CSPAI Dumps Help You Study Objectives

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1. 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.

2. 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

3. 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.

4. 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.

5. 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

6. 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.

7. 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,

8. 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.

9. 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.

10. 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.

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11. 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.

12. 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

13. 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.

14. 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.

15. 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

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