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High-quality CSPAI Dumps Questions | Easy To Study and Pass Exam at first attempt & Reliable CSPAI: Certified Security Professional in Artificial Intelligence

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SISA CSPAI Exam Syllabus Topics:

Topic
Details

Topic 1

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.

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

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 Certified Security Professional in Artificial Intelligence Sample Questions (Q18-Q23):

NEW QUESTION # 18
A company's chatbot, Tay, was poisoned by malicious interactions. What is the primary lesson learned from this case study?

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

Answer: B

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 # 19
Fine-tuning an LLM on a single task involves adjusting model parameters to specialize in a particular domain.
What is the primary challenge associated with fine tuning for a single task compared to multi task fine tuning?

A. Single-task fine-tuning is less effective in generalizing to new, unseen tasks compared to multi-task fine- tuning.
B. Single-task fine-tuning tends to degrade the model's performance on the original tasks it was trained on.
C. Single-task fine-tuning requires significantly more data to achieve comparable performance to multi- task fine tuning.
D. Single-task fine-tuning introduces more complexity in managing different versions of the model compared to multi-task fine-tuning.

Answer: A

Explanation:
Single-task fine-tuning specializes the LLM but risks overfitting, limiting generalization to novel tasks unlike multi-task approaches that promote transfer learning across domains. This challenge requires careful regularization in SDLC to balance specificity and versatility, often needing more resources for version management. Exact extract: "Single-task fine-tuning is less effective in generalizing to new tasks compared to multi-task fine-tuning." (Reference: Cyber Security for AI by SISA Study Guide, Section on Fine-Tuning Challenges, Page 115-118).

NEW QUESTION # 20
In transformer models, how does the attention mechanism improve model performance compared to RNNs?

A. By processing each input independently, ensuring the model captures all aspects of the sequence equally.
B. By enabling the model to attend to both nearby and distant words simultaneously, improving its understanding of long-term dependencies
C. By dynamically assigning importance to every word in the sequence, enabling the model to focus on relevant parts of the input.
D. By enhancing the model's ability to process data in parallel, ensuring faster training without compromising context.

Answer: B

Explanation:
Transformer models leverage self-attention to process entire sequences concurrently, unlike RNNs, which handle inputs sequentially and struggle with long-range dependencies due to vanishing gradients. By computing attention scores across all words, Transformers capture both local and global contexts, enabling better modeling of relationships in tasks like translation or summarization. For example, in a long sentence, attention links distant pronouns to their subjects, improving coherence. This contrasts with RNNs' sequential limitations, which hinder capturing far-apart dependencies. While parallelism (option C) aids efficiency, the core improvement lies in dependency modeling, not just speed. Exact extract: "The attention mechanism enables Transformers to attend to nearby and distant words simultaneously, significantly improving long-term dependency understanding over RNNs." (Reference: Cyber Security for AI by SISA Study Guide, Section on Transformer vs. RNN Architectures, Page 50-53).

NEW QUESTION # 21
How can Generative AI be utilized to enhance threat detection in cybersecurity operations?

A. By automating the deletion of security logs to reduce storage costs.
B. By replacing all human analysts with AI-generated reports.
C. By generating random data to overload security systems.
D. By creating synthetic attack scenarios for training detection models.

Answer: D

Explanation:
Generative AI improves security posture by synthesizing realistic cyber threat scenarios, which can be used to train and test detection systems without exposing real networks to risks. This approach allows for the creation of diverse, evolving attack patterns that mimic advanced persistent threats, enabling machine learning models to learn from simulated data and improve accuracy in identifying anomalies. For example, GenAI can generate phishing emails or malware variants, helping in proactive defense tuning. This not only enhances detection rates but also reduces false positives through better model robustness. Integration into security operations centers (SOCs) facilitates continuous improvement, aligning with zero-trust architectures. Security benefits include cost-effective training and faster response to emerging threats. Exact extract: "Generative AI enhances threat detection by creating synthetic attack scenarios for training models, thereby improving the overall security posture without real-world risks." (Reference: Cyber Security for AI by SISA Study Guide, Section on GenAI Applications in Threat Detection, Page 200-203).

NEW QUESTION # 22
In assessing GenAI supply chain risks, what is a critical consideration?

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

Answer: C

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 # 23
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