Free CSPAI Exam braindumps download (CSPAI exam dumps Free)

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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QUESTION: 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.

Answer(s): B

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QUESTION: 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

Answer(s): D

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Free CSPAI Exam Braindumps
Certified Security Professional in Artificial Intelligence Questions 6–10 (page: 3)

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Free CSPAI Exam Braindumps Certified Security Professional in Artificial Intelligence Questions 6–10 (page: 3)

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Free CSPAI Exam Braindumps
Certified Security Professional in Artificial Intelligence Questions 6–10 (page: 3)