CSPAI Practice Questions

A.  

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

B.  

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

C.  

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

D.  

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

A.  

Operational Critical Threat, Asset, and Vulnerability Evaluation focused on organizational risks.

B.  

Solely technical vulnerabilities in AI models.

C.  

Short-term tactical responses over strategic planning.

D.  

Exclusion of stakeholder input in assessments.

A.  

By ignoring low-priority vulnerabilities to focus on high-impact ones.

B.  

By generating code patches and suggesting fixes based on vulnerability descriptions.

C.  

By increasing the frequency of manual scans to ensure thoroughness.

D.  

By compiling lists of vulnerabilities without any analysis.

A.  

Increasing data silos to protect information.

B.  

Predicting future threats through pattern recognition in large datasets.

C.  

Limiting analysis to historical data only.

D.  

Reducing the use of analytics tools to save costs.

A.  

Hallucinations can lead to creative outputs, which are beneficial for all applications; hence, no measures are necessary.

B.  

Hallucinations cause models to slow down; optimizing hardware performance is necessary to mitigate this issue.

C.  

Hallucinations can produce inaccurate or misleading information; it should be addressed by incorporating external knowledge bases and retrieval systems.

D.  

Hallucinations are primarily due to overfitting; regularization techniques should be applied during training.

A.  

By enabling the model to attend to both nearby and distant words simultaneously, improving its understanding of long-term dependencies

B.  

By processing each input independently, ensuring the model captures all aspects of the sequence equally.

C.  

By enhancing the model's ability to process data in parallel, ensuring faster training without compromising context.

D.  

By dynamically assigning importance to every word in the sequence, enabling the model to focus on relevant parts of the input.

A.  

Integrating the LLM into the application without modifications, using its out-of-the-box capabilities directly within the application.

B.  

Replacing the LLM with a more specialized model tailored to the application's needs.

C.  

Customizing the LLM to fit specific application requirements and workflows before integration.

D.  

Using the LLM solely for backend data processing, while the application handles all user interactions.

A.  

Retraining the model with more comprehensive and accurate datasets.

B.  

Reducing the number of attention layers to speed up generation

C.  

Increasing the model's output length to enhance response complexity.

D.  

Encouraging randomness in responses to explore more diverse outputs.

A.  

Consent management and data minimization principles.

B.  

Maximizing data collection for better AI performance.

C.  

Storing all data indefinitely for auditing.

D.  

Sharing data freely among AI systems.

A.  

They are designed to run exclusively on quantum computers

B.  

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

C.  

They are only used for computer vision tasks

D.  

They are trained on broad datasets covering multiple domains