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New CSPAI Exam Guide & CSPAI Exam Introduction

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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

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

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 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 (Q26-Q31):

NEW QUESTION # 26
What aspect of privacy does ISO 27563 emphasize in AI data processing?

A. Storing all data indefinitely for auditing.
B. Consent management and data minimization principles.
C. Maximizing data collection for better AI performance.
D. Sharing data freely among AI systems.

Answer: B

Explanation:
ISO 27563 stresses consent management, ensuring informed user agreement, and data minimization, collecting only necessary data to reduce privacy risks in AI processing. These principles prevent overreach and support ethical data handling. Exact extract: "ISO 27563 emphasizes consent management and data minimization in AI data processing for privacy." (Reference: Cyber Security for AI by SISA Study Guide, Section on Privacy Principles in ISO 27563, Page 275-278).

NEW QUESTION # 27
Which of the following is a characteristic of domain-specific Generative AI models?

A. They are tailored and fine-tuned for specific fields or industries
B. They are trained on broad datasets covering multiple domains
C. They are only used for computer vision tasks
D. They are designed to run exclusively on quantum computers

Answer: A

Explanation:
Domain-specific Generative AI models are refined versions of foundational models, adapted through fine- tuning on specialized datasets to excel in niche areas like healthcare, finance, or legal applications. This tailoring enhances precision, relevance, and efficiency by incorporating industry-specific jargon, patterns, and constraints, unlike general models that handle broad tasks but may lack depth. For example, a medical GenAI model might generate accurate diagnostic reports by focusing on clinical data, reducing errors in specialized contexts. This approach balances computational resources and performance, making them ideal for targeted deployments while maintaining the generative capabilities of larger models. Security implications include better control over sensitive domain data. Exact extract: "Domain-specific GenAI models are characterized by being tailored and fine-tuned for particular fields or industries, leveraging specialized data to achieve higher accuracy and relevance in those domains." (Reference: Cyber Security for AI by SISA Study Guide, Section on GenAI Model Types, Page 65-67).

NEW QUESTION # 28
How does machine learning improve the accuracy of predictive models in finance?

A. By avoiding any use of past data and focusing solely on current trends
B. By using historical data patterns to make predictions without updates
C. By relying exclusively on manual adjustments and human input for predictions.
D. By continuously learning from new data patterns to refine predictions

Answer: D

Explanation:
Machine learning enhances financial predictive models by continuously learning from new data, refining predictions for tasks like fraud detection or market forecasting. This adaptability leverages evolving patterns, unlike static historical or manual methods, and improves security posture through real-time anomaly detection. Exact extract: "ML improves financial predictive accuracy by continuously learning from new data patterns to refine predictions." (Reference: Cyber Security for AI by SISA Study Guide, Section on ML in Financial Security, Page 85-88).

NEW QUESTION # 29
In transformer models, how does the attention mechanism improve model performance compared to RNNs?

A. By enhancing the model's ability to process data in parallel, ensuring faster training without compromising context.
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 processing each input independently, ensuring the model captures all aspects of the sequence equally.

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 # 30
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 introduces more complexity in managing different versions of the model 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 is less effective in generalizing to new, unseen tasks compared to multi-task fine- tuning.

Answer: D

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 # 31
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