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[General] Latest SISA CSPAI Test Question | CSPAI Reliable Real Exam

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【General】 Latest SISA CSPAI Test Question | CSPAI Reliable Real Exam

Posted at 5/15/2026 20:51:33      View：56 | Replies：0        Print      Only Author   [Copy Link] 1#
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SISA CSPAI Exam Syllabus Topics:
TopicDetails
Topic 1
  • Improving SDLC Efficiency Using Gen AI: This section of the exam measures skills of the AI Security Analyst and explores how generative AI can be used to streamline the software development life cycle. It emphasizes using AI for code generation, vulnerability identification, and faster remediation, all while ensuring secure development practices.
Topic 2
  • Using Gen AI for Improving the Security Posture: This section of the exam measures skills of the Cybersecurity Risk Manager and focuses on how Gen AI tools can strengthen an organization’s overall security posture. It includes insights on how automation, predictive analysis, and intelligent threat detection can be used to enhance cyber resilience and operational defense.
Topic 3
  • AIMS and Privacy Standards: ISO 42001 and ISO 27563: This section of the exam measures skills of the AI Security Analyst and addresses international standards related to AI management systems and privacy. It reviews compliance expectations, data governance frameworks, and how these standards help align AI implementation with global privacy and security regulations.

SISA Certified Security Professional in Artificial Intelligence Sample Questions (Q37-Q42):NEW QUESTION # 37
In a financial technology company aiming to implement a specialized AI solution, which approach would most effectively leverage existing AI models to address specific industry needs while maintaining efficiency and accuracy?
  • A. Integrating multiple separate Domain-Specific GenAI models for various financial functions without using a foundational model for consistency
  • B. Using a general Large Language Model (LLM) without adaptation, relying solely on its broad capabilities to handle financial tasks.
  • C. Adopting a Foundation Model as the base and fine-tuning it with domain-specific financial data to enhance its capabilities for forecasting and risk assessment.
  • D. Building a new, from scratch Domain-Specific GenAI model for financial tasks without leveraging preexisting models.
Answer: C
Explanation:
Leveraging foundation models like GPT or BERT for fintech involves fine-tuning with sector-specific data, such as transaction logs or market trends, to tailor for tasks like risk prediction, ensuring high accuracy without the overhead of scratch-building. This approach maintains efficiency by reusing pretrained weights, reducing training time and resources in SDLC, while domain adaptation mitigates generalization issues. It outperforms unadapted general models or fragmented specifics by providing cohesive, scalable solutions.
Security is enhanced through controlled fine-tuning datasets. Exact extract: "Adopting a Foundation Model and fine-tuning with domain-specific data is most effective for leveraging existing models in fintech, balancing efficiency and accuracy." (Reference: Cyber Security for AI by SISA Study Guide, Section on Model Adaptation in SDLC, Page 105-108).

NEW QUESTION # 38
How does ISO 27563 support privacy in AI systems?
  • A. By providing guidelines for privacy-enhancing technologies in AI.
  • B. By focusing on performance metrics over privacy.
  • C. By limiting AI to non-personal data only.
  • D. By mandating the use of specific encryption algorithms.
Answer: A
Explanation:
ISO 27563 offers practical guidance on implementing privacy-enhancing technologies (PETs) in AI, such as differential privacy or federated learning, to protect data while maintaining utility. It addresses risks like inference attacks, ensuring compliance with privacy regulations. Exact extract: "ISO 27563 supports privacy in AI by providing guidelines for privacy-enhancing technologies." (Reference: Cyber Security for AI by SISA Study Guide, Section on ISO 27563 for Privacy, Page 265-268).

NEW QUESTION # 39
Which of the following is a primary goal of enforcing Responsible AI standards and regulations in the development and deployment of LLMs?
  • A. Ensuring that AI systems operate safely, ethically, and without causing harm.
  • B. Maximizing model performance while minimizing computational costs.
  • C. Focusing solely on improving the speed and scalability of AI systems
  • D. Developing AI systems with the highest accuracy regardless of data privacy concerns
Answer: A
Explanation:
Responsible AI standards, including ISO 42001 for AI management systems, aim to promote ethical development, ensuring safety, fairness, and harm prevention in LLM deployments. This encompasses bias mitigation, transparency, and accountability, aligning with societal values. Regulations like the EU AI Act reinforce this by categorizing risks and mandating safeguards. The goal transcends performance to foster trust and sustainability, addressing issues like discrimination or misuse. Exact extract: "The primary goal is to ensure AI systems operate safely, ethically, and without causing harm, as outlined in standards like ISO
42001." (Reference: Cyber Security for AI by SISA Study Guide, Section on Responsible AI and ISO Standards, Page 150-153).

NEW QUESTION # 40
An organization is evaluating the risks associated with publishing poisoned datasets. What could be a significant consequence of using such datasets in training?
  • A. Increased model efficiency in processing and generation tasks.
  • B. Improved model performance due to higher data volume.
  • C. Enhanced model adaptability to diverse data types.
  • D. Compromised model integrity and reliability leading to inaccurate or biased outputs
Answer: D
Explanation:
Poisoned datasets introduce adversarial perturbations or malicious samples that, when used in training, can subtly alter a model's decision boundaries, leading to degraded integrity and unreliable outputs. This risk manifests as backdoors or biases, where the model performs well on clean data but fails or behaves maliciously on triggered inputs, compromising security in applications like classification or generation. For instance, in a facial recognition system, poisoned data might cause misidentification of certain groups, resulting in biased or inaccurate results. Mitigation involves rigorous data validation, anomaly detection, and diverse sourcing to ensure dataset purity. The consequence extends to ethical concerns, potential legal liabilities, and loss of trust in AI systems. Addressing this requires ongoing monitoring and adversarial training to bolster resilience. Exact extract: "Using poisoned datasets can compromise model integrity, leading to inaccurate, biased, or manipulated outputs, which undermines the reliability of AI systems and poses significant security risks." (Reference: Cyber Security for AI by SISA Study Guide, Section on Data Poisoning Risks, Page 112-115).

NEW QUESTION # 41
In line with the US Executive Order on AI, a company's AI application has encountered a security vulnerability. What should be prioritized to align with the order's expectations?
  • A. Immediate public disclosure of the vulnerability.
  • B. Implementing a rapid response to address and remediate the vulnerability, followed by a review of security practices.
  • C. Halting all AI projects until a full investigation is complete.
  • D. Ignoring the vulnerability if it does not affect core functionalities.
Answer: B
Explanation:
The US Executive Order on AI emphasizes proactive risk management and robust security to ensure safe AI deployment. When a vulnerability is detected, rapid response to remediate it, coupled with a thorough review of security practices, aligns with these mandates by minimizing harm and preventing recurrence. This approach involves patching the issue, assessing root causes, and updating protocols to strengthen defenses, ensuring compliance with standards like ISO 42001, which prioritizes risk mitigation in AI systems. Public disclosure, while important, is secondary to remediation to avoid premature exposure, and halting projects is overly disruptive unless risks are critical. Ignoring vulnerabilities contradicts responsible AI principles, risking regulatory penalties and trust erosion. This strategy fosters accountability and aligns with governance frameworks for secure AI operations. Exact extract: "Addressing vulnerabilities promptly through remediation and reviewing security practices is prioritized to meet the US Executive Order's expectations for safe and secure AI systems." (Reference: Cyber Security for AI by SISA Study Guide, Section on AI Governance and US EO Compliance, Page 165-168).

NEW QUESTION # 42
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