CSPAI Pre-Exam Practice Tests (Updated 52 Questions) [Q30-Q49]

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CSPAI Pre-Exam Practice Tests | (Updated 52 Questions)

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SISA CSPAI Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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.
Topic 3	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 4	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.
Topic 5	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.

Q30. 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?

Single-task fine-tuning introduces more complexity in managing different versions of the model compared to multi-task fine-tuning.

Single-task fine-tuning is less effective in generalizing to new, unseen tasks compared to multi-task fine- tuning.

Single-task fine-tuning requires significantly more data to achieve comparable performance to multi- task fine tuning.

Single-task fine-tuning tends to degrade the model’s performance on the original tasks it was trained on.

Q31. 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

Q32. What is a potential risk associated with hallucinations in LLMs, and how should it be addressed to ensure Responsible AI?

Hallucinations can lead to creative outputs, which are beneficial for all applications; hence, no measures are necessary.

Hallucinations cause models to slow down; optimizing hardware performance is necessary to mitigate this issue.

Hallucinations can produce inaccurate or misleading information; it should be addressed by incorporating external knowledge bases and retrieval systems.

Hallucinations are primarily due to overfitting; regularization techniques should be applied during training.

Q33. 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.

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. How does GenAI contribute to incident response in cybersecurity?

By delaying responses to gather more data for analysis.

By automating playbook generation and response orchestration.

By manually reviewing each incident without AI assistance.

By focusing only on post-incident reporting.

Q36. What role does GenAI play in automating vulnerability scanning and remediation processes?

By ignoring low-priority vulnerabilities to focus on high-impact ones.

By generating code patches and suggesting fixes based on vulnerability descriptions.

By increasing the frequency of manual scans to ensure thoroughness.

By compiling lists of vulnerabilities without any analysis.

Q37. 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.

Q38. What is a key benefit of using GenAI for security analytics?

Increasing data silos to protect information.

Predicting future threats through pattern recognition in large datasets.

Limiting analysis to historical data only.

Reducing the use of analytics tools to save costs.

Q39. Which of the following is a method in which simulation of various attack scenarios are applied to analyze the model’s behavior under those conditions.

input sanitation

Model firewall

Prompt injections

Adversarial testing

Adversarial testing involves systematically simulating attack vectors, such as input perturbations or evasion techniques, to evaluate an AI model’s robustness and identify vulnerabilities before deployment. This proactive method replicates real-world threats, like adversarial examples that fool classifiers or prompt manipulations in LLMs, allowing developers to observe behavioral anomalies, measure resilience, and implement defenses like adversarial training or input validation. Unlike passive methods like input sanitation, which cleans data reactively, adversarial testing is dynamic and comprehensive, covering scenarios from data poisoning to model inversion. In practice, tools like CleverHans or ART libraries facilitate these simulations, providing metrics on attack success rates and model degradation. This is crucial for securing AI models, as it uncovers hidden weaknesses that could lead to exploits, ensuring compliance with security standards. By iterating through attack-defense cycles, it enhances overall data and model integrity, reducing risks in high-stakes environments like autonomous systems or financial AI. Exact extract: “Adversarial testing is a method where simulation of various attack scenarios is applied to analyze the model’s behavior, helping to fortify AI against potential threats.” (Reference: Cyber Security for AI by SISA Study Guide, Section on AI Model Security Testing, Page 140-143).

Q40. How does the multi-head self-attention mechanism improve the model’s ability to learn complex relationships in data?

By forcing the model to focus on a single aspect of the input at a time.

By ensuring that the attention mechanism looks only at local context within the input

By simplifying the network by removing redundancy in attention layers.

By allowing the model to focus on different parts of the input through multiple attention heads

Q41. In a scenario where Open-Source LLMs are being used to create a virtual assistant, what would be the most effective way to ensure the assistant is continuously improving its interactions without constant retraining?

Training a larger proprietary model to replace the open-source LLM

Shifting the assistant to a completely rule-based system to avoid reliance on user feedback.

Implementing reinforcement learning from human feedback (RLHF) to refine responses based on user input.

Reducing the amount of feedback integrated to speed up deployment.

Q42. In what way can GenAI assist in phishing detection and prevention?

By sending automated phishing emails to test employee awareness.

By generating realistic phishing simulations and analyzing user responses.

By blocking all incoming emails to prevent any potential threats.

By relying solely on signature-based detection methods.

Q43. 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

Q44. 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.

Q45. 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

Q46. 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.

Q47. What is the main objective of ISO 42001 in AI management systems?

To establish requirements for an AI management system within organizations.

To focus solely on technical specifications for AI algorithms.

To regulate hardware used in AI deployments.

To provide guidelines only for small-scale AI projects.

Q48. 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.

Q49. During the development of AI technologies, how did the shift from rule-based systems to machine learning models impact the efficiency of automated tasks?

Enabled more dynamic decision-making and adaptability with minimal manual intervention

Enhanced the precision and relevance of automated outputs with reduced manual tuning.

Improved scalability and performance in handling diverse and evolving data.

Increased system complexity and the requirement for specialized knowledge,