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SISA CSPAI Authentic Exam Hub | CSPAI Official Study Guide

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SISA CSPAI Exam Syllabus Topics:

Topic
Details

Topic 1

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 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

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 4

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.

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SISA Certified Security Professional in Artificial Intelligence Sample Questions (Q14-Q19):

NEW QUESTION # 14
Which of the following describes the scenario where an LLM is embedded 'As-is' into an application frame?

A. Integrating the LLM into the application without modifications, using its out-of-the-box capabilities directly within the application.
B. Replacing the LLM with a more specialized model tailored to the application's needs.
C. Using the LLM solely for backend data processing, while the application handles all user interactions.
D. Customizing the LLM to fit specific application requirements and workflows before integration.

Answer: A

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 # 15
How does AI enhance customer experience in retail environments?

A. By optimizing customer service through automated systems and tailored recommendations.
B. By automating repetitive tasks and providing consistent data driven insights to improve customer service.
C. By ensuring every customer receives the same generic response from automated systems.
D. By integrating personalized interactions with AI-driven analytics for a more customized shopping experience.

Answer: D

Explanation:
AI enhances retail CX through personalization, using analytics to recommend products based on behavior, preferences, and history, creating tailored experiences that boost satisfaction and loyalty. Tools like chatbots and predictive models enable real-time interactions, while security posture improves via fraud detection integrated into these systems. This data-driven approach ensures relevance, differentiating from generic methods. Automation supports but personalization drives engagement. Exact extract: "AI integrates personalized interactions with driven analytics to customize shopping experiences, thereby enhancing customer satisfaction in retail." (Reference: Cyber Security for AI by SISA Study Guide, Section on GenAI in Security and Customer Enhancement, Page 70-73).

NEW QUESTION # 16
In a time-series prediction task, how does an RNN effectively model sequential data?

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

Answer: B

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 # 17
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 # 18
In a scenario where Open-Source LLMs are being used to create a virtual assistant, what would be the most effective way to ensure the assistant is continuously improving its interactions without constant retraining?

A. Shifting the assistant to a completely rule-based system to avoid reliance on user feedback.
B. Training a larger proprietary model to replace the open-source LLM
C. Implementing reinforcement learning from human feedback (RLHF) to refine responses based on user input.
D. Reducing the amount of feedback integrated to speed up deployment.

Answer: C

Explanation:
For continuous improvement in open-source LLM-based virtual assistants, RLHF integrates human evaluations to align model outputs with preferences, iteratively refining behavior without full retraining. This method uses reward models trained on feedback to guide policy optimization, enhancing interaction quality over time. It addresses limitations like initial biases or suboptimal responses by leveraging real-world user inputs, making the system adaptive and efficient. Unlike full retraining, RLHF is parameter-efficient and scalable, ideal for production environments. Security benefits include monitoring feedback for adversarial attempts. Exact extract: "Implementing RLHF allows continuous refinement of the assistant's interactions based on user feedback, avoiding the need for constant full retraining while improving performance." (Reference: Cyber Security for AI by SISA Study Guide, Section on AI Improvement Techniques in SDLC, Page 85-88).

NEW QUESTION # 19
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