CCAO-F Prompting and Context Engineering Practice Question
A data scientist is using Claude to classify customer feedback into categories: 'bug', 'feature request', 'complaint', or 'praise'. The feedback is often short and informal. The data scientist wants to maximize classification accuracy. Which prompting strategy is most effective?
⚠ Common exam trap
The trap here is thinking that step-by-step reasoning always improves performance, but for simple classification, few-shot examples are more direct and effective.
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
✓
Include a few examples of feedback for each category in the prompt.
For classification tasks with informal and varied input, providing a few labeled examples per category is the most effective prompting strategy. Examples allow Claude to learn the specific patterns and boundaries between categories, improving accuracy. This few-shot approach outperforms detailed definitions alone because it demonstrates how to handle nuances like slang or brevity. It also avoids the potential confusion of step-by-step reasoning for a straightforward task.
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 a high temperature to encourage diverse interpretations of the feedback.
Why it's wrong here
High temperature increases randomness, which is detrimental to classification accuracy. The goal is to consistently assign the correct category, not to generate diverse outputs. Lower temperatures are preferred for deterministic tasks like classification. High temperature would likely reduce accuracy.
- ✓
Include a few examples of feedback for each category in the prompt.
Why this is correct
Providing a few labeled examples for each category gives Claude a clear pattern to follow. It can learn the boundaries between categories from the examples, improving accuracy on informal and varied feedback. This few-shot approach is highly effective for classification tasks, especially when the input language is diverse.
- ✗
Provide a detailed definition of each category in the system prompt.
Why it's wrong here
Detailed definitions can help, but without examples, Claude may struggle to map informal language to the categories. Definitions alone may not capture nuances like sarcasm or slang. This approach is less effective than providing concrete examples that illustrate the categories in context.
- ✗
Ask Claude to think step-by-step about the sentiment before classifying.
Why it's wrong here
Step-by-step reasoning can sometimes help, but for simple classification, it may introduce unnecessary complexity. The model might overthink and produce inconsistent labels. Moreover, without examples, it may still misinterpret informal language. Direct few-shot examples are more reliable for this task.
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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.