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Title: CSPAI Popular Exams | CSPAI Practice Exam Online [Print This Page]
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Title: CSPAI Popular Exams | CSPAI Practice Exam Online
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SISA CSPAI Exam Syllabus Topics:
TopicDetails
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
  • 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 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.
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.

SISA Certified Security Professional in Artificial Intelligence Sample Questions (Q19-Q24):NEW QUESTION # 19
Which of the following describes the scenario where an LLM is embedded 'As-is' into an application frame?
Answer: D
Explanation:
Embedding an LLM 'as-is' means direct integration of the pretrained model into the app framework without alterations, relying on its inherent capabilities for tasks like text generation, simplifying SDLC by avoiding customization overhead. This is suitable for general-purpose apps but may lack optimization for specifics, contrasting with tailored approaches. It accelerates deployment while posing risks like unmitigated biases, necessitating post-integration safeguards. Exact extract: "It describes integrating the LLM without modifications, using out-of-the-box capabilities directly in the application." (Reference: Cyber Security for AI by SISA Study Guide, Section on LLM Integration Methods, Page 110-113).

NEW QUESTION # 20
How does the multi-head self-attention mechanism improve the model's ability to learn complex relationships in data?
Answer: A
Explanation:
Multi-head self-attention enhances a model's capacity to capture intricate patterns by dividing the attention process into multiple parallel 'heads,' each learning distinct aspects of the relationships within the data. This diversification enables the model to attend to various subspaces of the input simultaneously-such as syntactic, semantic, or positional features-leading to richer representations. For example, one head might focus on nearby words for local context, while another captures global dependencies, aggregating these insights through concatenation and linear transformation. This approach mitigates the limitations of single- head attention, which might overlook nuanced interactions, and promotes better generalization in complex datasets. In practice, it results in improved performance on tasks like NLP and vision, where multifaceted relationships are key. The mechanism's parallelism also aids in scalability, allowing deeper insights without proportional computational increases. Exact extract: "Multi-head attention improves learning by permitting the model to jointly attend to information from different representation subspaces at different positions, thus capturing complex relationships more effectively than a single attention head." (Reference: Cyber Security for AI by SISA Study Guide, Section on Transformer Mechanisms, Page 48-50).

NEW QUESTION # 21
When dealing with the risk of data leakage in LLMs, which of the following actions is most effective in mitigating this issue?
Answer: B
Explanation:
Data leakage in LLMs occurs when sensitive information from training data is inadvertently revealed in outputs, posing privacy risks. Effective mitigation involves strict access controls, such as role-based permissions, and anonymization methods like differential privacy or tokenization to obscure personal data.
These measures prevent extraction attacks while maintaining model utility. Regular audits and data minimization further strengthen defenses. Unlike obfuscation alone, which may not fully protect, combined controls ensure compliance with regulations like GDPR. Exact extract: "Applying rigorous access controls and anonymization techniques to training data is most effective in mitigating data leakage risks in LLMs." (Reference: Cyber Security for AI by SISA Study Guide, Section on Data Security in AI Models, Page 130-
133).

NEW QUESTION # 22
In a Transformer model processing a sequence of text for a translation task, how does incorporating positional encoding impact the model's ability to generate accurate translations?
Answer: B
Explanation:
Positional encoding in Transformers addresses the lack of inherent sequential information in self-attention by embedding word order into token representations, using functions like sine and cosine to assign unique positional vectors. This enables the model to differentiate word positions, crucial for translation where syntax and context depend on sequence (e.g., subject-verb-object order). Without it, Transformers treat inputs as bags of words, losing syntactic accuracy. Positional encoding ensures precise contextual understanding, unlike options that misrepresent its role. Exact extract: "Positional encoding helps Transformers distinguish word order, leading to more accurate translations by maintaining positional context." (Reference: Cyber Security for AI by SISA Study Guide, Section on Transformer Components, Page 55-57).

NEW QUESTION # 23
How does GenAI contribute to incident response in cybersecurity?
Answer: D
Explanation:
GenAI enhances incident response by dynamically generating customized playbooks based on threat intelligence and orchestrating automated actions like isolation or patching. It processes vast logs in real-time, correlating events to prioritize alerts and suggest optimal responses, reducing mean time to respond (MTTR).
For complex incidents, it simulates outcomes of different strategies, aiding decision-making. This automation frees analysts for strategic tasks, improving efficiency and effectiveness in containing breaches. Exact extract:
"GenAI contributes to incident response by automating playbook generation and orchestration, enhancing cybersecurity operations." (Reference: Cyber Security for AI by SISA Study Guide, Section on AI in Incident Response, Page 215-218).

NEW QUESTION # 24
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