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.  

Enabled more dynamic decision-making and adaptability with minimal manual intervention

B.  

Enhanced the precision and relevance of automated outputs with reduced manual tuning.

C.  

Improved scalability and performance in handling diverse and evolving data.

D.  

Increased system complexity and the requirement for specialized knowledge,

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.  

Applying rigorous access controls and anonymization techniques to training data.

B.  

Using larger datasets to overshadow sensitive information.

C.  

Allowing unrestricted access to training data.

D.  

Relying solely on model obfuscation techniques

A.  

By using historical data patterns to make predictions without updates

B.  

By relying exclusively on manual adjustments and human input for predictions.

C.  

By continuously learning from new data patterns to refine predictions

D.  

By avoiding any use of past data and focusing solely on current trends

A.  

Restricting API access to a predefined list of IP addresses

B.  

Increasing the frequency of API endpoint updates.

C.  

Implementing stringent authentication and authorization mechanisms, along with regular security audits

D.  

Allowing open API access to facilitate ease of integration

A.  

Fine-tuning the generative model with synthetic datasets generated from the retrieved documents

B.  

Utilising an ensemble of multiple LLMs to cross-check the generated outputs.

C.  

Implementing a redundancy check by comparing the outputs from different retrieval modules.

D.  

Tuning the retrieval model to prioritize documents with the highest semantic similarity

A.  

The user interface of the AI application

B.  

The physical hardware running the AI system

C.  

The marketing materials associated with the AI product

D.  

The underlying ML model and its training data.

A.  

Implementing measures to prevent any harmful outcomes and ensure AI system safety

B.  

Focusing on integrating ethical guidelines to ensure AI decisions are fair and unbiased.

C.  

Prioritizing transparency and accountability in AI systems to avoid high-risk categorization

D.  

Ensuring the AI system meets stringent privacy standards to protect sensitive data

A.  

By integrating personalized interactions with AI-driven analytics for a more customized shopping experience.

B.  

By optimizing customer service through automated systems and tailored recommendations.

C.  

By ensuring every customer receives the same generic response from automated systems.

D.  

By automating repetitive tasks and providing consistent data driven insights to improve customer service.

A.  

By forcing the model to focus on a single aspect of the input at a time.

B.  

By ensuring that the attention mechanism looks only at local context within the input

C.  

By simplifying the network by removing redundancy in attention layers.

D.  

By allowing the model to focus on different parts of the input through multiple attention heads

A.  

Better integration with third-party tools

B.  

Improved model accuracy

C.  

Unauthorized access to sensitive information through compromised plugins

D.  

Reduced model training time

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.  

By processing words in strict sequential order, which is essential for capturing meaning

B.  

By considering all words in a sentence equally and simultaneously, allowing the model to establish long-range dependencies.

C.  

By assigning a constant weight to each word, ensuring uniform translation output

D.  

By focusing only on the most recent word in the sentence to speed up translation

A.  

By generating random data to overload security systems.

B.  

By creating synthetic attack scenarios for training detection models.

C.  

By automating the deletion of security logs to reduce storage costs.

D.  

By replacing all human analysts with AI-generated reports.