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Title: 100% Pass Quiz 2026 CSPAI: Fantastic Top Certified Security Professional in Arti [Print This Page]
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Title: 100% Pass Quiz 2026 CSPAI: Fantastic Top Certified Security Professional in Arti
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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
  • Securing AI Models and Data: This section of the exam measures skills of the Cybersecurity Risk Manager and focuses on the protection of AI models and the data they consume or generate. Topics include adversarial attacks, data poisoning, model theft, and encryption techniques that help secure the AI lifecycle.

SISA Certified Security Professional in Artificial Intelligence Sample Questions (Q47-Q52):NEW QUESTION # 47
In the Retrieval-Augmented Generation (RAG) framework, which of the following is the most critical factor for improving factual consistency in generated outputs?
Answer: A
Explanation:
The Retrieval-Augmented Generation (RAG) framework enhances generative models by incorporating external knowledge retrieval to ground outputs in factual data, thereby improving consistency and reducing hallucinations. The critical factor lies in optimizing the retrieval component to select documents with maximal semantic relevance, often using techniques like dense vector embeddings (e.g., via BERT or similar encoders) and similarity metrics such as cosine similarity. This ensures that the generator receives contextually precise information, minimizing irrelevant or misleading inputs that could lead to inconsistent outputs. For instance, in question-answering systems, prioritizing high-similarity documents allows the model to reference verified sources directly, boosting accuracy. Other approaches, like ensembles or redundancy checks, are supplementary but less foundational than effective retrieval tuning, which directly impacts the quality of augmented context. In SDLC, integrating RAG with fine-tuned retrieval accelerates development cycles by enabling modular updates without full model retraining. Security benefits include tracing outputs to sources for auditability, aligning with responsible AI practices. This method scales well for large knowledge bases, making it essential for production-grade applications where factual integrity is paramount. Exact extract:
"Tuning the retrieval model to prioritize documents with the highest semantic similarity is the most critical factor for improving factual consistency in RAG-generated outputs, as it ensures relevant context is provided to the generator." (Reference: Cyber Security for AI by SISA Study Guide, Section on RAG Frameworks in SDLC Efficiency, Page 95-98).

NEW QUESTION # 48
What role does GenAI play in automating vulnerability scanning and remediation processes?
Answer: C
Explanation:
GenAI automates vulnerability management by analyzing scan results and generating tailored code patches or remediation strategies, accelerating the fix process and reducing human error. Using natural language processing, it interprets vulnerability reports, cross-references with known exploits, and proposes secure code alternatives, integrating seamlessly into DevSecOps pipelines. This proactive approach minimizes exposure windows and enhances system resilience against exploits. For instance, in cloud environments, GenAI can simulate patch impacts before application. This contributes to a stronger security posture by enabling rapid, accurate responses to threats. Exact extract: "GenAI automates vulnerability scanning and remediation by generating code patches and fixes, improving efficiency and security posture." (Reference: Cyber Security for AI by SISA Study Guide, Section on Automation in Vulnerability Management, Page 205-208).

NEW QUESTION # 49
A company developing AI-driven medical diagnostic tools is expanding into the European market. To ensure compliance with local regulations, what should be the company's primary focus in adhering to the EU AI Act?
Answer: C
Explanation:
The EU AI Act classifies AI systems by risk, with medical diagnostics as high-risk, requiring stringent safety measures to prevent harm, such as misdiagnoses. Compliance prioritizes robust testing, validation, and monitoring to ensure safe outcomes, aligning with ISO 42001's risk management framework. While ethics and privacy are critical, safety is the primary focus to meet regulatory thresholds and protect users. Exact extract: "The EU AI Act emphasizes implementing measures to prevent harmful outcomes and ensure AI system safety, particularly for high-risk applications like medical diagnostics." (Reference: Cyber Security for AI by SISA Study Guide, Section on EU AI Act Compliance, Page 175-178).

NEW QUESTION # 50
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?
Answer: D
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 # 51
For effective AI risk management, which measure is crucial when dealing with penetration testing and supply chain security?
Answer: D
Explanation:
Effective AI risk management requires comprehensive penetration testing and continuous evaluation of both internal and third-party supply chain components to identify vulnerabilities like backdoors or weak APIs. This holistic approach, aligned with SISA risk models, ensures robust security across the AI ecosystem, unlike limited or external-only testing. Exact extract: "Comprehensive penetration testing and continuous evaluation of internal and third-party components are crucial for AI risk management." (Reference: Cyber Security for AI by SISA Study Guide, Section on AI Risk Assessment Models, Page 180-183).

NEW QUESTION # 52
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