CCDV-F Prompt and Context Engineering Practice Question
Which TWO of the following practices are considered best practices for optimizing Claude's performance using prompt engineering? (Choose two)
⚠ Common exam trap
Candidates often rely on unstructured conversational text for complex prompts, forgetting that explicit task instructions and structural XML tags are essential for reliable model parsing.
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
✓
Use XML tags (e.g., <instructions>...</instructions>) to delineate different sections of the prompt.
Prompt engineering is about clarity and structure. By providing specific constraints and using XML tags, you create a structured input that Claude can parse more effectively. These techniques minimize ambiguity and provide clear boundaries for where the model should focus its attention, ultimately leading to higher quality, more consistent, and more predictable outputs across varying inputs and complex tasks.
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 XML tags (e.g., <instructions>...</instructions>) to delineate different sections of the prompt.
Why this is correct
XML tags provide clear delimiters that help Claude distinguish between instructions, source text, and examples. This structure reduces noise in the prompt and allows the model to better understand the role of each segment, significantly improving performance when handling complex tasks with multiple distinct components or data sources.
- ✗
Include long, conversational filler text to make the model feel more comfortable.
Why it's wrong here
Conversational filler text increases the token count without adding useful information. This 'prompt bloat' can distract the model and increase latency. Concise, direct instructions are always preferred as they minimize the chance of the model being misled by irrelevant information embedded within the conversational preamble.
- ✓
Provide clear, specific, and unambiguous task instructions.
Why this is correct
Ambiguity is the enemy of consistent model output. By being explicit about the required task, format, and constraints, you leave less room for the model to make incorrect assumptions. Clear instructions act as a roadmap for the model, ensuring that the generated output aligns with the user's intent.
- ✗
Avoid using examples, as they encourage the model to copy rather than think.
Why it's wrong here
Few-shot prompting is one of the most powerful techniques for improving performance. Providing examples helps the model understand the expected format, tone, and logic. Rather than preventing thought, examples provide a concrete baseline that helps the model achieve the desired output quality more reliably and efficiently.
- ✗
Always set the maximum token count to 4096, regardless of the task.
Why it's wrong here
Setting the maximum token count should be based on the requirements of the specific task. Setting a blanket high limit does not improve performance and can lead to unnecessary costs or unexpected truncations if the model continues outputting irrelevant content beyond what is actually needed for the response.
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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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