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The context window determines how much information can fit into a single prompt when using a large language model (LLM) like those on Amazon Bedrock.

Context Window:

The context window is the maximum amount of text (measured in tokens) that a language model can process in a single pass.

For LLM applications, the size of the context window limits how much input data, such as text for sentiment analysis, can be fed into the model at once.

Why Option B is Correct:

Determines Prompt Size:The context window size directly informs how much information (e.g., words or sentences) can fit in one prompt.

Model Capacity:The larger the context window, the more information the model can consider for generating outputs.

Why Other Options are Incorrect:

A. Temperature:Controls randomness in model outputs but does not affect the prompt size.

C. Batch size:Refers to the number of training samples processed in one iteration, not the amount of information in a prompt.

D. Model size:Refers to the number of parameters in the model, not the input size for a single prompt.

Generative Pre-trained Transformers (GPT) are suitable for building an AI-based application that can generate SQL queries from natural language input provided by employees.

GPT for Natural Language Processing:

GPT models are designed for understanding and generating human-like text based on natural language input.

They can be fine-tuned to interpret specific tasks, such as converting natural language queries into SQL queries.

Why Option A is Correct:

Natural Language Understanding:GPT is highly effective for tasks that require understanding of human language and generating structured outputs like SQL.

User-Friendly:Requires minimal technology experience from employees, as they provide simple text input.

Why Other Options are Incorrect:

B. Residual neural network:Typically used in computer vision tasks, not for natural language-to-SQL conversion.

C. Support vector machine:Used for classification tasks, not for generating structured queries from text.

D. WaveNet:A deep generative model for audio data, unrelated to text-to-SQL tasks.

Providing labeled data with both a prompt field and a completion field is the correct strategy for fine-tuning a foundation model (FM) on Amazon Bedrock.

Fine-Tuning Strategy:

To fine-tune a model, labeled data that pairs input prompts with the correct outputs (completions) is necessary.

This allows the model to learn the desired behavior or response style based on the provided examples.

Why Option A is Correct:

Proper Training Format:The prompt-completion pairs provide the necessary format for training the model to produce accurate outputs.

Customization:Ensures that the model is fine-tuned to the specific requirements of the company’s data and desired outputs.

Why Other Options are Incorrect:

B. Prepare a .txt file in .csv format:This does not align with the specific need for labeled data with prompts and completions.

C. Purchase Provisioned Throughput:Relates to read/write capacity in databases, not to model fine-tuning.

D. Train on journals and textbooks:Lacks the specific format and labeling required for fine-tuning.

The business objective to evaluate the effect of an LLM chatbot aimed at reducing the actions required by call center employees should beaverage call duration.

Average Call Duration:

This metric measures the time taken to handle a customer call or query. A successful LLM chatbot should reduce the call duration by efficiently providing answers, minimizing the need for human intervention.

By decreasing the average call duration, the company can improve call center efficiency, reduce costs, and enhance the user experience.

Why Option B is Correct:

Direct Impact:The objective aligns directly with the goal of reducing the number of actions call center employees must take.

Operational Efficiency:Reducing call duration is a clear indicator of the chatbot's effectiveness in assisting customers without human help.

Why Other Options are Incorrect:

A. Website engagement rate:Is unrelated to call center operations.

C. Corporate social responsibility:Does not relate to call center efficiency.

D. Regulatory compliance:Is important but does not measure the effectiveness of a chatbot in reducing employee actions.