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Anthropic CCAR-P Exam Syllabus Topics:

SectionWeightObjectives
Claude Models, Prompting & Context Engineering13%- Context window optimization
- Guardrails
- Claude model selection and trade-offs
- System prompts and prompt templates
- Prompt reuse and context engineering strategies
Evaluation, Testing & Optimization16%- System issue diagnosis
- Cost and performance optimization
- Evaluation metrics and datasets
- Evaluation framework design
- A/B testing
- Production monitoring and optimization
Governance, Safety & Risk Management14%- AI safety and guardrails
- Security and risk management
- Regulatory and compliance requirements
- Human-in-the-loop validation
- Ethical AI considerations
Solution Design & Architecture17%- Translating business problems into Claude-based AI solutions
- Decomposition techniques for complex problem solving
- Multi-agent systems and orchestration
- Alignment with business value, cost, performance, and SLAs
- Architectural patterns
  • 1. Workflow architectures
    • 2. Augmented LLM architectures
      • 3. Agentic architectures
        - End-to-end architecture design
        Integration19%- Claude integration mechanisms
        • 1. MCP
          • 2. Agent-to-agent integration
            • 3. API and CLI
              - Enterprise system integration
              - RAG pipeline design
              • 1. Indexing
                • 2. Chunking
                  • 3. Retrieval
                    - Authentication and authorization analysis
                    Developer Productivity & Operational Enablement7%- Claude tooling configuration for teams
                    - AI-assisted developer workflows
                    - Developer enablement
                    - Debugging and operational issue resolution
                    Stakeholder Communication & Lifecycle Management14%- Communicating architectural decisions
                    - Discovery and requirements gathering
                    - Service-level agreements
                    - Solution lifecycle management
                    - Stakeholder management
                    - Architecture documentation

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                    CCAR-P Real Exam Answers - CCAR-P Test Topics Pdf

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                    Anthropic Claude Certified Architect - Professional Sample Questions (Q18-Q23):

                    NEW QUESTION # 18
                    You are running a controlled experiment to compare two prompts and must complete the design steps before executing the experiment.
                    Which two steps must be completed BEFORE running the experiment with random assignment? (Select two.) Each correct answer presents part of the solution.

                    Answer: D,E

                    Explanation:
                    A controlled prompt experiment must begin with a falsifiable hypothesis and a predefined primary metric.
                    Option C prevents the team from examining results first and then selecting whichever metric makes the candidate look successful. The metric might measure task accuracy, rubric score, citation validity, escalation rate, latency, cost, or another criterion directly connected to the hypothesis.
                    Option A determines whether the experiment can detect a practically meaningful improvement. The minimum detectable effect expresses the smallest difference worth acting upon, while the power calculation determines the required sample size. Without this step, the experiment may be too small to detect a real improvement or unnecessarily large and expensive.
                    Random assignment should then distribute representative traffic between the control and candidate prompts while controlling model version, retrieval configuration, tool availability, and other confounding variables.
                    Options B and D occur after data collection. Option E follows the completed analysis and decision. The team should also define significance thresholds, stopping rules, guardrail metrics, exclusion criteria, and treatment of repeated observations before launch.
                    Study Guide references/topics: Prompt A/B testing; hypothesis definition; primary metrics; minimum detectable effect; statistical power; random assignment; decision sequencing.


                    NEW QUESTION # 19
                    A managed agent deployment for claims triage has grown from 6 tools to 34 tools over 18 months as product teams added capabilities. Triage accuracy has declined from 91 percent to 78 percent, and average tool- selection latency has increased by 2.3 seconds. A junior engineer has proposed adding a tool-router agent in front of the current agent to filter the tool list per request.
                    Which two findings should you present to justify capability decomposition before adding the router? (Select two.) Each correct answer presents part of the solution.

                    Answer: A,B

                    Explanation:
                    Four distinct workflow domains indicate that the agent's responsibilities have expanded beyond a cohesive capability boundary. Decomposing the deployment into domain-focused agents reduces each agent's tool- selection space and produces clearer prompts, permissions, evaluations, and ownership. Option E strengthens this conclusion because placing a model-based router before the existing overloaded agent adds latency, cost, and another failure point without correcting the underlying capability sprawl. Overlapping descriptions should be clarified, while unused tools may simply be removed; neither finding alone proves that separate domain agents are required. Public documentation of a pattern is not evidence that it fits this workload. Anthropic emphasizes simple composable patterns, clearly differentiated tools, and empirical evaluation before adding orchestration complexity. Building Effective Agents


                    NEW QUESTION # 20
                    You are compiling continuity practices that span the deployment lifecycle.
                    Which two practices belong on the list? (Select two.)
                    Each correct answer presents a complete solution.

                    Answer: C,E


                    NEW QUESTION # 21
                    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 # 22
                    You are classifying token-management tactics by where each tactic applies in the request lifecycle: "Input Preparation," "Prompt Construction," or "Output Handling."

                    Answer:

                    Explanation:

                    Explanation:
                    * Persist the validated output for downstream consumption and audit - Output Handling
                    * Order prompt sections so cacheable content appears before per-request content - Prompt Construction
                    * Summarize prior conversation history when full history is no longer needed - Input Preparation
                    * Validate the model's structured output against the expected schema - Output Handling
                    * Move stable repeated content into a cacheable prefix at the beginning of the prompt - Prompt Construction
                    * Trim retrieved passages to spans relevant to the user's question - Input Preparation Input preparation determines what evidence and history should enter the request, so summarization and retrieval trimming belong there. Prompt construction determines ordering and cache boundaries; stable repeated instructions must precede dynamic per-request content to maximize cache reuse. Output handling begins after generation and includes schema validation, persistence, auditing, and downstream delivery.
                    Mixing these responsibilities creates inefficient prompts and weak validation boundaries. Anthropic's context guidance emphasizes curating the smallest useful context because unnecessary tokens can reduce recall and accuracy. Prompt caching similarly depends on a stable reusable prefix, while structured outputs or schema validation protect downstream systems from malformed responses. Context windows; prompt caching


                    NEW QUESTION # 23
                    ......

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