Exam Code: CCAR-P
Exam Name: Claude Certified Architect - Professional
Price: $68.00 $58.88
Exam Questions: 114 Q&As
Last Updated: 2026-08-31
The Claude Certified Architect - Professional certification validates that practitioners can make informed architectural tradeoffs when implementing production-grade solutions with Claude.
CCAR-P is designed for solution architects working across Claude Code, the Claude Agent SDK, the Claude API, and Model Context Protocol (MCP).
The exam uses multiple-choice and multiple-response items, with each item stating how many responses to select. Its four-scenario structure rewards consistent decisions across a shared architecture rather than isolated product recall.
Result reporting includes pass/fail, a scaled score from 100–1,000, and percent-correct by domain. Strong candidates can explain why an architecture fits its reliability, cost, latency, security, and operational constraints.
Look for the architecture component that changed immediately before the failure, then diagnose the closest layer first.
Prefer the smallest design that satisfies the stated quality bar; additional agents, retrieval, or reasoning are not automatically improvements.
Largest domain: select between direct calls, prompt chains, routers, parallel workers, evaluators, and agent loops. The key is matching autonomy and coordination cost to the uncertainty of the task.
Design for dependable action: use clear tool contracts, constrained schemas, least privilege, useful errors, and MCP boundaries that preserve ownership and provenance.
Operational focus: scope project guidance, rules, commands, subagents, and repository workflows correctly. Shared standards belong in version-controlled project configuration rather than personal memory.
Reliability begins at the interface: state constraints, provide the minimum useful examples, and use schemas or structured outputs when downstream systems require predictable fields.
Diagnose before expanding context: distinguish retrieval, indexing, context-window, cache, semantic-validation, and silent-failure problems. More context does not repair stale or irrelevant evidence.
Separate model capability from system reliability, retrieval quality from generation quality, and tool execution from orchestration policy. The wrong layer can produce a plausible fix that increases cost without addressing the failure.
Week 1: orchestration, tools, and MCP. Week 2: Claude Code, prompts, schemas, and context. Week 3: cost and reliability tradeoffs, four-scenario timed sets, and an ADR-style error log for every missed architectural decision.
Spend the first pass mapping actors, data sources, tools, constraints, and recent changes. For each item, identify the requested quality attribute—accuracy, cost, latency, reliability, or reversibility—and reject options that optimize a different problem.
These independent educational questions focus on architectural diagnosis and tradeoffs. Use the scenario evidence before applying a familiar pattern.
A grounded policy assistant began returning confident but outdated answers right after the knowledge base was refreshed with new documents. The model version and prompt were untouched. Where should diagnosis begin?
Correct answer: A
Explanation: The failure began immediately after a knowledge refresh while the model and prompt stayed constant. That change points first to ingestion, indexing, retrieval freshness, or chunk selection. Inspect whether the new corpus was indexed correctly and whether queries return current evidence.
Architecture tradeoff: Layered diagnosis minimizes unnecessary changes. Fixing retrieval is cheaper and more targeted than retraining or destabilizing a known prompt.
Common mistake: Treating confident wording as a generation problem when the model may be faithfully using stale retrieved context.
Why the other options are wrong: B is premature because the knowledge is external. C cannot force freshness when stale evidence is supplied. D may reduce variation, but it can make the same outdated answer more consistent.
A high-volume classification service must hit a strict cost-per-request target while meeting its accuracy bar. It currently runs the largest model in real time with a large static rubric resent each call. As architect, which combination best lowers cost while preserving accuracy for this real-time path?
Correct answer: D
Explanation: The service needs the lowest-cost design that still meets a defined accuracy threshold. A smaller qualifying model reduces token cost, while caching the stable rubric avoids repeatedly processing the same prefix on every real-time request.
Architecture tradeoff: Optimize against the actual service-level objective, not maximum possible accuracy. Right-sizing preserves the bar while reducing recurring cost and latency.
Common mistake: Adding architectural sophistication before testing whether a smaller model already satisfies the measured requirement.
Why the other options are wrong: A increases compute. B adds retrieval and reranking work without a retrieval requirement. C adds multiple calls and latency. All three move against the strict cost-per-request target.
An architect must decide which choices to capture in the append-only decision log. Which decision MOST warrants an Architecture Decision Record?
Correct answer: B
Explanation: A hybrid retrieval strategy affects data flow, quality, latency, dependencies, evaluation, and future maintenance across the system. It is consequential, cross-cutting, and relatively expensive to reverse, which makes its context and rationale worth preserving.
Architecture tradeoff: ADRs preserve important reasoning but create maintenance overhead. Record decisions whose reversal cost and system impact justify that overhead.
Common mistake: Using the decision log as a chronological diary of every implementation choice, making significant decisions harder to find.
Why the other options are wrong: A, C, and D are local, low-cost, easily reversible choices. Normal code review, formatting configuration, or source history already provides sufficient traceability.
Generating a chunk-specific context for every chunk in a large corpus sounds expensive, since it requires a model call per chunk. A team worries the indexing cost is prohibitive. What makes this step affordable in practice?
Correct answer: B
Explanation: Prompt caching allows the source document prefix to be processed once and reused while generating context for its many chunks. The repeated calls then benefit from the cached document rather than paying full processing cost each time.
Architecture tradeoff: Contextual indexing spends more during ingestion to improve retrieval quality later; caching controls that one-time cost without discarding document-level context.
Common mistake: Memorizing a temporary price figure instead of understanding which reusable prefix makes the workflow economical.
Why the other options are wrong: A removes the document context that gives the technique value. C concerns a different execution and timing claim. D may change retrieval design but does not eliminate the model calls used to create contextual descriptions.
A grounded assistant returns fluent answers that are occasionally wrong, with no error codes raised. Which control catches this failure class?
Correct answer: C
Explanation: The request succeeds technically, so transport and application error handling see no failure. A semantic gate evaluates content-level properties such as grounding, citation support, schema integrity, policy compliance, or contradiction before the answer is accepted.
Architecture tradeoff: Semantic validation adds latency and cost, but it targets silent quality failures that availability controls cannot observe. Apply it where the consequence of a wrong answer justifies the check.
Common mistake: Assuming operational success and content correctness are the same reliability dimension.
Why the other options are wrong: A activates only on detectable errors. B addresses slow completion. D addresses unhealthy infrastructure. None inspects whether a fluent response is semantically supported.
Practise maintaining a consistent architecture model across several related questions. Record constraints and recent changes before answering individual items.
Each item states how many responses to select. Read that instruction before evaluating options so you do not stop after finding one plausible answer.
Results include pass/fail, a scaled score from 100–1,000, and percent-correct by domain.
The credential is valid for 12 months from the award date. Check current renewal requirements with the certification provider.
Yes. The five examples above show scenario evidence, tradeoff analysis, common mistakes, and distractor elimination.
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Use each scenario to identify the changed layer, the governing constraint, and the smallest reliable design.
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