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Anthropic CCAR-P Exam Syllabus Topics:

SectionWeightObjectives
Developer Productivity & Operational Enablement7%- Configure Claude tools and environments for teams
- Improve developer workflows with AI-assisted tooling
- Support debugging, monitoring, and operational resolution
Stakeholder Communication & Lifecycle Management14%- Communicate architectural decisions and trade-offs
- Manage stakeholder feedback and expectation alignment
- Document architectures and support full lifecycle phases
- Conduct structured discovery and requirement gathering
Governance, Safety & Risk Management14%- Address ethical AI considerations and bias mitigation
- Ensure regulatory compliance (GDPR, HIPAA, etc.)
- Implement guardrails and safety controls
- Manage data privacy and security compliance
Solution Design & Architecture17%- Design end-to-end architectures and feedback loops
- Design multi-agent systems and orchestration strategies
- Translate business problems into Claude-based AI solutions
- Align solutions to business value pillars
- Select architectural patterns: workflow, agentic, augmented LLM
Evaluation, Testing & Optimization16%- Define evaluation metrics and success criteria
- Test accuracy, reliability, latency, and cost
- Implement iterative improvement pipelines
- Optimize performance, prompting, and model selection
Claude Models, Prompting & Context Engineering13%- Mitigate prompt injection, leaks, and jailbreak risks
- Select appropriate Claude models based on trade-offs
- Design system prompts, templates, and guardrails
- Apply context engineering and context management techniques
Integration19%- Design authentication, authorization, and observability
- Implement Model Context Protocol (MCP) integrations
- Integrate Claude with enterprise systems, APIs, and tools
- Integrate with data pipelines and RAG systems

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Anthropic Claude Certified Architect - Professional Sample Questions (Q88-Q93):

NEW QUESTION # 88
You are a solution architect evaluating candidate use cases for a Claude-based program.
For each scenario, select Yes if Claude is appropriate as the primary solution at the architectural level.
Otherwise, select No.

Answer:

Explanation:

Explanation:
* Drafting first-pass investigative reports from semi-structured incident logs for analyst review - Yes
* Computing real-time fraud scores at sub-50-millisecond latency on a streaming pipeline - No
* Long-context contract review with structured clause extraction and deviation flagging - Yes
* Replacing a vector index for semantic retrieval over a multi-million-document corpus - No
* Routing inbound support tickets into 30 categories with reasoning for the selected route - Yes Claude is well suited to language-intensive analysis, summarization, extraction, classification, and drafting.
Investigative-report drafting, contract analysis, and support-ticket routing all require interpretation of unstructured or semi-structured language and can incorporate human review. A sub-50-millisecond fraud- scoring path requires deterministic, specialized real-time processing and should not place LLM inference on the critical path. Claude also does not replace the retrieval index used to search millions of documents; it consumes the results produced by that infrastructure. The correct architectural decision separates tasks requiring semantic language reasoning from deterministic computation and information-retrieval infrastructure. Model selection and evaluation must then confirm that the chosen Claude model satisfies quality, latency, and cost requirements.


NEW QUESTION # 89
You are listing characteristics of robust guardrail design for an enterprise deployment.
Which two characteristics belong on the list? (Select two.)
Each correct answer presents a complete solution.

Answer: A,D

Explanation:
Robust guardrails require both preventive enforcement and continuous empirical validation. Per-role tool allow-lists satisfy the preventive requirement by restricting each authenticated role to explicitly authorized tools. Enforcement must occur in the orchestration or permission layer before execution; relying on the model to decide whether a tool call is authorized is not an adequate security boundary. Anthropic recommends allow- list-based validation and provides permission rules and pre-tool-use controls capable of denying calls before they execute.
Adversarial evaluation with regression tracking supplies the validation component. The evaluation set should include prompt injections, indirect injections, encoded instructions, multi-turn escalation, role-confusion attempts, and requests designed to trigger unauthorized tool use. Guardrail performance must then be measured across releases so that changes to prompts, models, tools, or orchestration logic do not silently reduce protection.
Centralized logging and user feedback are useful detective and lifecycle controls, but neither prevents an unsafe action nor proves guardrail effectiveness. Periodically refreshing refusal wording is also insufficient unless the revised behavior is evaluated against defined safety criteria. The strongest design therefore combines deterministic authorization with adversarial regression testing.
Study Guide references/topics: Claude Code permissions and pre-tool enforcement ; tool allow-list guidance ; adversarial evaluations; regression testing; least-privilege orchestration.


NEW QUESTION # 90
You are distinguishing functional from non-functional requirements during discovery.
Which item is a non-functional requirement?

Answer: B

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 # 91
A technical team is debating whether to implement a new capability for generating regulatory filings from internal data as a Claude Skill or as an MCP server. The capability requires a fixed authoring procedure with embedded examples, does not need to call live external systems, and must be portable across three Claude deployments: claude.ai, an internal API integration, and Claude Code.
Which factor most strongly favors implementing the capability as a Skill?

Answer: D

Explanation:
A Skill is the appropriate packaging mechanism for reusable procedural knowledge, instructions, templates, examples, and supporting resources. The filing capability follows a defined authoring method and requires no live external-system interaction, so an MCP server would introduce unnecessary transport, authentication, hosting, and operational complexity. MCP becomes appropriate when Claude must invoke live databases, APIs, or enterprise services. Command-line availability does not independently determine the design because Skills can be used across supported Claude environments. Direct identity-provider authentication would favor an integration or MCP service rather than a self-contained Skill. Anthropic describes Agent Skills as portable packages that provide agents with specialized procedural and organizational expertise. Agent Skills


NEW QUESTION # 92
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: A

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 # 93
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