CCAO-F Using the Claude API Practice Question
A developer maintains a long-running support session and wants to keep the conversation coherent without exceeding the model's context window. They currently resend the entire transcript on every turn. Which approach best addresses the context limit while preserving conversational continuity?
⚠ Common exam trap
Many candidates confuse max_tokens, which caps generated output, with the context window, which bounds the entire input plus output.
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
✓
Summarize or truncate older turns and send only the relevant recent messages plus a summary.
The context window limits the combined tokens of input and output. Because the developer resends the full transcript each turn, the payload grows until it overflows. Condensing older turns into a summary or trimming stale messages, while keeping recent exchanges intact, preserves continuity and keeps each request within the token budget.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Summarize or truncate older turns and send only the relevant recent messages plus a summary.
Why this is correct
The context window bounds total input plus output tokens. Condensing older turns into a summary or dropping stale messages keeps the payload within that bound while retaining the gist needed for continuity, which directly solves the growing-transcript problem for this support session.
- ✗
Increase 'max_tokens' so the model can hold more of the transcript in memory.
Why it's wrong here
max_tokens caps the number of tokens generated in the response, not the size of the input context. Raising it lets the model write longer answers but does nothing to fit a growing transcript, so the request would still eventually exceed the context window in this support session.
- ✗
Set a higher 'temperature' so the model compresses prior context automatically.
Why it's wrong here
Temperature controls sampling randomness and has no compression effect on the input. Turning it up makes responses more varied, not more compact, and leaves the transcript just as large, so the context window would still be exceeded in this scenario.
- ✗
Switch the conversation to a single user message that concatenates all prior turns into one string.
Why it's wrong here
Concatenating every turn into one user message does not reduce token count; it is the same content in a different shape and will still overflow the window. It also removes the role structure the model uses to distinguish speakers, harming coherence without solving the underlying size problem.
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.