Apr-2026 Free SISA CSPAI Exam Question Practice Exams [Q17-Q31]

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Apr-2026 Free SISA CSPAI Exam Question Practice Exams

Ace CSPAI Certification with 52 Actual Questions

SISA CSPAI Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 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.
Topic 4	Securing AI Models and Data: This section of the exam measures skills of the Cybersecurity Risk Manager and focuses on the protection of AI models and the data they consume or generate. Topics include adversarial attacks, data poisoning, model theft, and encryption techniques that help secure the AI lifecycle.

QUESTION 17
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.

QUESTION 18
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.

QUESTION 19
How does ISO 27563 support privacy in AI systems?

By providing guidelines for privacy-enhancing technologies in AI.

By mandating the use of specific encryption algorithms.

By limiting AI to non-personal data only.

By focusing on performance metrics over privacy.

QUESTION 20
Which framework is commonly used to assess risks in Generative AI systems according to NIST?

The AI Risk Management Framework (AI RMF) for evaluating trustworthiness.

A general IT risk assessment without AI-specific considerations.

Focusing solely on financial risks associated with AI deployment.

Using outdated models from traditional software risk assessment.

QUESTION 21
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.

QUESTION 22
Which of the following is a primary goal of enforcing Responsible AI standards and regulations in the development and deployment of LLMs?

Focusing solely on improving the speed and scalability of AI systems

Maximizing model performance while minimizing computational costs.

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

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

QUESTION 23
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?

Leveraging a diverse set of data sources to enrich the response with varied perspectives

Integrating advanced search algorithms to ensure the retrieval of highly relevant documents for context.

Utilizing feedback mechanisms to continuously improve the relevance of responses based on user interactions.

Retrieving relevant information from the vector database before generating a response

QUESTION 24
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.

QUESTION 25
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.

QUESTION 26
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.

QUESTION 27
In transformer models, how does the attention mechanism improve model performance compared to RNNs?

By enabling the model to attend to both nearby and distant words simultaneously, improving its understanding of long-term dependencies

By processing each input independently, ensuring the model captures all aspects of the sequence equally.

By enhancing the model’s ability to process data in parallel, ensuring faster training without compromising context.

By dynamically assigning importance to every word in the sequence, enabling the model to focus on relevant parts of the input.

QUESTION 28
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.

QUESTION 29
What is a potential risk of LLM plugin compromise?

Better integration with third-party tools

Improved model accuracy

Unauthorized access to sensitive information through compromised plugins

Reduced model training time

QUESTION 30
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.

QUESTION 31
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.

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SISA CSPAI Exam Syllabus Topics:

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