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A marketing agency is using Amazon Bedrock to generate brand-tagline ideas. The outputs feel repetitive and lack originality. What adjustment should the data-science team make?
Real Exam
Community
RRitesh
0
A
Lower the temperature to 0.2 for more precise outputs
B
Increase the temperature to around 0.9 to encourage creative variation
C
Reduce the top-p to 0.3 to increase determinism
D
Add stop sequences to control token length
Explanation:
Explanation
When using Amazon Bedrock for text generation, the temperature parameter controls the randomness and creativity of the outputs:
- Lower temperature (e.g., 0.2): Produces more deterministic, focused, and repetitive outputs
- Higher temperature (e.g., 0.9): Increases randomness and encourages more creative, diverse variations
Since the marketing agency is experiencing repetitive outputs lacking originality, they need to increase the temperature to introduce more variation and creativity into the generated brand-tagline ideas.
Other options explained:
- Option A (Lower temperature): Would make outputs even more repetitive and deterministic
- Option C (Reduce top-p): Top-p (nucleus sampling) controls vocabulary diversity, but reducing it would limit word choices rather than increase creativity
- Option D (Stop sequences): Controls output length but doesn't address the creativity issue
The correct approach is Option B - increasing the temperature to around 0.9 to encourage more creative and varied outputs.
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