AWS Certified Cloud Practitioner

Explanation

When using language models like those in Amazon Bedrock, the temperature parameter controls the randomness of the output:

• Lower temperature (e.g., 0.2): Makes the model more deterministic and focused on the most probable tokens, which can lead to more repetitive outputs

• Higher temperature (e.g., 0.9): Increases randomness and creativity by allowing the model to consider less probable tokens, resulting in more varied and original outputs

In this scenario, the marketing agency is experiencing repetitive outputs lacking originality, which indicates the model is being too conservative. Increasing the temperature to around 0.9 would encourage more creative variation in the generated brand-tagline ideas.

Why other options are incorrect:

• A (Lower temperature to 0.2): This would make outputs even more repetitive and deterministic

• C (Reduce top-p to 0.3): Top-p (nucleus sampling) controls token selection based on cumulative probability. Lowering it restricts the model to only the most probable tokens, reducing variety

• D (Add stop sequences): Stop sequences control when generation stops, not the creativity or variety of content

Key takeaway: For creative tasks like marketing taglines, higher temperature values (0.7-0.9) typically yield better results by introducing more randomness and originality.