CCAO-F Using the Claude API Practice Question
You are building a high-throughput application. Which TWO of the following strategies are best for optimizing your API costs and efficiency?
⚠ Common exam trap
Candidates often prioritize model fine-tuning or complex prompt engineering over basic architectural efficiencies like model selection and prompt caching, which provide more immediate cost benefits.
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
✓
Cache frequently used static system prompts or common context.
Optimizing API usage involves balancing model choice with intelligent prompt management. Choosing the right model for the task (Sonnet vs. Haiku) and ensuring input tokens are minimized through efficient prompting are the most effective ways to reduce operational overhead. These practices are fundamental to scaling Claude-based applications while maintaining a sustainable cost structure and ensuring that the API responds with low latency to handle high-volume user traffic.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use the largest available model for all tasks to ensure accuracy.
Why it's wrong here
Using the largest model for every task is inefficient and costly. Many tasks, such as classification or simple summarization, can be handled by smaller, faster models like Claude 3.5 Haiku, which provides significant cost savings without sacrificing the accuracy required for those specific, less-complex functional requirements.
- ✓
Cache frequently used static system prompts or common context.
Why this is correct
Prompt caching allows you to store long, static portions of your prompt, reducing the number of tokens processed in subsequent requests. This drastically decreases latency and lowers cost for applications that frequently reuse large amounts of reference documentation or complex instruction sets across many API calls.
- ✗
Set the temperature to 0 for all production API calls.
Why it's wrong here
Setting temperature to 0 makes responses deterministic but does not reduce costs or increase efficiency. It is a configuration for consistency, not optimization. Cost reduction is achieved by optimizing token counts and choosing cost-efficient models, not by adjusting parameters that dictate the randomness of the model's output.
- ✓
Select the most cost-efficient model that meets the latency and task quality requirements.
Why this is correct
Matching the model to the complexity of the task is the most effective way to optimize costs. By utilizing lighter models for simpler tasks and reserving more powerful models for complex reasoning, you minimize token expenditure and achieve a balanced, high-efficiency architecture that scales effectively for your organization.
- ✗
Increase the 'max_tokens' to the maximum allowed for every request.
Why it's wrong here
Setting 'max_tokens' to the maximum limit is poor practice, as it provides no performance benefit and risks unpredictable costs if the model generates excessively long, irrelevant responses. Always set this value to a reasonable limit based on the specific requirements of the intended response length.
About these practice questions
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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
This CCAO-F practice question is part of Courseiva's free Anthropic certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the CCAO-F exam.