100% Pass Top-selling CSPAI Exams – New 2026 SISA Pratice Exam [Q20-Q41]

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100% Pass Top-selling CSPAI Exams – New 2026 SISA Pratice Exam

Cyber Security for AI Dumps CSPAI Exam for Full Questions – Exam Study Guide

Q20. How can Generative AI be utilized to enhance threat detection in cybersecurity operations?

By generating random data to overload security systems.

By creating synthetic attack scenarios for training detection models.

By automating the deletion of security logs to reduce storage costs.

By replacing all human analysts with AI-generated reports.

Q21. A company’s chatbot, Tay, was poisoned by malicious interactions. What is the primary lesson learned from this case study?

Continuous live training is essential for enhancing chatbot performance.

Encrypting user data can prevent such attacks

Open interaction with users without safeguards can lead to model poisoning and generation of inappropriate content.

Chatbots should have limited conversational abilities to prevent poisoning.

Q22. In line with the US Executive Order on AI, a company’s AI application has encountered a security vulnerability. What should be prioritized to align with the order’s expectations?

Implementing a rapid response to address and remediate the vulnerability, followed by a review of security practices.

Immediate public disclosure of the vulnerability.

Halting all AI projects until a full investigation is complete.

Ignoring the vulnerability if it does not affect core functionalities.

Q23. Which of the following describes the scenario where an LLM is embedded ‘As-is’ into an application frame?

Integrating the LLM into the application without modifications, using its out-of-the-box capabilities directly within the application.

Replacing the LLM with a more specialized model tailored to the application’s needs.

Customizing the LLM to fit specific application requirements and workflows before integration.

Using the LLM solely for backend data processing, while the application handles all user interactions.

Q24. Which of the following is a primary goal of enforcing Responsible AI standards and regulations in the development and deployment of LLMs?

Maximizing model performance while minimizing computational costs.

Developing AI systems with the highest accuracy regardless of data privacy concerns

Focusing solely on improving the speed and scalability of AI systems

Ensuring that AI systems operate safely, ethically, and without causing harm.

Q25. Which of the following is a potential use case of Generative AI specifically tailored for CXOs (Chief Experience Officers)?

Developing autonomous vehicles for urban mobility solutions.

Automating financial transactions in blockchain networks.

Conducting genetic sequencing for personalized medicine

Enhancing customer support through AI-powered chatbots that provide 24/7 assistance.

Q26. What is a key concept behind developing a Generative AI (GenAI) Language Model (LLM)?

Operating only in supervised environments

Human intervention for every decision

Data-driven learning with large-scale datasets

Rule-based programming

Q27. A company developing AI-driven medical diagnostic tools is expanding into the European market. To ensure compliance with local regulations, what should be the company’s primary focus in adhering to the EU AI Act?

Implementing measures to prevent any harmful outcomes and ensure AI system safety

Focusing on integrating ethical guidelines to ensure AI decisions are fair and unbiased.

Prioritizing transparency and accountability in AI systems to avoid high-risk categorization

Ensuring the AI system meets stringent privacy standards to protect sensitive data

Q28. How does AI enhance customer experience in retail environments?

By integrating personalized interactions with AI-driven analytics for a more customized shopping experience.

By optimizing customer service through automated systems and tailored recommendations.

By ensuring every customer receives the same generic response from automated systems.

By automating repetitive tasks and providing consistent data driven insights to improve customer service.

Q29. In a machine translation system where context from both early and later words in a sentence is crucial, a team is considering moving from RNN-based models to Transformer models. How does the self-attention mechanism in Transformer architecture support this task?

By processing words in strict sequential order, which is essential for capturing meaning

By considering all words in a sentence equally and simultaneously, allowing the model to establish long-range dependencies.

By assigning a constant weight to each word, ensuring uniform translation output

By focusing only on the most recent word in the sentence to speed up translation

Q30. In a time-series prediction task, how does an RNN effectively model sequential data?

By focusing on the overall sequence structure rather than individual time steps for a more holistic approach.

By processing each time step independently, optimizing the model’s performance over time.

By storing only the most recent time step, ensuring efficient memory usage for real-time predictions

By using hidden states to retain context from prior time steps, allowing it to capture dependencies across the sequence.

Q31. What is a common use of an LLM as a Secondary Chatbot?

To serve as a fallback or supplementary AI assistant for more complex queries

To replace the primary AI system

To handle tasks unrelated to the main application

To only manage user credentials

Q32. In assessing GenAI supply chain risks, what is a critical consideration?

Evaluating third-party components for embedded vulnerabilities.

Ignoring open-source dependencies to reduce complexity.

Focusing only on internal development risks.

Assuming all vendors comply with standards automatically.

Q33. An organization is evaluating the risks associated with publishing poisoned datasets. What could be a significant consequence of using such datasets in training?

Increased model efficiency in processing and generation tasks.

Enhanced model adaptability to diverse data types.

Compromised model integrity and reliability leading to inaccurate or biased outputs

Improved model performance due to higher data volume.

Q34. In the Retrieval-Augmented Generation (RAG) framework, which of the following is the most critical factor for improving factual consistency in generated outputs?

Fine-tuning the generative model with synthetic datasets generated from the retrieved documents

Utilising an ensemble of multiple LLMs to cross-check the generated outputs.

Implementing a redundancy check by comparing the outputs from different retrieval modules.

Tuning the retrieval model to prioritize documents with the highest semantic similarity

Q35. 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?

It ensures that the model treats all words as equally important, regardless of their position in the sequence.

It simplifies the model’s computations by merging all words into a single representation, regardless of their order

It speeds up processing by reducing the number of tokens the model needs to handle.

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.

Q36. In the context of a supply chain attack involving machine learning, which of the following is a critical component that attackers may target?

The user interface of the AI application

The physical hardware running the AI system

The marketing materials associated with the AI product

The underlying ML model and its training data.

Q37. How does the STRIDE model adapt to assessing threats in GenAI?

By applying Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, and Elevation of Privilege to AI components.

By focusing only on hardware threats in AI systems.

By excluding AI-specific threats like model inversion.

By using it unchanged from traditional software.

Q38. What does the OCTAVE model emphasize in GenAI risk assessment?

Operational Critical Threat, Asset, and Vulnerability Evaluation focused on organizational risks.

Solely technical vulnerabilities in AI models.

Short-term tactical responses over strategic planning.

Exclusion of stakeholder input in assessments.

Q39. 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?

Retraining the model with more comprehensive and accurate datasets.

Reducing the number of attention layers to speed up generation

Increasing the model’s output length to enhance response complexity.

Encouraging randomness in responses to explore more diverse outputs.

SISA CSPAI Exam Syllabus Topics:

Topic	Details
Topic 1	Evolution of Gen AI and Its Impact: This section of the exam measures skills of the AI Security Analyst and covers how generative AI has evolved over time and the implications of this evolution for cybersecurity. It focuses on understanding the broader impact of Gen AI technologies on security operations, threat landscapes, and risk management strategies.
Topic 2	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 3	Models for Assessing Gen AI Risk: This section of the exam measures skills of the Cybersecurity Risk Manager and deals with frameworks and models used to evaluate risks associated with deploying generative AI. It includes methods for identifying, quantifying, and mitigating risks from both technical and governance perspectives.

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SISA CSPAI Exam Syllabus Topics:

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