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CSPAI Latest Test Cram - VCE CSPAI Dumps

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SISA CSPAI Exam Syllabus Topics:

Topic
Details

Topic 1

Evolution of Gen AI and Its Impact: This section of the exam measures skills of the AI Security Analyst and covers how generative AI has evolved over time and the implications of this evolution for cybersecurity. It focuses on understanding the broader impact of Gen AI technologies on security operations, threat landscapes, and risk management strategies.

Topic 2

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 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.

Topic 4

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 5

Models for Assessing Gen AI Risk: This section of the exam measures skills of the Cybersecurity Risk Manager and deals with frameworks and models used to evaluate risks associated with deploying generative AI. It includes methods for identifying, quantifying, and mitigating risks from both technical and governance perspectives.

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SISA Certified Security Professional in Artificial Intelligence Sample Questions (Q42-Q47):

NEW QUESTION # 42
How does ISO 27563 support privacy in AI systems?

A. By focusing on performance metrics over privacy.
B. By mandating the use of specific encryption algorithms.
C. By limiting AI to non-personal data only.
D. By providing guidelines for privacy-enhancing technologies in AI.

Answer: D

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 # 43
What is a potential risk associated with hallucinations in LLMs, and how should it be addressed to ensure Responsible AI?

A. Hallucinations can lead to creative outputs, which are beneficial for all applications; hence, no measures are necessary.
B. Hallucinations can produce inaccurate or misleading information; it should be addressed by incorporating external knowledge bases and retrieval systems.
C. Hallucinations cause models to slow down; optimizing hardware performance is necessary to mitigate this issue.
D. Hallucinations are primarily due to overfitting; regularization techniques should be applied during training.

Answer: B

Explanation:
Hallucinations in LLMs risk generating inaccurate or misleading outputs, undermining trust and safety.
Incorporating external knowledge bases and retrieval systems, like RAG, grounds responses in verified data, reducing fabrications and aligning with Responsible AI principles. Regularization helps but is secondary to factual grounding. Exact extract: "Hallucinations produce misleading information, addressed by incorporating external knowledge bases and retrieval systems for Responsible AI." (Reference: Cyber Security for AI by SISA Study Guide, Section on LLM Hallucination Mitigation, Page 125-128).

NEW QUESTION # 44
A company's chatbot, Tay, was poisoned by malicious interactions. What is the primary lesson learned from this case study?

A. Encrypting user data can prevent such attacks
B. Chatbots should have limited conversational abilities to prevent poisoning.
C. Continuous live training is essential for enhancing chatbot performance.
D. Open interaction with users without safeguards can lead to model poisoning and generation of inappropriate content.

Answer: D

Explanation:
The Tay incident, where Microsoft's chatbot was manipulated via toxic inputs to produce offensive content, underscores the dangers of unfiltered live learning, leading to rapid poisoning. Key lesson: Implement safeguards like content filters, rate limits, and moderated feedback loops to prevent adversarial exploitation.
This informs AI security by emphasizing input validation and ethical alignment in interactive systems. Exact extract: "Open interactions without safeguards can lead to model poisoning and inappropriate content, as seen in the Tay case." (Reference: Cyber Security for AI by SISA Study Guide, Section on Case Studies in AI Poisoning, Page 160-163).

NEW QUESTION # 45
When deploying LLMs in production, what is a common strategy for parameter-efficient fine-tuning?

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

Answer: A

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 # 46
In assessing GenAI supply chain risks, what is a critical consideration?

A. Evaluating third-party components for embedded vulnerabilities.
B. Ignoring open-source dependencies to reduce complexity.
C. Assuming all vendors comply with standards automatically.
D. Focusing only on internal development risks.

Answer: A

Explanation:
GenAI supply chain risk assessment prioritizes scrutinizing third-party libraries, datasets, and models for vulnerabilities like backdoors or biases, using tools for dependency scanning. This holistic view prevents cascade failures, as seen in compromised pretrained models. Mitigation includes vendor audits and secure sourcing. Exact extract: "A critical consideration in GenAI supply chain risks is evaluating third-party components for vulnerabilities." (Reference: Cyber Security for AI by SISA Study Guide, Section on Supply Chain Risk Assessment, Page 250-253).

NEW QUESTION # 47
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