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[General] Unique Features of PassExamDumps's SISA CSPAI Exam Questions (Desktop and Web-Ba

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【General】 Unique Features of PassExamDumps's SISA CSPAI Exam Questions (Desktop and Web-Ba

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SISA Certified Security Professional in Artificial Intelligence Sample Questions (Q19-Q24):NEW QUESTION # 19
In the Retrieval-Augmented Generation (RAG) framework, which of the following is the most critical factor for improving factual consistency in generated outputs?
  • A. Utilising an ensemble of multiple LLMs to cross-check the generated outputs.
  • B. Implementing a redundancy check by comparing the outputs from different retrieval modules.
  • C. Tuning the retrieval model to prioritize documents with the highest semantic similarity
  • D. Fine-tuning the generative model with synthetic datasets generated from the retrieved documents
Answer: C
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 # 20
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. Training a larger proprietary model to replace the open-source LLM
  • B. Implementing reinforcement learning from human feedback (RLHF) to refine responses based on user input.
  • C. Reducing the amount of feedback integrated to speed up deployment.
  • D. Shifting the assistant to a completely rule-based system to avoid reliance on user feedback.
Answer: B
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 # 21
In a Retrieval-Augmented Generation (RAG) system, which key step is crucial for ensuring that the generated response is contextually accurate and relevant to the user's question?
  • A. Integrating advanced search algorithms to ensure the retrieval of highly relevant documents for context.
  • B. Leveraging a diverse set of data sources to enrich the response with varied perspectives
  • C. Retrieving relevant information from the vector database before generating a response
  • D. Utilizing feedback mechanisms to continuously improve the relevance of responses based on user interactions.
Answer: C
Explanation:
In RAG systems, retrieving relevant information from a vector database before generation is pivotal, as it grounds responses in verified, contextually aligned data. Using embeddings and similarity metrics, the system fetches documents matching the query's intent, ensuring accuracy and relevance. While diverse sources or feedback aid long-term improvement, the retrieval step directly drives contextual fidelity, streamlining SDLC by modularizing data access. Exact extract: "Retrieving relevant information from the vector database is crucial for ensuring contextually accurate responses in RAG systems." (Reference: Cyber Security for AI by SISA Study Guide, Section on RAG Optimization, Page 120-123).

NEW QUESTION # 22
In utilizing Giskard for vulnerability detection, what is a primary benefit of integrating this open-source tool into the security function?
  • A. Automatically patching vulnerabilities without additional configuration
  • B. Enabling real-time detection of vulnerabilities with actionable insights.
  • C. Reducing the need for manual vulnerability assessment entirely
  • D. Limiting its use to only high-priority vulnerabilities.
Answer: B
Explanation:
Giskard, an open-source tool, enhances AI security by enabling real-time vulnerability detection, scanning models for issues like bias or adversarial weaknesses, and providing actionable insights for remediation. This proactive approach supports continuous monitoring, unlike automated patching or limited scopes, and integrates into SDLC for robust security. Exact extract: "Giskard enables real-time detection of vulnerabilities with actionable insights, strengthening AI security functions." (Reference: Cyber Security for AI by SISA Study Guide, Section on Vulnerability Detection Tools, Page 190-193).

NEW QUESTION # 23
What is a key benefit of using GenAI for security analytics?
  • A. Reducing the use of analytics tools to save costs.
  • B. Limiting analysis to historical data only.
  • C. Increasing data silos to protect information.
  • D. Predicting future threats through pattern recognition in large datasets.
Answer: D
Explanation:
GenAI revolutionizes security analytics by mining massive datasets for patterns, predicting emerging threats like zero-day attacks through generative modeling. It synthesizes insights from disparate sources, enabling proactive defenses and anomaly detection with high precision. This foresight allows organizations to allocate resources effectively, preventing breaches before they occur. In practice, it integrates with SIEM systems for enhanced threat hunting. The benefit lies in transforming reactive security into predictive, bolstering posture against sophisticated adversaries. Exact extract: "A key benefit of GenAI in security analytics is predicting future threats via pattern recognition, improving proactive security measures." (Reference: Cyber Security for AI by SISA Study Guide, Section on Predictive Analytics with GenAI, Page 220-223).

NEW QUESTION # 24
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