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Anthropic CCAR-P Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Solution Design & Architecture	17%	- Design end-to-end architectures and feedback loops - Align solutions to business value pillars - Translate business problems into Claude-based AI solutions - Design multi-agent systems and orchestration strategies - Select architectural patterns: workflow, agentic, augmented LLM
Topic 2: Claude Models, Prompting & Context Engineering	13%	- Design system prompts, templates, and guardrails - Select appropriate Claude models based on trade-offs - Apply context engineering and context management techniques - Mitigate prompt injection, leaks, and jailbreak risks
Topic 3: Evaluation, Testing & Optimization	16%	- Test accuracy, reliability, latency, and cost - Implement iterative improvement pipelines - Define evaluation metrics and success criteria - Optimize performance, prompting, and model selection
Topic 4: Integration	19%	- Implement Model Context Protocol (MCP) integrations - Integrate with data pipelines and RAG systems - Design authentication, authorization, and observability - Integrate Claude with enterprise systems, APIs, and tools
Topic 5: Stakeholder Communication & Lifecycle Management	14%	- Manage stakeholder feedback and expectation alignment - Communicate architectural decisions and trade-offs - Conduct structured discovery and requirement gathering - Document architectures and support full lifecycle phases
Topic 6: Governance, Safety & Risk Management	14%	- Ensure regulatory compliance (GDPR, HIPAA, etc.) - Address ethical AI considerations and bias mitigation - Manage data privacy and security compliance - Implement guardrails and safety controls
Topic 7: Developer Productivity & Operational Enablement	7%	- Improve developer workflows with AI-assisted tooling - Support debugging, monitoring, and operational resolution - Configure Claude tools and environments for teams

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Anthropic CCAR-P資格問題対応、CCAR-P認証pdf資料

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Anthropic Claude Certified Architect - Professional 認定 CCAR-P 試験問題 (Q36-Q41):

質問 # 36
You are responding to a stakeholder request that adds significant scope to a deployment already in flight.
Which response best aligns expectations while respecting the stakeholder's underlying need?

A. Accept the additional scope into the current release without describing the timeline or descoping implications, allowing the delivery plan to absorb unacknowledged changes until they surface as delays.
B. Acknowledge the request, describe the scope and timeline implications transparently, and propose options-defer it, descope something else, or extend the timeline-for the stakeholder to choose.
C. Hide the scope-addition request from the broader delivery team to avoid a difficult conversation, allowing the change to proceed without team awareness of its timeline implications.
D. Decline the request outright without acknowledging the stakeholder's underlying need or offering alternatives such as deferral, descoping, or timeline extension.

正解：B

解説：
Option C preserves both transparency and stakeholder agency. The architect acknowledges the underlying business need while making the delivery consequences explicit. Presenting controlled alternatives converts an unbounded request into a governed decision: defer the capability, exchange it for existing scope, or accept a later release date. The selected option should be documented with revised success criteria, dependencies, risk, cost, ownership, and approval. Concealing the request prevents accurate planning and creates unmanaged delivery risk. Rejecting it without exploring alternatives ignores legitimate stakeholder value. Quietly absorbing additional work produces scope creep and makes later delays appear unexpected. Effective lifecycle communication does not promise every requested capability; it presents the trade-offs necessary for an informed decision.
Study Guide references/topics: Scope governance; expectation alignment; transparent trade-offs; change control; stakeholder decision rights.

質問 # 37
You are distinguishing functional from non-functional requirements during discovery.
Which item is a non-functional requirement?

A. The system must respond at p95 latency under 800 milliseconds at the expected request volume.
B. The system must extract a defined set of specific fields from invoice attachments and populate a downstream data record.
C. The system must classify inbound tickets into a defined set of categories.
D. The system must produce a draft response that a human reviewer can edit before sending.

正解：A

解説：
Option D defines a measurable performance characteristic rather than a business capability. The requirement specifies a percentile-based latency threshold-under 800 milliseconds at the 95th percentile-and ties that threshold to the expected workload volume. It therefore describes how well the system must operate under defined conditions.
Options A, B, and C are functional requirements. They describe actions the solution must perform: extracting fields and creating a downstream record, producing an editable draft, and assigning tickets to categories.
Functional requirements define observable system behaviors or outputs.
A strong non-functional requirement must include a metric, threshold, measurement method, and operating condition. For Option D, the architect should further define whether latency is measured end-to-end or only at the model API, the test duration, concurrency, input-size distribution, cache state, timeout behavior, geographic region, and treatment of failed requests. Without those details, teams may report incompatible results while claiming to satisfy the same requirement.
Non-functional requirements for Claude solutions commonly address latency, availability, throughput, cost, privacy, security, recoverability, observability, scalability, and regulatory compliance.
Study Guide references/topics: Structured discovery; functional requirements; non-functional requirements; p95 latency; workload conditions; measurable acceptance criteria.

質問 # 38
You are a solution architect designing a Claude-based assistant with access to 60 internal tools across multiple business domains. Loading every tool definition on every request increases token usage and time to first response.
Which design pattern best addresses this issue without sacrificing capability breadth?

A. Use a separate model call to summarize all 60 tool definitions before each user turn.
B. Apply progressive tool discovery so a curated initial subset is exposed and additional tools are loaded on demand based on the task.
C. Increase the maximum context length and load all 60 tool definitions on every request, accepting the higher token cost and latency as necessary for full capability.
D. Hard-code a fixed set of five tools per request to reduce token usage, regardless of whether those tools are relevant to the current task.

正解：B

解説：
Progressive tool discovery preserves the complete capability catalog while limiting initial context to a curated set of common tools and a discovery mechanism. When the task requires an additional capability, Claude searches the catalog and loads only the relevant tool definitions.
Anthropic's Tool Search mechanism implements this pattern through deferred loading. The context initially contains the search tool and any deliberately non-deferred tools; relevant definitions are expanded only when discovered. This reduces token consumption and helps maintain tool-selection accuracy in large catalogs.
Tool Search Tool
Option B adds a model call and still repeatedly processes all definitions, increasing latency and cost while potentially losing important schema details. Option C treats a larger context window as permission to waste context and does not address time-to-first-response overhead. Option D reduces capability breadth because the fixed five tools may be irrelevant to a given task.
The architect should provide clear tool and server descriptions, define which high-frequency tools remain immediately available, and evaluate discovery recall, selection accuracy, latency, and context savings across representative workflows.
Study Guide references/topics: Progressive discovery; deferred tool loading; MCP scaling; context optimization; tool-selection accuracy; on-demand capability loading.

質問 # 39
You are diagnosing a Claude Code session whose subagent uses 50,000 tokens of context before the engineer types a single message.
Which root cause is most likely?

A. The model has internal personal preferences or default behaviors that silently consume large portions of context budget before any user message is processed, independent of tool configuration.
B. The font rendering or display-scaling settings of the IDE are converting visual output into additional context tokens, causing the high pre-session context consumption.
C. Many MCP servers are configured, each contributing tool definitions to the context budget; Tool Search is not enabled, so all definitions load upfront.
D. The developer's keyboard layout or input-method configuration is the cause of the elevated context consumption, introducing extra tokens before the engineer types any message.

正解：C

解説：
Claude Code constructs the model context before the first user message. That initial context can include the system prompt, project instructions, subagent configuration, MCP server instructions, and tool definitions expressed as JSON schemas. When numerous MCP servers expose many tools and Tool Search is disabled, those schemas must be loaded upfront. A large collection of verbose tool descriptions and parameter schemas can therefore consume tens of thousands of tokens before the engineer enters any text.
Tool Search is specifically designed to prevent this form of context inflation. With Tool Search enabled, Claude initially receives only server instructions and compact tool-identification information; full tool schemas are deferred until a relevant tool is discovered and required. Anthropic consequently describes Tool Search as the mechanism for keeping MCP context consumption low as integrations scale.
Keyboard layouts, font rendering, and display scaling operate outside the language-model context and cannot generate API input tokens. Likewise, model behavior does not silently reserve 50,000 tokens for personal preferences. The diagnostic action is to inspect configured MCP servers and tool counts, determine whether Tool Search has been disabled, and remove or defer unnecessary definitions.
Study Guide references/topics: [MCP Tool Search and context scaling](https://docs.anthropic.com/en/docs
/claude-code/mcp); MCP server configuration; subagent context budgeting; tool-schema optimization.

質問 # 40
You are rolling out monitoring for a Claude-based deployment and must complete the specification steps before instrumenting the deployment.
Which two steps must be completed BEFORE instrumenting the deployment to emit metrics and traces?
(Select two.)
Each correct answer presents part of the solution.

A. Document the dashboards, alerts, and runbooks for the on-call rotation that will respond.
B. Tune the alert thresholds based on observed normal-state distributions to reduce false positives.
C. Build the dashboards that surface metrics across slices at the cadence the team operates on.
D. Define the metrics and the slices the deployment will monitor across normal and adversarial traffic.
E. Define the service-level objectives (SLOs) and the error budgets the deployment will be held to.

正解：D、E

質問 # 41
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