk treatment, escalation, or acceptance decisions are necessary.<\/span><\/p>\n

            Question 246. What is the primary purpose of an AI risk register?<\/b><\/h3>\n
              \n
            1. <\/b> To provide a structured record of identified risks, ownership, treatment, and status<\/span><\/li>\n
            2. <\/b> To replace all AI policies<\/span><\/li>\n
            3. <\/b> To document only cybersecurity incidents<\/span><\/li>\n
            4. <\/b> To list AI systems without recording their risks<\/span><\/li>\n<\/ol>\n

              Correct Answer: 1. To provide a structured record of identified risks, ownership, treatment, and status<\/b><\/p>\n

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

              An AI risk register provides a centralized mechanism for recording and tracking risks associated with AI systems and related activities. Useful fields can include the risk description, affected system or process, risk owner, inherent and residual risk, existing controls, treatment actions, target dates, status, and escalation information. This allows governance teams to monitor whether risks are being addressed and whether actions are overdue. The register should not replace policies, incident records, or system inventories; it serves a different purpose by focusing specifically on risk management. Maintaining an accurate register also improves reporting and helps management identify recurring or enterprise-wide risk patterns.<\/span><\/p>\n

              Question 247. Which characteristic makes an AI risk statement useful for governance purposes?<\/b><\/h3>\n
                \n
              1. <\/b> It uses only highly technical terminology<\/span><\/li>\n
              2. <\/b> It identifies the cause, potential event, and resulting impact clearly<\/span><\/li>\n
              3. <\/b> It avoids identifying affected business objectives<\/span><\/li>\n
              4. <\/b> It combines unrelated risks into one statement<\/span><\/li>\n<\/ol>\n

                Correct Answer: 2. It identifies the cause, potential event, and resulting impact clearly<\/b><\/p>\n

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

                A well-structured risk statement helps decision-makers understand what could happen, why it could happen, and what consequences may result. For AI governance, this can involve identifying a vulnerability or condition, the potential event or failure, and the impact on business objectives, individuals, compliance, security, or operations. Clear risk statements support consistent assessment and treatment because stakeholders are evaluating the same scenario rather than interpreting vague descriptions differently. Excessive technical terminology can make risks difficult for business leaders to understand, while combining unrelated issues can make ownership and treatment unclear. A precise risk statement therefore improves communication, prioritization, and accountability.<\/span><\/p>\n

                Question 248. What should determine the frequency of AI risk reassessments?<\/b><\/h3>\n
                  \n
                1. <\/b> A fixed annual schedule in every case<\/span><\/li>\n
                2. <\/b> The number of employees in the organization<\/span><\/li>\n
                3. <\/b> The age of the organization<\/span><\/li>\n
                4. <\/b> The AI system\u2019s risk, rate of change, regulatory environment, and material events<\/span><\/li>\n<\/ol>\n

                  Correct Answer: 4. The AI system\u2019s risk, rate of change, regulatory environment, and material events<\/b><\/p>\n

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

                  Risk reassessment frequency should be proportionate to the characteristics of the AI system and its operating environment. High-risk systems or systems subject to rapid changes may require more frequent review than low-risk, stable applications. Reassessment triggers can include material model changes, new data sources, changes in purpose, significant incidents, new regulations, major vendor changes, changes in affected populations, or substantial performance deterioration. A fixed schedule can still be useful as a minimum requirement, but it should not be the only trigger. Risk-based reassessment allows governance resources to focus attention where changing conditions could materially alter the organization\u2019s exposure.<\/span><\/p>\n

                  Question 249. Which control is most appropriate for protecting sensitive AI training data from unauthorized access?<\/b><\/h3>\n
                    \n
                  1. <\/b> Least-privilege access with authentication and appropriate monitoring<\/span><\/li>\n
                  2. <\/b> Publicly sharing the training dataset<\/span><\/li>\n
                  3. <\/b> Allowing all project members unrestricted access<\/span><\/li>\n
                  4. <\/b> Removing all access logs<\/span><\/li>\n<\/ol>\n

                    Correct Answer: 1. Least-privilege access with authentication and appropriate monitoring<\/b><\/p>\n

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

                    Sensitive AI training data should be protected using controls that restrict access to authorized personnel and systems with a legitimate business need. Least privilege limits unnecessary exposure, while strong authentication helps verify the identity of users and services accessing the data. Monitoring and logging can provide evidence of access and support detection or investigation of suspicious activity. Additional safeguards may include encryption, data classification, retention controls, segregation, and data-loss prevention depending on the risk. Broad access is generally inconsistent with least privilege and can increase exposure. Removing logs also reduces accountability and investigative capability. Access controls should be reviewed periodically as roles and project requirements change.<\/span><\/p>\n

                    Question 250. Why should AI governance address data retention requirements?<\/b><\/h3>\n
                      \n
                    1. <\/b> To ensure every dataset is retained permanently<\/span><\/li>\n
                    2. <\/b> To avoid documenting data sources<\/span><\/li>\n
                    3. <\/b> To determine how long data should be retained and when secure disposal is required<\/span><\/li>\n
                    4. <\/b> To prevent all data from being deleted<\/span><\/li>\n<\/ol>\n

                      Correct Answer: 3. To determine how long data should be retained and when secure disposal is required<\/b><\/p>\n

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

                      Data retention should be based on legitimate business, legal, regulatory, contractual, privacy, and operational requirements. Retaining AI training, validation, operational, or audit data indefinitely can increase privacy, security, storage, and compliance risks. Conversely, deleting data too early may prevent required investigations, audits, reproducibility, or fulfillment of legitimate obligations. Governance should therefore establish retention periods and disposal procedures appropriate to the type and sensitivity of information. The requirements may differ across datasets and jurisdictions. Secure disposal should be performed when retention requirements expire, while exceptions such as legal holds or ongoing investigations should be handled through documented processes and appropriate authorization.<\/span><\/p>\n

                      Question 251. What is the primary governance benefit of separating development, testing, and production environments for AI systems?<\/b><\/h3>\n
                        \n
                      1. <\/b> It guarantees perfect model performance<\/span><\/li>\n
                      2. <\/b> It reduces the risk that unapproved changes directly affect production<\/span><\/li>\n
                      3. <\/b> It eliminates the need for model validation<\/span><\/li>\n
                      4. <\/b> It allows developers to bypass change management<\/span><\/li>\n<\/ol>\n

                        Correct Answer: 2. It reduces the risk that unapproved changes directly affect production<\/b><\/p>\n

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

                        Separating development, testing, and production environments helps control how AI changes move into operational use. Development environments allow experimentation, while testing environments provide a controlled setting for validation before deployment. Production environments should contain approved configurations and controlled changes. This separation reduces the likelihood that experimental code, unvalidated models, incorrect configurations, or unauthorized modifications will affect business operations. It also supports segregation of duties, traceability, and controlled release processes. Environment separation does not eliminate the need for validation or change management. Instead, it provides an important structural control that supports those processes and helps organizations maintain confidence in the integrity of production AI systems.<\/span><\/p>\n

                        Question 252. Which activity provides evidence that an AI model performs as expected before deployment?<\/b><\/h3>\n
                          \n
                        1. <\/b> Formal validation against predefined acceptance criteria<\/span><\/li>\n
                        2. <\/b> Informal discussion among developers<\/span><\/li>\n
                        3. <\/b> Marketing approval<\/span><\/li>\n
                        4. <\/b> A general statement that the model is accurate<\/span><\/li>\n<\/ol>\n

                          Correct Answer: 1. Formal validation against predefined acceptance criteria<\/b><\/p>\n

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

                          Model validation should provide objective evidence that the AI system meets defined requirements before deployment. Appropriate validation may evaluate accuracy, robustness, fairness, reliability, security, explainability, or other characteristics relevant to the system\u2019s intended purpose. Predefined acceptance criteria allow reviewers to determine whether results meet established expectations rather than relying on subjective impressions. Validation evidence should be documented and linked to the model version and test conditions. Developer opinions or general claims about accuracy are not sufficient substitutes for structured evidence. The depth and type of validation should be proportionate to the system\u2019s risk, use case, affected population, and potential consequences of incorrect outputs.<\/span><\/p>\n

                          Question 253. What should happen when model validation identifies a result outside an approved acceptance threshold?<\/b><\/h3>\n
                            \n
                          1. <\/b> The result should be ignored if the model is commercially valuable<\/span><\/li>\n
                          2. <\/b> The threshold should automatically be removed<\/span><\/li>\n
                          3. <\/b> The issue should be investigated and resolved or formally escalated before approval<\/span><\/li>\n
                          4. <\/b> The model should immediately be deployed without further testing<\/span><\/li>\n<\/ol>\n

                            Correct Answer: 3. The issue should be investigated and resolved or formally escalated before approval<\/b><\/p>\n

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

                            A validation result outside an approved acceptance threshold indicates that the model may not satisfy an established requirement. Governance should therefore require investigation to determine the cause, significance, and potential impact. Corrective action may involve changing the model, improving data, adjusting configuration, strengthening controls, or refining the use case. If the issue cannot be fully resolved, an authorized decision-maker may need to determine whether the remaining risk can be accepted under applicable governance requirements. Simply removing the threshold because the model is commercially valuable undermines the control. Deployment should occur only when the organization has sufficient evidence that requirements are met or that residual risk has been appropriately authorized.<\/span><\/p>\n

                            Question 254. Which practice best supports reproducibility of an important AI model result?<\/b><\/h3>\n
                              \n
                            1. <\/b> Recording only the final output<\/span><\/li>\n
                            2. <\/b> Keeping the relevant model version, data references, configuration, and execution context<\/span><\/li>\n
                            3. <\/b> Deleting intermediate artifacts immediately<\/span><\/li>\n
                            4. <\/b> Allowing undocumented manual changes<\/span><\/li>\n<\/ol>\n

                              Correct Answer: 2. Keeping the relevant model version, data references, configuration, and execution context<\/b><\/p>\n

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

                              Reproducibility requires enough information to recreate or meaningfully investigate how an AI result was generated. Depending on the system, this may include the model version, relevant datasets or data references, configuration parameters, software dependencies, prompts or inputs, execution environment, timestamps, and other material settings. Simply recording the final output may not explain how it was produced. Deleting important artifacts or permitting undocumented changes makes later investigation difficult. Governance should define which artifacts must be retained based on risk and business requirements. Reproducibility supports assurance, troubleshooting, incident investigation, regulatory inquiries, and model lifecycle management by allowing the organization to understand historical system behavior.<\/span><\/p>\n

                              Question 255. What is the purpose of maintaining an AI system\u2019s dependency inventory?<\/b><\/h3>\n
                                \n
                              1. <\/b> To identify components and services whose failure or change could affect the AI system<\/span><\/li>\n
                              2. <\/b> To ensure every system has the same architecture<\/span><\/li>\n
                              3. <\/b> To eliminate third-party providers<\/span><\/li>\n
                              4. <\/b> To record only employee information<\/span><\/li>\n<\/ol>\n

                                Correct Answer: 1. To identify components and services whose failure or change could affect the AI system<\/b><\/p>\n

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

                                An AI dependency inventory helps organizations understand the technical and external components required for a system to operate. Dependencies may include cloud services, APIs, model providers, software libraries, data pipelines, infrastructure, identity services, and external datasets. Understanding these dependencies supports risk assessment, change management, incident response, continuity planning, vulnerability management, and concentration-risk analysis. Without an accurate inventory, an organization may discover critical dependencies only after a disruption or security event occurs. The inventory should be maintained as the system evolves and should identify important relationships and ownership. It does not require eliminating third-party providers or standardizing every architecture.<\/span><\/p>\n

                                Question 256. Which governance control helps detect unauthorized modification of an AI model artifact?<\/b><\/h3>\n
                                  \n
                                1. <\/b> Increasing the number of AI users<\/span><\/li>\n
                                2. <\/b> Removing version history<\/span><\/li>\n
                                3. <\/b> Allowing unrestricted write access<\/span><\/li>\n
                                4. <\/b> Integrity verification combined with controlled access and version management<\/span><\/li>\n<\/ol>\n

                                  Correct Answer: 4. Integrity verification combined with controlled access and version management<\/b><\/p>\n

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

                                  Model artifacts should be protected against unauthorized or accidental modification because changes can affect system behavior and invalidate previous validation results. Integrity controls can include cryptographic hashes, signed artifacts, controlled repositories, access restrictions, version management, and monitoring of privileged activities. These measures provide evidence that the artifact deployed into production corresponds to the approved version. Unrestricted write access and removal of version history weaken accountability and make unauthorized changes difficult to detect. Integrity verification should be integrated with change management so that legitimate modifications are documented, tested, approved, and traceable. This provides stronger assurance over the reliability and provenance of AI model artifacts.<\/span><\/p>\n

                                  Question 257. What should governance require when a critical AI system depends on a single external data provider?<\/b><\/h3>\n
                                    \n
                                  1. <\/b> Immediate termination of the provider<\/span><\/li>\n
                                  2. <\/b> Assessment of dependency and concentration risk with appropriate contingency planning<\/span><\/li>\n
                                  3. <\/b> Automatic approval of all provider changes<\/span><\/li>\n
                                  4. <\/b> Elimination of all data validation<\/span><\/li>\n<\/ol>\n

                                    Correct Answer: 2. Assessment of dependency and concentration risk with appropriate contingency planning<\/b><\/p>\n

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

                                    A single external data provider can create concentration and availability risk if the provider experiences an outage, changes its service, introduces data-quality problems, becomes unavailable, or terminates the relationship. Governance should identify the dependency, assess its importance, evaluate the potential business impact, and determine whether contingency measures are appropriate. Depending on the system, alternatives may include secondary sources, validated backup datasets, contractual protections, monitoring, data quality controls, or alternative operating procedures. Immediate termination may be unnecessary and could itself create disruption. Automatic approval of provider changes is also inappropriate. The organization should maintain visibility into critical dependencies and ensure that resilience measures reflect the AI system\u2019s business importance.<\/span><\/p>\n

                                    Question 258. What is an important governance consideration when AI outputs are used to support human decisions?<\/b><\/h3>\n
                                      \n
                                    1. <\/b> Humans should automatically accept every AI recommendation<\/span><\/li>\n
                                    2. <\/b> Human reviewers should understand their responsibilities and have sufficient information to challenge inappropriate outputs<\/span><\/li>\n
                                    3. <\/b> Human oversight should be removed to improve efficiency<\/span><\/li>\n
                                    4. <\/b> AI outputs should never be documented<\/span><\/li>\n<\/ol>\n

                                      Correct Answer: 2. Human reviewers should understand their responsibilities and have sufficient information to challenge inappropriate outputs<\/b><\/p>\n

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

                                      Human oversight is meaningful only when reviewers understand their responsibilities and have enough information to identify potentially unreliable AI outputs. Governance should define when human review is mandatory, what evidence reviewers should consider, when they may override an AI recommendation, and how decisions and overrides should be documented. Simply placing a person in the workflow does not guarantee effective oversight if the reviewer lacks authority, training, time, or relevant information. Humans should not be expected to accept AI outputs automatically. Appropriate oversight helps address uncertainty, unusual cases, model limitations, and situations where the AI output conflicts with available evidence or established policy. Requirements should be proportionate to the impact of the decision.<\/span><\/p>\n

                                      Question 259. What should an organization do if AI monitoring identifies a sustained deterioration in model performance?<\/b><\/h3>\n
                                        \n
                                      1. <\/b> Ignore the trend until the next annual review<\/span><\/li>\n
                                      2. <\/b> Remove the monitoring threshold<\/span><\/li>\n
                                      3. <\/b> Investigate the cause, assess impact, and apply appropriate remediation or escalation<\/span><\/li>\n
                                      4. <\/b> Continue deployment without documenting the issue<\/span><\/li>\n<\/ol>\n

                                        Correct Answer: 3. Investigate the cause, assess impact, and apply appropriate remediation or escalation<\/b><\/p>\n

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

                                        Sustained performance deterioration can indicate model drift, changes in data, altered user behavior, infrastructure problems, external conditions, or other issues affecting reliability. Governance should require investigation to determine the cause and significance of the deterioration. The organization may need to retrain or replace the model, adjust controls, increase human oversight, restrict use, or activate contingency procedures depending on the impact. The issue and response should be documented so that accountability and lessons learned are preserved. Ignoring the trend or removing the threshold would undermine monitoring. The response should be proportionate to the system\u2019s risk and should follow predefined escalation and incident-management procedures where applicable.<\/span><\/p>\n

                                        Question 260. Which characteristic is most important for an AI governance decision to be auditable?<\/b><\/h3>\n
                                          \n
                                        1. <\/b> The decision is supported by documented evidence, rationale, authority, and relevant records<\/span><\/li>\n
                                        2. <\/b> The decision is made informally<\/span><\/li>\n
                                        3. <\/b> The decision is kept confidential from all authorized reviewers<\/span><\/li>\n
                                        4. <\/b> The decision is changed frequently without documentation<\/span><\/li>\n<\/ol>\n

                                          Correct Answer: 1. The decision is supported by documented evidence, rationale, authority, and relevant records<\/b><\/p>\n

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

                                          An auditable governance decision should allow an independent reviewer to understand what was decided, why it was decided, who had authority to make the decision, and what evidence supported it. Depending on the decision, relevant records may include risk assessments, validation results, policy requirements, approval conditions, stakeholder input, and documented exceptions. This evidence creates traceability and allows the organization to demonstrate that decisions followed established governance processes. Informal decisions or undocumented changes make accountability difficult and can create inconsistent treatment of similar AI systems. Auditability does not require retaining every piece of information indefinitely; records should be retained according to applicable governance, legal, regulatory, and business requirements.<\/span><\/p>\n

                                           <\/p>\n","protected":false},"excerpt":{"rendered":"

                                          View Full Isaca AAISM Exam Dumps and Practice Test Dumps   Question 241. What should an organization do before approving an AI system for a high-impact business process? Conduct a risk-based assessment and confirm required controls are in place Approve the system based only on expected financial benefits Skip testing because the system uses an […]<\/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\/16865"}],"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=16865"}],"version-history":[{"count":1,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/16865\/revisions"}],"predecessor-version":[{"id":16924,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/16865\/revisions\/16924"}],"wp:attachment":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/media?parent=16865"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/categories?post=16865"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/tags?post=16865"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}