CCAR-F Context and Reliability Practice Question
A customer support platform uses Claude to answer policy questions by retrieving relevant help-center articles and inserting them into the prompt. Agents report that for questions whose answer spans two separate articles, Claude often cites only one article and omits the other. Logs show both articles were retrieved and included. Which change to the context assembly is most likely to resolve this?
⚠ Common exam trap
The trap here is assuming that a retrieval or generation-length problem is at fault when both documents are already present and the reply fits, when the real issue is how the context is delimited and instructed.
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 retrieved article in clearly labeled XML tags with source identifiers, and instruct Claude to synthesize across all provided sources before answering.
The failure is a context-structuring problem: two retrieved articles are present but Claude anchors on one. Delimiting each article with labeled tags and explicitly instructing synthesis across all sources gives the model clear document boundaries and a reason to reconcile them. Output limits, temperature, and text merging do not change how Claude parses or prioritizes the supplied context.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the max_tokens parameter so Claude has room to mention both articles in its reply.
Why it's wrong here
Max tokens governs the length of Claude's generated output, not how thoroughly it reads the supplied context. The reply already fits within current limits, so raising the ceiling adds no new reasoning capacity. The omission stems from how retrieved articles are presented inside the prompt, not from output truncation. Increasing output length would not cause Claude to reconcile two documents it is not cross-referencing.
- ✗
Concatenate both articles into a single unbroken text block so Claude treats them as one source.
Why it's wrong here
Merging documents removes the boundaries that let Claude attribute claims to a specific source, making citation worse rather than better. Without delimiters, the model cannot tell where one policy ends and the other begins, so it may blend or drop content. The goal is to preserve distinct sources while encouraging synthesis, which a single unbroken block actively undermines.
- ✗
Lower the temperature setting so Claude produces more deterministic and complete citations.
Why it's wrong here
Temperature affects sampling randomness, not coverage of supplied context. A lower temperature makes output more repeatable but does not make Claude consult a second article it is skipping. The reported behavior is consistent across sessions, indicating a structural prompt issue rather than sampling variance. Determinism alone will not cause the model to merge information from two distinct documents.
- ✓
Wrap each retrieved article in clearly labeled XML tags with source identifiers, and instruct Claude to synthesize across all provided sources before answering.
Why this is correct
Claude responds well to explicit structure and instruction. Labeling each article with tags and a source identifier makes the boundary between documents unambiguous, and directing Claude to synthesize across all sources counteracts the tendency to anchor on the first plausible passage. This directly addresses the failure mode of citing only one of two retrieved articles despite both being present in the prompt.
Visual reference
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 CCAR-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 CCAR-F exam.