CCAO-F Prompting and Context Engineering Practice Question
A developer is building a customer support assistant using Claude. The assistant must always respond in a friendly tone, never discuss competitors, and always ask for an order number when the user reports a shipping issue. The developer wants these rules to apply across all conversations with minimal per-request token cost. What is the most appropriate mechanism?
⚠ Common exam trap
The trap here is thinking that repeating instructions in every user message is equivalent to setting a system-level policy.
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
✓
Place the rules in the system prompt so they are applied consistently to every request.
The system prompt is the correct place for persistent behavioral instructions that should apply to every request. It is processed by the model as a high-level directive and is not repeated in each user message, making it token-efficient. This ensures consistent tone, competitor restrictions, and required questions across all conversations without per-request repetition.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Add the rules to the end of each user message as a reminder.
Why it's wrong here
Appending rules to every user message increases token usage and can be overridden by the user's own text. It also does not establish a persistent behavioral contract. While reminders can help, they are less reliable and more expensive than a system prompt for global rules.
- ✓
Place the rules in the system prompt so they are applied consistently to every request.
Why this is correct
The system prompt is designed to provide persistent instructions that apply across all turns of a conversation. It is the correct place for behavioral rules like tone, competitor mentions, and required questions. This approach is token-efficient because the system prompt is sent once per request but not repeated in each user message.
- ✗
Include the rules as a few-shot example in every user message.
Why it's wrong here
Few-shot examples in each user message increase token usage and do not guarantee consistent rule adherence. They also mix instructions with user input, which can confuse the model when the user message is long. This approach is costly and less reliable than a dedicated system-level instruction.
- ✗
Fine-tune a custom model with the rules embedded in training data.
Why it's wrong here
Fine-tuning is a heavyweight solution that requires training data, cost, and maintenance. It is not necessary for simple behavioral rules that can be expressed in a system prompt. Fine-tuning may also make it harder to update rules quickly, as changes would require retraining.
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.