CCAR-F Context and Reliability Practice Question
You are architecting a customer-support assistant that maintains a persistent knowledge base across sessions. After several weeks, users report that the assistant confidently cites policy details that were never in the source documents. Which architectural change most directly addresses this reliability failure?
⚠ Common exam trap
The trap here is assuming that lowering temperature or adding emphatic accuracy instructions makes a model factual, when grounding requires external retrieval and an explicit no-answer path.
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
✓
Require the assistant to answer only from retrieved passages and to return a sentinel value when retrieval yields no supporting passage.
The reported failure is unsupported factual claims, so the fix must tie answers to verifiable source content. Constraining the assistant to retrieved passages and defining a no-answer sentinel gives the system an explicit grounding contract and a detectable failure state. Determinism settings, longer outputs, and accuracy exhortations change style or length but never establish whether a cited policy actually exists.
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 the assistant has more room to explain its reasoning before answering.
Why it's wrong here
Raising max_tokens only changes how much output the model may generate per turn; it does nothing to verify whether cited facts exist in the knowledge base. The hallucinated policy details would still be produced, just potentially with longer surrounding prose. This setting governs response length, not factual grounding, so it fails to address the root reliability failure in this scenario.
- ✗
Add a system prompt instruction telling the assistant to never make mistakes and to always be accurate.
Why it's wrong here
Generic instructions to be accurate do not supply the model with the policy content it lacks, nor do they give it a mechanism to detect unsupported claims. Such prompts can even increase confident phrasing without improving factual grounding. Reliability here depends on retrieval and verification structure, not on exhortation, so this change would not stop the fabricated citations.
- ✓
Require the assistant to answer only from retrieved passages and to return a sentinel value when retrieval yields no supporting passage.
Why this is correct
Grounding responses in retrieved passages and returning a defined sentinel when nothing supports the claim prevents the model from fabricating policy details. This makes the assistant's factual claims traceable to source content and gives downstream logic an explicit signal that no answer exists. It directly targets the confidence-without-evidence failure described in the scenario.
- ✗
Lower the temperature parameter to zero so the assistant produces more deterministic responses.
Why it's wrong here
Temperature zero reduces sampling randomness, making repeated outputs more consistent, but a consistently wrong citation is still wrong. Determinism does not introduce missing source material or verify that a stated policy exists. The reported symptom is fabricated content, not variability, so tightening sampling parameters leaves the underlying grounding gap entirely unresolved.
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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.