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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 16

Question#16Topic1

You are designing a content moderation classifier that processes high volumes of user-generated comments under a tight per-message latency budget using well-defined classification labels.
Which model selection best aligns with the workload?

  1. A. Claude Opus with extended thinking enabled on every comment.
  2. B. Claude Haiku for high-throughput, low-latency classification into the fixed moderation labels.
  3. C. Claude Sonnet with the largest available max output tokens on every message.
  4. D. Batch-only inference with no synchronous online moderation path.
Show answer and explanation

Answer

B

Explanation

High-volume, latency-sensitive closed-set classification aligns with the fastest Claude tier (Haiku) while reserving heavier models for complex reasoning workloads. Validate accuracy with a representative eval set before production. See https://docs.anthropic.com/en/docs/about-claude/models/overview

Question 17

Question#17Topic1

HOTSPOT -
You are evaluating prompting claims in a peer’s design document.
For each claim, select yes if the claim reflects sound practice. Otherwise, select no.
Question illustration

Show answer and explanation

Answer

AABAB

Explanation

Heavier models trade cost/latency for capability; validate on representative workloads. Avoid 'always largest model' and release-date-driven selection. Tiered routing optimizes cost/latency while preserving quality where needed.

Question 18

Question#18Topic1

HOTSPOT -
You are evaluating model-selection claims used by a peer team.
For each statement, select yes if the statement is generally true. Otherwise, select no.
Question illustration

Show answer and explanation

Answer

AABAB

Explanation

Larger models trade throughput and latency for capability; production selection should be workload-driven with representative evaluation, tiered routing, and regression checks—not release-date heuristics or always using the largest model. See https://docs.anthropic.com/en/docs/about-claude/models/overview and https://docs.anthropic.com/en/docs/test-and-evaluate/eval-tool

Question 19

Question#19Topic1

You are selecting a model for a new production workload and must complete the upstream steps before testing candidate models empirically.
Which two steps must be completed BEFORE running a representative sample on a candidate model? (Choose two.)
Each correct answer presents part of the solution.

  1. A. Document measurable success criteria for quality, latency, and cost aligned to the production use case.
  2. B. Run the representative sample on every available model in parallel without a rubric.
  3. C. Assemble a representative evaluation dataset drawn from production-like inputs and edge cases.
  4. D. Promote the newest flagship model to production to establish a baseline.
  5. E. Instrument dashboards before defining which metrics matter.
Show answer and explanation

Answer

AC

Explanation

Before empirical model trials, define what “good” means (criteria/rubric) and build a representative eval set that mirrors real inputs; only then run candidate models against that set. See https://docs.anthropic.com/en/docs/test-and-evaluate/develop-tests and https://docs.anthropic.com/en/docs/test-and-evaluate/eval-tool

Question 20

Question#20Topic1

You are investigating an MCP server that fails on first launch but succeeds on subsequent runs. The system permission dialogs appeared on first launch.
Which response is most appropriate?

  1. A. Reinstall the operating system to clear all permission state without first confirming whether the one-time permission grant caused the failure.
  2. B. Treat the first-run failure as a permanent fault, replace the MCP server, and do not verify whether subsequent runs succeed.
  3. C. Disable operating-system permission dialogs entirely, accept the resulting security implications, and proceed without confirming whether the failure recurs.
  4. D. Recognize the first-run permission grant as the cause, document the expected behavior in onboarding guidance, and confirm that subsequent runs succeed.
Show answer and explanation

Answer

D

Explanation

First-run OS permission prompts (workspace trust, MCP approval, macOS TCC) are expected during initial MCP onboarding. After grants are persisted, subsequent launches should succeed; document this in operator guidance rather than treating it as a defective server. See Anthropic Claude Code MCP onboarding and permission flows.

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