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SISA CSPAI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Models for Assessing Gen AI Risk | 15% | - Risk identification, quantification and mitigation - Risk assessment frameworks for Gen AI |
| Topic 2: Using Gen AI for Improving the Security Posture | 20% | - Gen AI tools in security operations - Strengthening organizational security with Gen AI |
| Topic 3: Securing AI Models and Data | 24% | - Model protection and encryption - Adversarial attacks and data poisoning - AI lifecycle security |
| Topic 4: AIMS and Privacy Standards | 15% | - ISO 27563 privacy and data governance - ISO 42001 AI management systems |
| Topic 5: Evolution of Gen AI and Its Impact | 15% | - Evolution of generative AI - Cybersecurity implications of Gen AI |
| Topic 6: Improving SDLC Efficiency Using Gen AI | 11% | - Secure code generation and vulnerability detection - Gen AI in software development lifecycle |
SISA CSPAI Exam: Candid Answers
The SISA Certified Security Professional in Artificial Intelligence blueprint spans 6 domains — including Improving SDLC Efficiency Using Gen AI (11%), Models for Assessing Gen AI Risk (15%), Using Gen AI for Improving the Security Posture (20%). Work the domains in order of weight; the complete outline above lists every subtopic.
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The SISA Certified Security Professional in Artificial Intelligence is SISA's certification exam for Certified Security Professional in Artificial Intelligence, at the Professional level. It's harder than a driver's license test — but brand-new learning ways make it manageable.
Option 1: Minimum 2 years verified experience in information security or AI/ML role; Option 2: Complete 16-hour official CSPAI workshop; Option 3: Equivalent 16+ hours formal training covering exam topics Eligibility rules change over time, so verify the current requirements on the official page (official CSPAI exam page) before registering.
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SISA Certified Security Professional in Artificial Intelligence Sample Questions:
What is a key concept behind developing a Generative AI (GenAI) Language Model (LLM)?
- A. Data-driven learning with large-scale datasets
- B. Operating only in supervised environments
- C. Rule-based programming
- D. Human intervention for every decision
Correct Answer: A 🗳️
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An AI system is generating confident but incorrect outputs, commonly known as hallucinations. Which strategy would most likely reduce the occurrence of such hallucinations and improve the trustworthiness of the system?
- A. Retraining the model with more comprehensive and accurate datasets.
- B. Encouraging randomness in responses to explore more diverse outputs.
- C. Reducing the number of attention layers to speed up generation
- D. Increasing the model's output length to enhance response complexity.
Correct Answer: A 🗳️
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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. Integrating multiple separate Domain-Specific GenAI models for various financial functions without using a foundational model for consistency
- B. Using a general Large Language Model (LLM) without adaptation, relying solely on its broad capabilities to handle financial tasks.
- C. 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.
- D. Building a new, from scratch Domain-Specific GenAI model for financial tasks without leveraging preexisting models.
Correct Answer: C 🗳️
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How does the multi-head self-attention mechanism improve the model's ability to learn complex relationships in data?
- A. By ensuring that the attention mechanism looks only at local context within the input
- B. By simplifying the network by removing redundancy in attention layers.
- C. By allowing the model to focus on different parts of the input through multiple attention heads
- D. By forcing the model to focus on a single aspect of the input at a time.
Correct Answer: C 🗳️
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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. Retrieving relevant information from the vector database before generating a response
- 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. Integrating advanced search algorithms to ensure the retrieval of highly relevant documents for context.
Correct Answer: A 🗳️
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