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SISA Certified Security Professional in Artificial Intelligence Sample Questions (Q19-Q24):
NEW QUESTION # 19
In utilizing Giskard for vulnerability detection, what is a primary benefit of integrating this open-source tool into the security function?
- A. Limiting its use to only high-priority vulnerabilities.
- B. Automatically patching vulnerabilities without additional configuration
- C. Reducing the need for manual vulnerability assessment entirely
- D. Enabling real-time detection of vulnerabilities with actionable insights.
Answer: D
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 # 20
When deploying LLMs in production, what is a common strategy for parameter-efficient fine-tuning?
- A. Using external reinforcement learning to adjust the model's parameters dynamically.
- B. Freezing the majority of model parameters and only updating a small subset relevant to the task
- C. Training the model from scratch on the target task to achieve optimal performance.
- D. Implementing multiple independent models for each specific task instead of fine tuning a single model
Answer: B
Explanation:
Parameter-efficient fine-tuning (PEFT) strategies, like LoRA or adapters, freeze most pretrained parameters and train only lightweight modules, reducing computational costs while adapting to new tasks. This preserves general knowledge, prevents catastrophic forgetting, and enables quick deployments in resource-constrained settings. For LLMs, it's crucial for efficiency in production, allowing specialization without retraining billions of parameters. Security-wise, it minimizes exposure to new data risks. Exact extract: "A common strategy is freezing the majority of model parameters and updating only a small task-relevant subset, ensuring efficiency in fine-tuning for production deployment." (Reference: Cyber Security for AI by SISA Study Guide, Section on Efficient Fine-Tuning in SDLC, Page 90-92).
NEW QUESTION # 21
In a financial technology company aiming to implement a specialized AI solution, which approach would most effectively leverage existing AI models to address specific industry needs while maintaining efficiency and accuracy?
- A. Adopting a Foundation Model as the base and fine-tuning it with domain-specific financial data to enhance its capabilities for forecasting and risk assessment.
- B. Building a new, from scratch Domain-Specific GenAI model for financial tasks without leveraging preexisting models.
- C. Using a general Large Language Model (LLM) without adaptation, relying solely on its broad capabilities to handle financial tasks.
- D. Integrating multiple separate Domain-Specific GenAI models for various financial functions without using a foundational model for consistency
Answer: A
Explanation:
Leveraging foundation models like GPT or BERT for fintech involves fine-tuning with sector-specific data, such as transaction logs or market trends, to tailor for tasks like risk prediction, ensuring high accuracy without the overhead of scratch-building. This approach maintains efficiency by reusing pretrained weights, reducing training time and resources in SDLC, while domain adaptation mitigates generalization issues. It outperforms unadapted general models or fragmented specifics by providing cohesive, scalable solutions.
Security is enhanced through controlled fine-tuning datasets. Exact extract: "Adopting a Foundation Model and fine-tuning with domain-specific data is most effective for leveraging existing models in fintech, balancing efficiency and accuracy." (Reference: Cyber Security for AI by SISA Study Guide, Section on Model Adaptation in SDLC, Page 105-108).
NEW QUESTION # 22
Which of the following is a primary goal of enforcing Responsible AI standards and regulations in the development and deployment of LLMs?
- A. Maximizing model performance while minimizing computational costs.
- B. Ensuring that AI systems operate safely, ethically, and without causing harm.
- C. Developing AI systems with the highest accuracy regardless of data privacy concerns
- D. Focusing solely on improving the speed and scalability of AI systems
Answer: B
Explanation:
Responsible AI standards, including ISO 42001 for AI management systems, aim to promote ethical development, ensuring safety, fairness, and harm prevention in LLM deployments. This encompasses bias mitigation, transparency, and accountability, aligning with societal values. Regulations like the EU AI Act reinforce this by categorizing risks and mandating safeguards. The goal transcends performance to foster trust and sustainability, addressing issues like discrimination or misuse. Exact extract: "The primary goal is to ensure AI systems operate safely, ethically, and without causing harm, as outlined in standards like ISO
42001." (Reference: Cyber Security for AI by SISA Study Guide, Section on Responsible AI and ISO Standards, Page 150-153).
NEW QUESTION # 23
What role does GenAI play in automating vulnerability scanning and remediation processes?
- A. By generating code patches and suggesting fixes based on vulnerability descriptions.
- B. By ignoring low-priority vulnerabilities to focus on high-impact ones.
- C. By compiling lists of vulnerabilities without any analysis.
- D. By increasing the frequency of manual scans to ensure thoroughness.
Answer: A
Explanation:
GenAI automates vulnerability management by analyzing scan results and generating tailored code patches or remediation strategies, accelerating the fix process and reducing human error. Using natural language processing, it interprets vulnerability reports, cross-references with known exploits, and proposes secure code alternatives, integrating seamlessly into DevSecOps pipelines. This proactive approach minimizes exposure windows and enhances system resilience against exploits. For instance, in cloud environments, GenAI can simulate patch impacts before application. This contributes to a stronger security posture by enabling rapid, accurate responses to threats. Exact extract: "GenAI automates vulnerability scanning and remediation by generating code patches and fixes, improving efficiency and security posture." (Reference: Cyber Security for AI by SISA Study Guide, Section on Automation in Vulnerability Management, Page 205-208).
NEW QUESTION # 24
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