CCAR-P Advanced Agentic Architecture Practice Question
An agent uses a retrieval tool that returns the top 20 chunks for any query. In production, the agent frequently cites irrelevant chunks and sometimes misses the correct answer even when it is present in the corpus. The corpus contains documents with overlapping terminology. Which architectural change most improves answer grounding without increasing the number of retrieved chunks?
⚠ Common exam trap
The trap here is assuming that more retrieved context or a lower temperature will fix grounding, when the actual defect is ranking precision within a fixed candidate set.
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
✓
Add a re-ranking stage that scores the retrieved chunks against the query and passes only the highest-scoring subset to the model.
The retrieval step is high-recall but low-precision, which is why irrelevant chunks are cited and correct ones are missed amid overlapping terminology. Adding a re-ranking stage that scores candidates against the query and passes only the top subset improves precision without retrieving more chunks, directly improving grounding and citation quality.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Embed the entire corpus into the system prompt so the model always has full context available.
Why it's wrong here
Embedding a full corpus in the system prompt is impractical for any realistic size and would vastly exceed context limits while dramatically increasing cost and latency. It also does not solve relevance: a model faced with an enormous undifferentiated context can still attend to the wrong passages. This approach bypasses retrieval rather than improving it and is not a viable architectural change for the described system.
- ✗
Increase the retrieval count to 50 so the correct chunk is more likely to be included somewhere in the context.
Why it's wrong here
Retrieving more chunks increases the chance that the correct passage appears, but it also floods the context with additional irrelevant material, worsening the citation problem and raising token cost. The scenario explicitly asks for improvement without increasing the number of retrieved chunks. More candidates without better ranking does not fix the precision issue caused by overlapping terminology.
- ✗
Lower the model's temperature to zero so it sticks more closely to the retrieved text.
Why it's wrong here
Temperature affects sampling randomness, not the relevance of retrieved chunks. At temperature zero the model is more deterministic, but it will still see the same irrelevant chunks and may still cite them. The root cause is retrieval precision, not sampling. Lowering temperature does not promote the correct passage or demote noisy ones, so the grounding problem persists.
- ✓
Add a re-ranking stage that scores the retrieved chunks against the query and passes only the highest-scoring subset to the model.
Why this is correct
Re-ranking applies a more precise relevance model to the candidate set, so the chunks most likely to contain the answer are promoted and the noisy ones are dropped. This improves grounding without retrieving more chunks, directly addressing both irrelevant citations and missed answers. It preserves the retrieval tool's contract while adding a precision layer that overlapping terminology makes necessary.
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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-P 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-P exam.