CCDV-F Prompt and Context Engineering Practice Question
A developer is using few-shot prompting to help Claude classify customer emails. How should the examples be structured to maximize the model's performance?
⚠ Common exam trap
Candidates often provide raw text examples without delimiters, leading the model to confuse few-shot examples with the actual live user task instructions.
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
✓
Wrap each example in <example> tags, showing both the input and the correct label.
The structure and quality of examples in few-shot prompting are critical. Examples should be wrapped in XML tags to distinguish them from the actual task and should follow the exact format the developer expects in the final output. This consistent patterning allows Claude to mirror the demonstrated behavior with high precision and minimal instructional overhead.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Place all examples in the system prompt without any specific delimiters or tags.
Why it's wrong here
While examples can go in the system prompt, failing to use delimiters makes it harder for the model to distinguish where one example ends and another begins. This can lead to 'cross-contamination' of labels or content, where the model gets confused about which input corresponds to which classification label.
- ✗
Use a single, very long example that covers every possible edge case at once.
Why it's wrong here
A single long example is often less effective than several short, focused examples. Claude learns better from seeing the pattern repeated across different instances. A single complex example might be too idiosyncratic, causing the model to overfit to that specific case rather than learning the general classification rule.
- ✓
Wrap each example in <example> tags, showing both the input and the correct label.
Why this is correct
This is the recommended structure. Using <example> tags clearly demarcates the training data from the instructions. Providing both the input (the email) and the output (the label) creates a clear mapping for the model to follow, which is the core mechanism of successful few-shot learning.
- ✗
Provide only the labels in a comma-separated list and ask the model to guess the criteria.
Why it's wrong here
Providing only labels without the associated inputs is not few-shot prompting; it is essentially zero-shot with a list of categories. Claude cannot infer a classification pattern if it doesn't see how specific inputs map to specific outputs. This approach will result in much lower accuracy for nuanced tasks.
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 CCDV-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 CCDV-F exam.