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SISA Reliable CSPAI Test Cram: Certified Security Professional in Artificial Intelligence - ValidDumps High Pass Rate

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SISA CSPAI Exam Syllabus Topics:

Topic
Details

Topic 1

• 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.

Topic 2

• 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 3

• 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.

 

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SISA Certified Security Professional in Artificial Intelligence Sample Questions (Q17-Q22):

NEW QUESTION # 17
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 # 18
What aspect of privacy does ISO 27563 emphasize in AI data processing?

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

Answer: C

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 # 19
Which of the following is a characteristic of domain-specific Generative AI models?

• A. They are designed to run exclusively on quantum computers
• B. They are tailored and fine-tuned for specific fields or industries
• C. They are only used for computer vision tasks
• D. They are trained on broad datasets covering multiple domains

Answer: B

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 # 20
Fine-tuning an LLM on a single task involves adjusting model parameters to specialize in a particular domain.
What is the primary challenge associated with fine tuning for a single task compared to multi task fine tuning?

• A. Single-task fine-tuning requires significantly more data to achieve comparable performance to multi- task fine tuning.
• B. Single-task fine-tuning is less effective in generalizing to new, unseen tasks compared to multi-task fine- tuning.
• C. Single-task fine-tuning introduces more complexity in managing different versions of the model compared to multi-task fine-tuning.
• D. Single-task fine-tuning tends to degrade the model's performance on the original tasks it was trained on.

Answer: B

Explanation:
Single-task fine-tuning specializes the LLM but risks overfitting, limiting generalization to novel tasks unlike multi-task approaches that promote transfer learning across domains. This challenge requires careful regularization in SDLC to balance specificity and versatility, often needing more resources for version management. Exact extract: "Single-task fine-tuning is less effective in generalizing to new tasks compared to multi-task fine-tuning." (Reference: Cyber Security for AI by SISA Study Guide, Section on Fine-Tuning Challenges, Page 115-118).

 

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. Integrating multiple separate Domain-Specific GenAI models for various financial functions without using a foundational model for consistency
• C. Building a new, from scratch Domain-Specific GenAI model for financial tasks without leveraging preexisting models.
• D. Using a general Large Language Model (LLM) without adaptation, relying solely on its broad capabilities to handle financial tasks.

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
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