CCAR-F Context and Reliability Practice Question
A customer support platform routes conversations to Claude 3.5 Sonnet via the Messages API. The system prompt includes 40,000 tokens of product documentation, and each turn appends the full prior transcript. After roughly 30 exchanges, agents report that Claude starts contradicting earlier troubleshooting steps it gave in the same conversation and drifts from the documented procedures. Latency and cost have also grown steadily. Which architectural change best addresses the reliability degradation while controlling cost?
⚠ Common exam trap
The trap here is assuming that prompt caching or a larger output budget solves context drift, when the actual cause is an unbounded transcript competing with the authoritative system prompt.
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
✓
Keep only the last few turns verbatim, and maintain a rolling structured summary of resolved issues and confirmed product facts that is re-injected each turn alongside the static documentation.
Long sessions accumulate raw transcript tokens until earlier, authoritative content competes with stale or contradictory turns. Bounding the history and replacing it with a maintained, re-injected summary preserves the decisions that matter while keeping the static documentation prominent. This controls both the reliability drift and the runaway token cost, which prompt caching or model swaps alone cannot fix.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Keep only the last few turns verbatim, and maintain a rolling structured summary of resolved issues and confirmed product facts that is re-injected each turn alongside the static documentation.
Why this is correct
Compressing the transcript into a maintained summary keeps the salient troubleshooting decisions in context while bounding token growth, so the model is not distracted by stale or contradictory raw turns. Re-injecting the summary with the stable documentation each turn keeps the authoritative procedures in view and stabilises behaviour across long sessions.
- ✗
Increase the temperature to 1.0 so the model explores more of the documentation and naturally self-corrects when it detects contradictions.
Why it's wrong here
Higher temperature increases sampling randomness, which makes contradictions and drift more likely, not less. Self-correction is not a reliable emergent behaviour at higher temperature. The scenario calls for tighter grounding and bounded context, and raising temperature works directly against both of those reliability goals.
- ✗
Enable prompt caching on the static system prompt and raise the max_tokens parameter so the model has more room to restate earlier steps.
Why it's wrong here
Prompt caching reduces cost and latency for the repeated documentation prefix, but it does nothing about the accumulating transcript that causes the drift. Raising max_tokens only allows longer individual responses; it does not give the model more context window, nor does it prevent earlier troubleshooting steps from being diluted by an ever-growing history.
- ✗
Switch the model to Claude 3 Haiku for the long sessions, since a faster model will complete each turn before the context window fills and drift begins.
Why it's wrong here
Model speed has no bearing on context window limits or on how prior turns influence later completions. Haiku is a smaller, less capable model, so summarisation and adherence to complex documented procedures would likely degrade further. The drift stems from an unbounded transcript, which switching models does not address.
About these practice questions
Courseiva writes every CCAR-F question from scratch — 271 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
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 CCAR-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 CCAR-F exam.