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CCAR-P Exam Practice Questions

Claude Certified Architect – Professional

CCAR-P English PDF practice questions: 125 questions in source order, with options supported by the source format, answers and available explanations. Access covers this exam only. One-time payment; no automatic renewal.

Exam overview

For enterprise architects addressing integration architecture, governance and evaluation in large-scale Claude deployments.

The provider describes exams for Claude Partner Network members. Confirm eligibility, languages and scheduling in Partner Academy.

Exam code
CCAR-P
Official exam / credential
Claude Certified Architect – Professional
Exam format
Proctored, identity-verified certification assessment delivered through Pearson
Official exam languages
A complete language list could not be confirmed from the public official material. Check the provider’s booking page.

Official exam languages and practice-question file languages are listed separately. Confirm the exam version and available languages when booking with the provider.

Official sources: Provider reference
Information checked: 2026-10-20

Supported languages

  • English
  • Simplified Chinese
Try before you buy

First 20 practice questions

125 total questions

Questions follow the source order. Expand each answer to see its explanation.

Disclaimer: All questions on ExamPico are compiled from internet sources and are provided for study reference only.

Question 11

Question#11Topic1

You are reviewing a peer’s draft system prompt that contains contradictory instructions: one section says to never speculate beyond the supplied source, and another says to confidently fill in any gaps.
Which response is most appropriate?

  1. A. Approve the draft and rely on the model to resolve conflicts implicitly at runtime.
  2. B. Recommend revising the prompt to remove the contradiction and adopt one grounding policy: answer from supplied sources and explicitly state when information is missing.
  3. C. Keep both instructions so attorneys can choose the behavior per session.
  4. D. Delete the anti-speculation section because gap-filling improves perceived completeness.
  5. E. Escalate to legal review without changing the prompt text.
Show answer and explanation

Answer

B

Explanation

Conflicting system instructions produce unpredictable behavior; Anthropic prompt guidance emphasizes clear, consistent instructions and explicit context rather than opposing mandates. The architect should send the draft back with a single policy grounded in provided sources and transparent uncertainty. See https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/be-clear-and-direct

Question 12

Question#12Topic1

A document analysis service processes legal filings averaging 80,000 tokens each. Each filing is queried by attorneys an average of 14 times during a case. The current architecture sends the full filing on every query. The CFO has asked you to reduce per-query costs while preserving response quality. The security officer requires that filing contents not be stored outside Fabrikam’s tenancy.
Which optimization approach should you recommend?

  1. A. Mirror each full filing to a shared public object store so all attorneys reuse one URL.
  2. B. Cache each filing as a stable prompt prefix with prompt caching and send only the new attorney question on repeat queries in the same matter.
  3. C. Move all interactive attorney queries to the Batch API to eliminate synchronous inference charges.
  4. D. Replace the filings with 500-token summaries and discard the source text from the pipeline.
  5. E. Store vectors in a third-party SaaS index outside Fabrikam tenancy to avoid resending tokens.
Show answer and explanation

Answer

B

Explanation

The workload resends the same ~80k-token document across ~14 queries per case; prompt caching reuses a stable prefix so later calls pay primarily for new question tokens while preserving full-document context under commercial data handling. Place static filing content before cache breakpoints and variable questions after. See https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching

Question 13

Question#13Topic1

HOTSPOT -
You are classifying chunking strategies by the corpus type each is best suited to.
For each chunking strategy, select where in the request lifecycle each applies: “Long Structured Documents”, “Heterogeneous Short Records”, or “Code or Hierarchical Specs”.
Question illustration

Show answer and explanation

Answer

CCBABA

Explanation

Code/spec corpora use function-level or hierarchical/tree chunking; short heterogeneous records use per-record or fixed-size overlap chunking; long structured documents use semantic or hierarchical section-aware chunking.

Question 14

Question#14Topic1

You are designing the prompt for a ticket-triage classifier with thirty well-defined categories and clear category descriptions in the prompt.
Which technique is most appropriate as the starting point?

  1. A. Zero-shot classification using the thirty category definitions in the system prompt.
  2. B. Extended chain-of-thought on every ticket before assigning a category.
  3. C. Custom fine-tuning before establishing any zero-shot baseline.
  4. D. Multi-model debate with three models per ticket.
  5. E. Randomized category assignment with quarterly manual relabeling only.
Show answer and explanation

Answer

A

Explanation

With a closed label set and explicit category descriptions, zero-shot prompting is the appropriate first technique; add few-shot or evaluation only if metrics show format or boundary errors. See https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/use-xml-tags and https://docs.anthropic.com/en/docs/test-and-evaluate/develop-tests

Question 15

Question#15Topic1

HOTSPOT -
You are evaluating retrieval-strategy claims used by a peer team.
For each claim, select yes if the statement is generally accurate. Otherwise, select no.
Question illustration

Show answer and explanation

Answer

AAABB

Explanation

Dense retrieval handles semantic paraphrase; sparse/BM25 helps exact tokens; SQL/structured engines handle filter/aggregate workloads. Hybrid combines strengths and is not equivalent to dense-only; random sampling is not a production retrieval strategy.

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