SISA CSPAI Dumps in PDF and Online - Page: 1

QUESTION: 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.

Answer(s): C

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QUESTION: 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

Answer(s): A

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QUESTION: 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

Answer(s): B

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QUESTION: 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.

Answer(s): A

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QUESTION: 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.

Answer(s): D

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QUESTION: 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

Answer(s): C

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QUESTION: 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.

Answer(s): A

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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.

Answer(s): B

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SISA Certified Security Professional in Artificial Intelligence CSPAI Dumps in PDF

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