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Anthropic CCAR-P Exam Syllabus Topics:

SectionObjectives
Topic 1: Evaluation and Optimization- Model and architecture optimization
- Evaluation design and acceptance criteria
Topic 2: Governance, Safety, and Risk- Security, compliance, risk management, and governance
- Safety controls and fail-closed system design
Topic 3: Stakeholder Engagement and Solution Lifecycle- Stakeholder discovery and architectural trade-off communication
- Solution lifecycle, handoff, adoption, and operational enablement
Topic 4: Solution Design- Claude platform and solution architecture
- Model, architecture, context, and deployment strategy selection
Topic 5: Enterprise Integration and Production- Enterprise integration architecture and production readiness
- Cost, latency, reliability, and operational design

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Anthropic Claude Certified Architect - Professional Sample Questions (Q46-Q51):

NEW QUESTION # 46
You are supporting an engineer whose newly added MCP server does not appear in their Claude Code session.
Which diagnostic step should be tried first?

Answer: C

Explanation:
The first diagnostic action must establish whether Claude Code has loaded the MCP server configuration and whether it can connect to the registered endpoint. The official management command is claude mcp list; within Claude Code, /mcp can also be used to inspect server status. The listing distinguishes registered and connected servers from configurations that require authentication, approval, or connection troubleshooting.
Connect Claude Code to Tools via MCP
If the server was added or its configuration changed after the current session began, reconnecting or restarting the relevant Claude Code session is a proportionate next step. This ensures that configuration state and available tool definitions are refreshed before more intrusive troubleshooting begins. If the server is listed but unhealthy, the engineer can then inspect it with claude mcp get < name > and investigate transport, command path, credentials, workspace approval, or endpoint connectivity.
Options A, B, and D are operationally unsound because they discard working capabilities or environments before confirming the basic registration state. Disabling all MCP servers eliminates useful diagnostic comparisons; changing product families abandons the relevant configuration; reinstalling the operating system is grossly disproportionate and risks destroying evidence needed for diagnosis.
Study Guide references/topics: Claude Code environment configuration; MCP registration; server-status inspection; session refresh; targeted operational troubleshooting.


NEW QUESTION # 47
You are configuring Claude Code for a team that needs every engineer to share the same MCP server set, permission rules, and project context across the engineering monorepo.
Which configuration scope best supports this requirement?

Answer: B

Explanation:
Project scope is designed for configuration that must be shared by everyone working in a repository.
Committing the approved configuration provides versioning, review history, repeatable onboarding, and consistent MCP and permission behavior across the monorepo. User scope is intended primarily for individual preferences and personal rules, so independently maintained user files create configuration drift. Local scope affects only one person in one project and is therefore unsuitable for team-wide distribution. A lead architect's local configuration cannot establish a shared environment. Claude Code documentation specifically describes .
claude/settings.json as the shared project settings file that teams check into source control. Security-critical policies that must not be overridden should additionally be enforced through managed organizational settings rather than relying exclusively on repository configuration. Claude Code: Settings files and precedence


NEW QUESTION # 48
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.

Answer:

Explanation:

Explanation:
Yes, Yes, No, Yes, No
Zero-shot prompting is a reasonable baseline for a clearly defined closed-set classifier because the model receives both the permissible labels and their meanings. Performance should then be measured against an evaluation set before additional prompt complexity is introduced. Few-shot examples are also sound for extraction because examples containing the exact field names demonstrate the required schema and reduce formatting ambiguity. Anthropic identifies examples as one of the most reliable methods for steering output format, structure, and consistency. Prompting Best Practices Chain-of-thought is not an unconditional default. Additional reasoning is appropriate when the task requires substantive decomposition or complex judgment, but routine classification and extraction may gain little while consuming more tokens and increasing latency. The technique should be selected according to evaluated task requirements.
An even-handed comparison governed by explicit criteria mitigates leading-question bias by requiring symmetrical treatment of alternatives rather than presupposing a preferred conclusion. Finally, prompt validation is model-specific. New model versions may follow instructions differently, change formatting behavior, alter tool selection, or produce different latency and cost characteristics. Anthropic's migration guidance consequently recommends reevaluating prompts and workloads against the new baseline. Model Migration Guide Study Guide references/topics: Zero-shot prompting; few-shot examples; reasoning techniques; neutral framing; model migration and regression evaluation.


NEW QUESTION # 49
You are classifying token-management tactics by where in the request lifecycle each tactic applies.
For each tactic, select where in the request lifecycle it applies: Input Preparation , Prompt Construction , or Output Handling .

Answer:

Explanation:

Explanation:
Input Preparation reduces the amount of unnecessary information before the request is assembled.
Summarizing older conversation history and trimming retrieved passages preserve relevant context while reducing token consumption. Prompt Construction determines how prepared content is organized for the model. Stable instructions and repeated reference material should appear in a cacheable prefix, while variable per-request content follows afterward, improving prompt-cache reuse and lowering processing cost. Output Handling begins after Claude generates the response. Structured output must be validated against the required schema before downstream systems consume it. Once validated, the result can be persisted for later processing, audit, or compliance purposes. Correctly separating these tactics prevents validation and persistence from being confused with input optimization while ensuring that caching decisions are implemented during prompt assembly rather than during retrieval or post-processing.


NEW QUESTION # 50
You are distinguishing functional from non-functional requirements during discovery.
Which item is a non-functional requirement?

Answer: A

Explanation:
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.


NEW QUESTION # 51
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