SISA CSPAI Questions Answers
Certified Security Professional in Artificial Intelligence- 50 Questions & Answers
- Update Date : October 19, 2026
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SISA CSPAI Sample Questions
Question # 1What is a common use of an LLM as a Secondary Chatbot?
A. To serve as a fallback or supplementary AI assistant for more complex queries
B. To replace the primary AI system
C. To handle tasks unrelated to the main application
D. To only manage user credentials
Question # 2
In what way can GenAI assist in phishing detection and prevention?
A. By sending automated phishing emails to test employee awareness.
B. By generating realistic phishing simulations and analyzing user responses.
C. By blocking all incoming emails to prevent any potential threats.
D. By relying solely on signature-based detection methods.
Question # 3
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. Reducing the number of attention layers to speed up generation
C. Increasing the model's output length to enhance response complexity.
D. Encouraging randomness in responses to explore more diverse outputs.
Question # 4
How does ISO 27563 support privacy in AI systems?
A. By providing guidelines for privacy-enhancing technologies in AI.
B. By mandating the use of specific encryption algorithms.
C. By limiting AI to non-personal data only.
D. By focusing on performance metrics over privacy.
Question # 5
How does GenAI contribute to incident response in cybersecurity?
A. By delaying responses to gather more data for analysis.
B. By automating playbook generation and response orchestration.
C. By manually reviewing each incident without AI assistance.
D. By focusing only on post-incident reporting.
Question # 6
A company's chatbot, Tay, was poisoned by malicious interactions. What is the primary lesson learned from this case study?
A. Continuous live training is essential for enhancing chatbot performance.
B. Encrypting user data can prevent such attacks
C. Open interaction with users without safeguards can lead to model poisoning and generation of
inappropriate content.
D. Chatbots should have limited conversational abilities to prevent poisoning.
Question # 7
How does the STRIDE model adapt to assessing threats in GenAI?
A. By applying Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, and
Elevation of Privilege to AI components.
B. By focusing only on hardware threats in AI systems.
C. By excluding AI-specific threats like model inversion.
D. By using it unchanged from traditional software.
Question # 8
How do ISO 42001 and ISO 27563 integrate for comprehensive AI governance?
A. By combining AI management with privacy standards to address both operational and data
protection needs.
B. By replacing each other in different organizational contexts.
C. By focusing ISO 42001 on privacy and ISO 27563 on management.
D. By applying only to public sector AI systems.
Question # 9
How does the multi-head self-attention mechanism improve the model's ability to learn complex relationships in data?
A. By forcing the model to focus on a single aspect of the input at a time.
B. By ensuring that the attention mechanism looks only at local context within the input
C. By simplifying the network by removing redundancy in attention layers.
D. By allowing the model to focus on different parts of the input through multiple attention heads
Question # 10
What is the main objective of ISO 42001 in AI management systems?
A. To establish requirements for an AI management system within organizations.
B. To focus solely on technical specifications for AI algorithms.
C. To regulate hardware used in AI deployments.
D. To provide guidelines only for small-scale AI projects.
Question # 11
When integrating LLMs using a Prompting Technique, what is a significant challenge in achieving consistent performance across diverse applications?
A. Handling the security concerns that arise from dynamically generated prompts
B. Overcoming the lack of transparency in understanding how the LLM interprets varying prompt
structures
C. The need for optimizing prompt templates to ensure generalization across different contexts.
D. Reducing latency in generating responses to meet real-time application requirements.
Question # 12
In a Transformer model processing a sequence of text for a translation task, how does incorporating positional encoding impact the model's ability to generate accurate translations?
A. It ensures that the model treats all words as equally important, regardless of their position in the
sequence.
B. It simplifies the model's computations by merging all words into a single representation,
regardless of their order
C. It speeds up processing by reducing the number of tokens the model needs to handle.
D. It helps the model distinguish the order of words in the sentence, leading to more accurate
translation by maintaining the context of each word's position.
Question # 13
How does machine learning improve the accuracy of predictive models in finance?
A. By using historical data patterns to make predictions without updates
B. By relying exclusively on manual adjustments and human input for predictions.
C. By continuously learning from new data patterns to refine predictions
D. By avoiding any use of past data and focusing solely on current trends
Question # 14
How does AI enhance customer experience in retail environments?
A. By integrating personalized interactions with AI-driven analytics for a more customized shopping
experience.
B. By optimizing customer service through automated systems and tailored recommendations.
C. By ensuring every customer receives the same generic response from automated systems.
D. By automating repetitive tasks and providing consistent data driven insights to improve customer
service.
Question # 15
Which of the following describes the scenario where an LLM is embedded 'As-is' into an application frame?
A. Integrating the LLM into the application without modifications, using its out-of-the-box
capabilities directly within the application.
B. Replacing the LLM with a more specialized model tailored to the application's needs.
C. Customizing the LLM to fit specific application requirements and workflows before integration.
D. Using the LLM solely for backend data processing, while the application handles all user
interactions.