높은적중율을자랑하는AAISM시험공부자료

IT업계에 종사하고 계신 분은ISACA AAISM 시험을 패스하여 자격증을 취득하려고 검색하다 저희 블로그를 보게 되시고 저희 사이트까지 방문하게 될것입니다. 방문하는 순간 ISACA AAISM시험에 대한 두려움이 사라질것입니다. 완벽한 구매후 서비스까지 겸비하고 있어 자격증을 취득하는데서의 믿음직스러운 동반자로 되어드릴게요.

ISACA인증 AAISM시험을 등록했는데 마땅한 공부자료가 없어 고민중이시라면PassTIP의ISACA인증 AAISM덤프를 추천해드립니다. PassTIP의ISACA인증 AAISM덤프는 거의 모든 시험문제를 커버하고 있어 시험패스율이 100%입니다. PassTIP제품을 선택하시면 어려운 시험공부도 한결 가벼워집니다.

>> AAISM시험 <<

AAISM인기자격증 최신시험 덤프자료 & AAISM최고품질 덤프문제보기

PassTIP는 가장 효율높은 ISACA AAISM시험대비방법을 가르쳐드립니다. 저희 ISACA AAISM덤프는 실제 시험문제의 모든 범위를 커버하고 있어 ISACA AAISM덤프의 문제만 이해하고 기억하신다면 제일 빠른 시일내에 시험패스할수 있습니다. 경쟁율이 심한 IT시대에 ISACA AAISM시험 패스만으로 이 사회에서 자신만의 위치를 보장할수 있고 더욱이는 한층 업된 삶을 누릴수도 있습니다.

최신 Isaca Certification AAISM 무료샘플문제 (Q61-Q66):

질문 # 61
Which of the following would BEST help mitigate vulnerabilities associated with hidden triggers in generative AI models?

A. Monitoring model outputs and suspicious patterns to detect trigger activations
B. Regularly retraining the model using a diverse data set
C. Incorporating adversarial training to expose and neutralize potential triggers
D. Applying differential privacy and masking sensitive patterns in the training data

정답：C

설명：
Hidden triggers are adversarial backdoors planted in AI models, activated only by specific inputs. The AAISM materials specify that the best mitigation is to use adversarial training, which deliberately exposes the model to potential trigger inputs during training so it can learn to neutralize or resist them. Retraining with diverse data reduces bias but does not address hidden triggers. Differential privacy is focused on privacy preservation, not adversarial resilience. Monitoring outputs can help with detection but is reactive rather than preventative. The proactive solution highlighted in the study guide is adversarial training.
References:
AAISM Exam Content Outline - AI Risk Management (Backdoors and Hidden Triggers) AI Security Management Study Guide - Adversarial Training as a Mitigation Control

질문 # 62
An organization plans to apply an AI system to its business, but developers find it difficult to predict system results due to lack of visibility to the inner workings of the AI model. Which of the following is the GREATEST challenge associated with this situation?

A. Continuing operations to meet expected AI security requirements
B. Gaining the trust of end users through explainability and transparency
C. Determining average turnaround time for AI transaction completion
D. Assigning a risk owner who is responsible for system uptime and performance

정답：B

설명：
AAISM materials identify explainability and transparency as the greatest challenge when models operate as
"black boxes" where inner logic is opaque. Inability to interpret how results are produced undermines the trust of business users, customers, regulators, and auditors. Explainability is emphasized as a critical governance requirement, because without it, ethical validation, accountability, and regulatory compliance are at risk.
Assigning risk owners or measuring transaction times are operational concerns, but they do not address the core trust deficit caused by lack of visibility. The greatest challenge in this situation is therefore the loss of end-user trust due to insufficient explainability.
References:
AAISM Study Guide - AI Governance and Program Management (Transparency and Explainability) ISACA AI Security Management - Ethical and Trust Considerations

질문 # 63
How can an organization BEST protect itself from payment diversions caused by deepfake attacks impersonating management?

A. Implement resilient payment approval processes
B. Mandate that payments be sent only once per week
C. Issue a security policy on deepfakes
D. Require mandatory deepfake detection training for all employees

정답：A

설명：
AAISM's risk management framework stresses that the most effective defense against deepfake-enabled fraud, such as payment diversion, is resilient payment approval processes. This includes multi-step verification, segregation of duties, and independent confirmations for high-value transactions. Employee training, policies, or limiting payment frequency may reduce exposure, but they cannot guarantee prevention.
Only process-based controls enforce structural safeguards that prevent fraudulent instructions from being executed, even if a deepfake impersonation attempt is successful.
References:
AAISM Exam Content Outline - AI Risk Management (Fraud and Deepfake Risk) AI Security Management Study Guide - Transactional Resilience and Controls

질문 # 64
Which of the following technologies can be used to manage deepfake risk?

A. Multi-factor authentication (MFA)
B. Adaptive authentication
C. Blockchain
D. Systematic data tagging

정답：C

설명：
The AAISM study material highlights blockchain as a control mechanism for managing deepfake risk because it provides immutable verification of digital media provenance. By anchoring original data signatures on a blockchain, organizations can verify authenticity and detect tampered or synthetic content. Data tagging helps organize but does not guarantee authenticity. MFA and adaptive authentication strengthen identity security but do not address content manipulation risks. Blockchain's immutability and traceability make it the recognized technology for mitigating deepfake challenges.
References:
AAISM Study Guide - AI Technologies and Controls (Emerging Controls for Content Authenticity) ISACA AI Governance Guidance - Blockchain for Data Integrity and Deepfake Mitigation

질문 # 65
Which of the following is the GREATEST risk inherent to implementing generative AI?

A. Potential intellectual property violations
B. Unidentified asset vulnerabilities
C. Inadequate return on investment (ROI)
D. Lack of employee training

정답：A

설명：
The AAISM framework identifies intellectual property (IP) violations as the most significant inherent risk in deploying generative AI. These systems often rely on large-scale internet data for training, which may inadvertently contain copyrighted or proprietary material. This creates legal and reputational exposure when outputs reproduce or reference protected content. While employee training gaps, asset vulnerabilities, and ROI concerns are relevant risks, they are not inherent to generative models themselves. The greatest inherent risk tied directly to generative AI adoption is the possibility of violating intellectual property rights.
References:
AAISM Study Guide - AI Risk Management (Generative AI Risks and Legal Exposure) ISACA AI Security Management - Copyright and IP Concerns in Generative AI

질문 # 66
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PassTIP의 ISACA인증AAISM시험대비덤프는 실제시험문제 출제경향을 충분히 연구하여 제작한 완벽한 결과물입니다.실제시험문제가 바뀌면 덤프를 제일 빠른 시일내에 업데이트하도록 하기에 한번 구매하시면 1년동안 항상 가장 최신의ISACA인증AAISM시험덤프자료를 제공받을수 있습니다.

AAISM인기자격증 최신시험 덤프자료: https://www.passtip.net/AAISM-pass-exam.html

100%합격가능한 ISACA AAISM덤프는ISACA AAISM시험문제의 기출문제와 예상문제로 되어있는 퍼펙트한 모음문제집으로서 시험패스율이 100%에 가깝습니다, 우리PassTIP AAISM인기자격증 최신시험 덤프자료에는 아주 엘리트 한 전문가들로 구성된 팀입니다 그들은 끈임 없는 연구와 자기자신만의 지식으로 많은 IT관연 덤프자료를 만들어 냄으로 여러분의 꿈을 이루어드립니다, 기존의 시험문제와 답과 시험문제분석 등입니다, PassTIP의ISACA인증 AAISM덤프는 실제시험문제의 출제방형을 철저하게 연구해낸 말 그대로 시험대비공부자료입니다, ISACA AAISM시험 여러분께서는 아주 순조로이 시험을 패스하실 수 있을 것입니다.

잔뜩 곤란해진 목소리로 수영이 찬찬히 말했다, 두 사람 사이에 흐르는 농도 깊은 침묵, 100%합격가능한 ISACA AAISM덤프는ISACA AAISM시험문제의 기출문제와 예상문제로 되어있는 퍼펙트한 모음문제집으로서 시험패스율이 100%에 가깝습니다.