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SISA Certified Security Professional in Artificial Intelligence Sample Questions (Q42-Q47):
NEW QUESTION # 42
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. Utilizing feedback mechanisms to continuously improve the relevance of responses based on user interactions.
- D. Retrieving relevant information from the vector database before generating a response
Answer: D
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 # 43
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 only used for computer vision tasks
- C. They are trained on broad datasets covering multiple domains
- 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 # 44
Which of the following is a method in which simulation of various attack scenarios are applied to analyze the model's behavior under those conditions.
- A. input sanitation
- B. Adversarial testing
- C. Prompt injections
- D. Model firewall
- E. Adversarial testing involves systematically simulating attack vectors, such as input perturbations or evasion techniques, to evaluate an AI model's robustness and identify vulnerabilities before deployment. This proactive method replicates real-world threats, like adversarial examples that fool classifiers or prompt manipulations in LLMs, allowing developers to observe behavioral anomalies, measure resilience, and implement defenses like adversarial training or input validation. Unlike passive methods like input sanitation, which cleans data reactively, adversarial testing is dynamic and comprehensive, covering scenarios from data poisoning to model inversion. In practice, tools like CleverHans or ART libraries facilitate these simulations, providing metrics on attack success rates and model degradation. This is crucial for securing AI models, as it uncovers hidden weaknesses that could lead to exploits, ensuring compliance with security standards. By iterating through attack-defense cycles, it enhances overall data and model integrity, reducing risks in high-stakes environments like autonomous systems or financial AI. Exact extract: "Adversarial testing is a method where simulation of various attack scenarios is applied to analyze the model's behavior, helping to fortify AI against potential threats." (Reference: Cyber Security for AI by SISA Study Guide, Section on AI Model Security Testing, Page 140-143).
Answer: E
NEW QUESTION # 45
In the context of a supply chain attack involving machine learning, which of the following is a critical component that attackers may target?
- A. The marketing materials associated with the AI product
- B. The user interface of the AI application
- C. The physical hardware running the AI system
- D. The underlying ML model and its training data.
Answer: D
Explanation:
Supply chain attacks in ML exploit vulnerabilities in the ecosystem, with the core ML model and training data being prime targets due to their foundational role in system behavior. Attackers might inject backdoors into pretrained models via compromised libraries (e.g., PyTorch or TensorFlow packages) or poison datasets during sourcing, leading to manipulated outputs or data exfiltration. This is more critical than targeting UI or hardware, as model/data compromises persist across deployments, enabling stealthy, long-term exploits like trojan attacks. Mitigation includes verifying model provenance, using secure repositories, and conducting integrity checks with hashing or digital signatures. In SISA guidelines, emphasis is on end-to-end supply chain auditing to prevent such intrusions, which could result in biased decisions or security breaches in applications like recommendation systems. Protecting these components ensures model reliability and data confidentiality, integral to AI security posture. Exact extract: "In supply chain attacks on machine learning, attackers critically target the underlying ML model and its training data to introduce persistent vulnerabilities." (Reference: Cyber Security for AI by SISA Study Guide, Section on Supply Chain Risks in AI, Page 145-148).
NEW QUESTION # 46
In utilizing Giskard for vulnerability detection, what is a primary benefit of integrating this open-source tool into the security function?
- A. Enabling real-time detection of vulnerabilities with actionable insights.
- B. Reducing the need for manual vulnerability assessment entirely
- C. Automatically patching vulnerabilities without additional configuration
- D. Limiting its use to only high-priority vulnerabilities.
Answer: A
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 # 47
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