CCAO-F Using the Claude API Practice Question
A developer needs to configure a Claude 3 model to generate a wide variety of creative and unpredictable marketing slogans for a new product line. Which TWO parameter adjustments would best support this goal?
⚠ Common exam trap
Candidates often lower the temperature to reduce errors, forgetting that creative tasks require maximized sampling parameters like temperature and top_p set to 1.0.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Set temperature to 1.0
To increase creativity and variety in model outputs, developers must adjust the sampling parameters. Increasing temperature makes the probability distribution of the next token flatter, allowing for less likely choices. Similarly, a high top_p value ensures a larger pool of potential tokens is considered. These settings are ideal for creative tasks where consistency and predictability are less important than novelty.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Set temperature to 1.0
Why this is correct
Higher temperature values increase the randomness of the output by allowing the model to choose tokens that are not the most statistically likely. For creative tasks like slogan generation, a temperature near 1.0 is effective. This helps the model avoid repetitive or generic phrasing and explore more unique linguistic combinations.
- ✗
Set temperature to 0.0
Why it's wrong here
A temperature of 0.0 makes the model deterministic, meaning it will consistently choose the most likely next token. This is excellent for factual extraction or coding but detrimental to creative variety. Using this setting for marketing slogans would likely result in the same few boring suggestions every time the API is called.
- ✓
Set top_p to 1.0
Why this is correct
Setting top_p to 1.0, also known as nucleus sampling, ensures that the model considers the entire cumulative probability distribution of tokens. This maximizes the diversity of the output. When combined with a high temperature, it allows the model to draw from a very broad vocabulary, which is essential for creative brainstorming sessions.
- ✗
Set top_p to 0.1
Why it's wrong here
Low top_p values restrict the model to only the most probable tokens, effectively cutting off the 'long tail' of creative possibilities. This setting is used to increase the accuracy and focus of the model. For a task requiring high variety, this would be counterproductive as it would force the model to be extremely conservative.
- ✗
Set max_tokens to a very low value
Why it's wrong here
The max_tokens parameter controls the length of the response, not its creativity or variety. Setting this too low would likely result in slogans being cut off mid-sentence. While it limits the volume of output, it does nothing to influence the statistical sampling process that determines how creative or unpredictable the generated text actually is.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Anthropic exam blueprint
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