CSPAI Certified Security Professional in Artificial Intelligence Exam Free Dumps

dumps2025-09-16T10:19:44+08:00

dumps SISA Certified Security Professional in Artificial Intelligence CSPAI exam, CSPAI exam dumps

CSPAI Certified Security Professional in Artificial Intelligence exam is hot nowadays. Learning CSPAI exam information is a required step. There are 50 questions in real CSPAI exam, and you have 1 hour to answer all the questions. The passing score is 56%. We provide the latest Certified Security Professional in Artificial Intelligence CSPAI exam dumps, which can ensure you pass the test easily. Free Certified Security Professional in Artificial Intelligence CSPAI exam dumps are available below.

Page 1 of 2

1. 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.

2. When dealing with the risk of data leakage in LLMs, which of the following actions is most effective in mitigating this issue?

Applying rigorous access controls and anonymization techniques to training data.

Using larger datasets to overshadow sensitive information.

Allowing unrestricted access to training data.

Relying solely on model obfuscation techniques

3. When deploying LLMs in production, what is a common strategy for parameter-efficient fine-tuning?

Using external reinforcement learning to adjust the model's parameters dynamically.

Freezing the majority of model parameters and only updating a small subset relevant to the task

Training the model from scratch on the target task to achieve optimal performance.

Implementing multiple independent models for each specific task instead of fine tuning a single model

4. 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.

5. 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.

6. 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

7. 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.

8. 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.

9. 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.

10. 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

Page 2 of 2

11. What is a primary step in the risk assessment model for GenAI data privacy?

Ignoring data sources to speed up assessment.

Conducting data flow mapping to identify privacy risks.

Limiting assessment to model outputs only.

Relying on vendor assurances without verification.

12. Which of the following is a characteristic of domain-specific Generative AI models?

They are designed to run exclusively on quantum computers

They are tailored and fine-tuned for specific fields or industries

They are only used for computer vision tasks

They are trained on broad datasets covering multiple domains

13. 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.

14. 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.

15. 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,

CSPAI Certified Security Professional in Artificial Intelligence Exam Free Dumps

CSPAI Certified Security Professional in Artificial Intelligence Exam Free Dumps

Share this post