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SISA Certified Security Professional in Artificial Intelligence Sample Questions (Q48-Q53):

NEW QUESTION # 48
In the Retrieval-Augmented Generation (RAG) framework, which of the following is the most critical factor for improving factual consistency in generated outputs?

A. Tuning the retrieval model to prioritize documents with the highest semantic similarity
B. Implementing a redundancy check by comparing the outputs from different retrieval modules.
C. Fine-tuning the generative model with synthetic datasets generated from the retrieved documents
D. Utilising an ensemble of multiple LLMs to cross-check the generated outputs.

Answer: A

Explanation:
The Retrieval-Augmented Generation (RAG) framework enhances generative models by incorporating external knowledge retrieval to ground outputs in factual data, thereby improving consistency and reducing hallucinations. The critical factor lies in optimizing the retrieval component to select documents with maximal semantic relevance, often using techniques like dense vector embeddings (e.g., via BERT or similar encoders) and similarity metrics such as cosine similarity. This ensures that the generator receives contextually precise information, minimizing irrelevant or misleading inputs that could lead to inconsistent outputs. For instance, in question-answering systems, prioritizing high-similarity documents allows the model to reference verified sources directly, boosting accuracy. Other approaches, like ensembles or redundancy checks, are supplementary but less foundational than effective retrieval tuning, which directly impacts the quality of augmented context. In SDLC, integrating RAG with fine-tuned retrieval accelerates development cycles by enabling modular updates without full model retraining. Security benefits include tracing outputs to sources for auditability, aligning with responsible AI practices. This method scales well for large knowledge bases, making it essential for production-grade applications where factual integrity is paramount. Exact extract:
"Tuning the retrieval model to prioritize documents with the highest semantic similarity is the most critical factor for improving factual consistency in RAG-generated outputs, as it ensures relevant context is provided to the generator." (Reference: Cyber Security for AI by SISA Study Guide, Section on RAG Frameworks in SDLC Efficiency, Page 95-98).

NEW QUESTION # 49
What is the main objective of ISO 42001 in AI management systems?

A. To establish requirements for an AI management system within organizations.
B. To regulate hardware used in AI deployments.
C. To provide guidelines only for small-scale AI projects.
D. To focus solely on technical specifications for AI algorithms.

Answer: A

Explanation:
ISO 42001 outlines a framework for organizations to manage AI responsibly, covering risk assessment, governance, and continual improvement. It ensures alignment with ethical principles, promoting trustworthy AI through structured processes. Applicable across sectors, it integrates with existing management systems like ISO 27001. Exact extract: "The main objective of ISO 42001 is to establish requirements for an AI management system in organizations." (Reference: Cyber Security for AI by SISA Study Guide, Section on ISO 42001 Overview, Page 260-263).

NEW QUESTION # 50
What aspect of privacy does ISO 27563 emphasize in AI data processing?

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

Answer: D

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 # 51
Which of the following is a potential use case of Generative AI specifically tailored for CXOs (Chief Experience Officers)?

A. Conducting genetic sequencing for personalized medicine
B. Developing autonomous vehicles for urban mobility solutions.
C. Automating financial transactions in blockchain networks.
D. Enhancing customer support through AI-powered chatbots that provide 24/7 assistance.

Answer: D

Explanation:
For CXOs focused on customer experience, Generative AI excels in powering chatbots that deliver round-the- clock, personalized support, addressing queries with context-aware responses. This enhances user satisfaction by reducing wait times and tailoring interactions using predictive analytics, while integrated security measures like anomaly detection safeguard against threats like phishing. Unlike unrelated applications like autonomous vehicles or genetic sequencing, chatbots directly align with CXO goals of improving engagement and trust.
Security posture is bolstered by monitoring interactions for malicious inputs, ensuring safe AI-driven CX.
Exact extract: "Generative AI enhances customer support through AI-powered chatbots providing 24/7 assistance, tailored for CXOs to improve engagement and security." (Reference: Cyber Security for AI by SISA Study Guide, Section on GenAI for CX Enhancement, Page 75-78).

NEW QUESTION # 52
What is a common use of an LLM as a Secondary Chatbot?

A. To handle tasks unrelated to the main application
B. To replace the primary AI system
C. To serve as a fallback or supplementary AI assistant for more complex queries
D. To only manage user credentials

Answer: C

Explanation:
A secondary chatbot, powered by an LLM, acts as a fallback or supplementary assistant, handling complex or overflow queries when the primary system is insufficient. This enhances CX by ensuring continuity and depth in responses, with security benefits like isolating sensitive tasks to a monitored secondary layer. Unlike replacing primary systems or handling unrelated tasks, this role leverages LLMs' flexibility to complement, not supplant, core functionalities. Exact extract: "LLMs as secondary chatbots serve as fallback assistants for complex queries, improving system resilience and user experience." (Reference: Cyber Security for AI by SISA Study Guide, Section on AI in Support Systems, Page 80-82).

NEW QUESTION # 53
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