Decreasing the number of tokens in the prompt reduces the cost associated with using an LLM model on Amazon Bedrock, as costs are often based on the number of tokens processed by the model.

Token Reduction Strategy:

By decreasing the number of tokens (words or characters) in each prompt, the company reduces the computational load and, therefore, the cost associated with invoking the model.

Since the model is performing well with few-shot prompting, reducing token usage without sacrificing performance can lower monthly costs.

Why Option B is Correct:

Cost Efficiency: Directly reduces the number of tokens processed, lowering costs without requiring additional adjustments.

Maintaining Performance: If the model is already performing well, a reduction in tokens should not significantly impact its performance.

Why Other Options are Incorrect:

A . Fine-tuning: Can be costly and time-consuming and is not needed if the current model is already performing well.

C . Increase the number of tokens: Would increase costs, not lower them.

D . Use Provisioned Throughput: Is unrelated to token costs and applies more to read/write capacity in databases.

In generative AI models, such as those built on Amazon Bedrock, inference costs are driven by the number of tokens processed. A token can be as short as one character or as long as one word, and the more tokens consumed during the inference process, the higher the cost.

Option A (Correct): 'Number of tokens consumed': This is the correct answer because the inference cost is directly related to the number of tokens processed by the model.

Option B: 'Temperature value' is incorrect as it affects the randomness of the model's output but not the cost directly.

Option C: 'Amount of data used to train the LLM' is incorrect because training data size affects training costs, not inference costs.

Option D: 'Total training time' is incorrect because it relates to the cost of training the model, not the cost of inference.

AWS AI Practitioner Reference:

Understanding Inference Costs on AWS: AWS documentation highlights that inference costs for generative models are largely based on the number of tokens processed.

To comply with the company's security policy, which restricts each team to access data for only their own customers, creating an Amazon Bedrock custom service role for each team is the correct solution.

Custom Service Role Per Team:

A custom service role for each team ensures that the access control is granular, allowing only specific teams to access their own customer data in Amazon S3.

This setup aligns with the principle of least privilege, ensuring teams can only interact with data they are authorized to access.

Why Option A is Correct:

Access Control: Allows precise access permissions for each team's data.

Security Compliance: Directly meets the company's security policy requirements by ensuring data segregation.

Why Other Options are Incorrect:

B . Custom service role with customer name specification: This approach is impractical as it relies on manual input, which is prone to errors and does not inherently enforce data access controls.

C . Redacting personal data and updating S3 bucket policy: Redaction does not solve the requirement for team-specific access, and updating bucket policies is less granular than creating roles.

D . One Bedrock role with full S3 access and IAM roles for teams: This setup does not meet the least privilege principle, as having a single role with full access is contrary to the company's security policy.

Thus, A is the correct answer to meet the company's security requirements.

Amazon Bedrock provides large language models (LLMs) that are optimized for natural language understanding and text summarization tasks, making it the best choice for creating concise summaries of user reviews. Time-series forecasting, classification, and image analysis (Rekognition) are not suitable for summarizing textual data. Reference: AWS Bedrock Documentation.