CCAR-P Practice Question: Stakeholder Communication and Lifecycle Management
Six months into production, a Claude-based claims-triage system shows a slow decline in acceptance of its recommendations, from 82 percent to 61 percent, with no code or model changes. The operations manager asks what to do. Which investigation best addresses the root cause?
⚠ Common exam trap
The trap here is reaching for a model-side remedy such as retraining or rollback when the absence of code and model changes makes an input, population, or human-judgement cause far more likely.
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
✓
Segment acceptance by claim type, region, and time, and review samples of recently rejected recommendations with the adjusters who rejected them.
When a stable system degrades without code or model changes, the likely drivers are input drift, population change, or evolving human judgement. Segmenting the metric localizes the problem, and reviewing rejected recommendations with the adjusters reveals the mechanism behind each rejection. Retraining, rolling back an unchanged version, or passively monitoring all bypass that diagnosis and either risk worsening the issue or prolonging it.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Segment acceptance by claim type, region, and time, and review samples of recently rejected recommendations with the adjusters who rejected them.
Why this is correct
A gradual decline without code changes points to drift in inputs, population, or human expectations, none of which a single global metric can reveal. Segmenting isolates where the drop concentrates, and reviewing rejected samples with the people who rejected them surfaces the actual failure mode, whether new claim patterns, stale reference data, or shifting adjuster judgement.
- ✗
Roll back to the model version that was in production when acceptance was 82 percent.
Why it's wrong here
There were no model changes, so the earlier version is the same version currently running. Rolling back is technically meaningless here and would consume change-management effort without altering behaviour. It also skips the investigation entirely, leaving the real driver, likely input or population drift, unidentified and free to recur.
- ✗
Immediately retrain or fine-tune the model on the most recent accepted claims to restore the previous acceptance rate.
Why it's wrong here
Fine-tuning on accepted claims before knowing why acceptance fell risks encoding the wrong pattern and creating a feedback loop that amplifies whatever bias caused the decline. It also treats a symptom rather than a cause, and it discards the diagnostic information contained in the rejected cases. Diagnosis should precede any model change.
- ✗
Report the metric to the steering committee as a normal variance and continue monitoring for another quarter.
Why it's wrong here
A 21-point drop is far outside normal variance and directly affects operational trust in the system. Deferring action for a quarter risks entrenching adjuster distrust that is hard to reverse, and it wastes time when the cause may be a simple data-freshness or routing issue. Passive monitoring is not a proportionate response to a sustained decline.
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
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