[UPDATED 2026] Free SISA CSPAI Exam Questions Self-Assess Preparation [Q12-Q29]

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[UPDATED 2026] Free SISA CSPAI Exam Questions Self-Assess Preparation

CSPAI Free Sample Questions to Practice One Year Update

QUESTION 12
How do ISO 42001 and ISO 27563 integrate for comprehensive AI governance?

By combining AI management with privacy standards to address both operational and data protection needs.

By replacing each other in different organizational contexts.

By focusing ISO 42001 on privacy and ISO 27563 on management.

By applying only to public sector AI systems.

QUESTION 13
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.

QUESTION 14
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 15
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

QUESTION 16
When integrating LLMs using a Prompting Technique, what is a significant challenge in achieving consistent performance across diverse applications?

Handling the security concerns that arise from dynamically generated prompts

Overcoming the lack of transparency in understanding how the LLM interprets varying prompt structures.

The need for optimizing prompt templates to ensure generalization across different contexts.

Reducing latency in generating responses to meet real-time application requirements.

QUESTION 17
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.

QUESTION 18
In utilizing Giskard for vulnerability detection, what is a primary benefit of integrating this open-source tool into the security function?

Automatically patching vulnerabilities without additional configuration

Reducing the need for manual vulnerability assessment entirely

Enabling real-time detection of vulnerabilities with actionable insights.

Limiting its use to only high-priority vulnerabilities.

QUESTION 19
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 20
In the context of LLM plugin compromise, as demonstrated by the ChatGPT Plugin Privacy Leak case study, what is a key practice to secure API access and prevent unauthorized information leaks?

Restricting API access to a predefined list of IP addresses

Increasing the frequency of API endpoint updates.

Implementing stringent authentication and authorization mechanisms, along with regular security audits

Allowing open API access to facilitate ease of integration

QUESTION 21
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

QUESTION 22
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

QUESTION 23
In ISO 42001, what is required for AI risk treatment?

Identifying, analyzing, and evaluating AI-specific risks with treatment plans.

Ignoring risks below a certain threshold.

Delegating all risk management to external auditors.

Focusing only on post-deployment risks.

QUESTION 24
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.

QUESTION 25
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.

QUESTION 26
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 27
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

QUESTION 28
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 29
For effective AI risk management, which measure is crucial when dealing with penetration testing and supply chain security?

Perform occasional penetration testing and only address vulnerabilities in the internal network.

Prioritize external audits over internal penetration testing to assess supply chain security.

Implement penetration testing only for high-risk components and ignore less critical ones

Conduct comprehensive penetration testing and continuously evaluate both internal systems and third- party components in the supply chain.

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[UPDATED 2026] Free SISA CSPAI Exam Questions Self-Assess Preparation [Q12-Q29]

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