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2025 Valid CSPAI Test Online - High-quality SISA CSPAI Exam Cost: Certified Security Professional in Artificial Intelligence

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SISA CSPAI Exam Syllabus Topics:

Topic
Details

Topic 1

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.

Topic 2

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.

Topic 3

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.

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SISA CSPAI Online Practice Test (SISA-CSPAI-Practice-Test)

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SISA Certified Security Professional in Artificial Intelligence Sample Questions (Q33-Q38):

NEW QUESTION # 33
Which framework is commonly used to assess risks in Generative AI systems according to NIST?

A. Using outdated models from traditional software risk assessment.
B. A general IT risk assessment without AI-specific considerations.
C. Focusing solely on financial risks associated with AI deployment.
D. The AI Risk Management Framework (AI RMF) for evaluating trustworthiness.

Answer: D

Explanation:
The NIST AI Risk Management Framework (AI RMF) provides a structured approach to identify, assess, and mitigate risks in GenAI, emphasizing trustworthiness attributes like safety, fairness, and explainability. It categorizes risks into governance, mapping, measurement, and management phases, tailored for AI lifecycles.
For GenAI, it addresses unique risks such as hallucinations or bias amplification. Organizations apply it to conduct impact assessments and implement controls, ensuring compliance and ethical deployment. Exact extract: "NIST's AI RMF is commonly used to assess risks in Generative AI, focusing on trustworthiness and lifecycle management." (Reference: Cyber Security for AI by SISA Study Guide, Section on NIST Frameworks for AI Risk, Page 230-233).

NEW QUESTION # 34
In a time-series prediction task, how does an RNN effectively model sequential data?

A. By focusing on the overall sequence structure rather than individual time steps for a more holistic approach.
B. By processing each time step independently, optimizing the model's performance over time.
C. By storing only the most recent time step, ensuring efficient memory usage for real-time predictions
D. By using hidden states to retain context from prior time steps, allowing it to capture dependencies across the sequence.

Answer: D

Explanation:
RNNs model sequential data in time-series tasks by maintaining hidden states that propagate information across time steps, capturing temporal dependencies like trends or seasonality. This memory mechanism allows RNNs to learn from past data, unlike independent processing or holistic approaches, though they face gradient issues for long sequences. Exact extract: "RNNs use hidden states to retain context from prior time steps, effectively capturing dependencies in sequential data for time-series tasks." (Reference: Cyber Security for AI by SISA Study Guide, Section on RNN Architectures, Page 40-43).

NEW QUESTION # 35
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. Ignoring the vulnerability if it does not affect core functionalities.
B. Halting all AI projects until a full investigation is complete.
C. Immediate public disclosure of the vulnerability.
D. Implementing a rapid response to address and remediate the vulnerability, followed by a review of security practices.

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 # 36
When deploying LLMs in production, what is a common strategy for parameter-efficient fine-tuning?

A. Training the model from scratch on the target task to achieve optimal performance.
B. Using external reinforcement learning to adjust the model's parameters dynamically.
C. Implementing multiple independent models for each specific task instead of fine tuning a single model
D. Freezing the majority of model parameters and only updating a small subset relevant to the task

Answer: D

Explanation:
Parameter-efficient fine-tuning (PEFT) strategies, like LoRA or adapters, freeze most pretrained parameters and train only lightweight modules, reducing computational costs while adapting to new tasks. This preserves general knowledge, prevents catastrophic forgetting, and enables quick deployments in resource-constrained settings. For LLMs, it's crucial for efficiency in production, allowing specialization without retraining billions of parameters. Security-wise, it minimizes exposure to new data risks. Exact extract: "A common strategy is freezing the majority of model parameters and updating only a small task-relevant subset, ensuring efficiency in fine-tuning for production deployment." (Reference: Cyber Security for AI by SISA Study Guide, Section on Efficient Fine-Tuning in SDLC, Page 90-92).

NEW QUESTION # 37
In utilizing Giskard for vulnerability detection, what is a primary benefit of integrating this open-source tool into the security function?

A. Limiting its use to only high-priority vulnerabilities.
B. Automatically patching vulnerabilities without additional configuration
C. Enabling real-time detection of vulnerabilities with actionable insights.
D. Reducing the need for manual vulnerability assessment entirely

Answer: C

Explanation:
Giskard, an open-source tool, enhances AI security by enabling real-time vulnerability detection, scanning models for issues like bias or adversarial weaknesses, and providing actionable insights for remediation. This proactive approach supports continuous monitoring, unlike automated patching or limited scopes, and integrates into SDLC for robust security. Exact extract: "Giskard enables real-time detection of vulnerabilities with actionable insights, strengthening AI security functions." (Reference: Cyber Security for AI by SISA Study Guide, Section on Vulnerability Detection Tools, Page 190-193).

NEW QUESTION # 38
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