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Anthropic CCAR-F Exam Syllabus Topics:

SectionWeightObjectives
Agentic Architecture & Orchestration27%- Agentic architecture patterns
  • 1. Single-agent and multi-agent architectures
    • 2. Planning and execution strategies
      • 3. Workflow design
        • 4. Agent orchestration
          Claude Code Configuration & Workflows20%- Claude Code
          • 1. Development workflows
            • 2. Configuration and project setup
              • 3. Agent skills
                • 4. Code generation and automation
                  Tool Design & MCP Integration18%- Tool integration
                  • 1. Tool selection and safety
                    • 2. Resource and server integration
                      • 3. Model Context Protocol (MCP)
                        • 4. Tool interface design
                          Context Management & Reliability15%- Context handling
                          • 1. Memory strategies
                            • 2. Context window management
                              • 3. Cost and performance optimization
                                • 4. Reliability and evaluation
                                  Prompt Engineering & Structured Output20%- Prompt design
                                  • 1. Output validation
                                    • 2. Few-shot prompting
                                      • 3. Prompt engineering techniques
                                        • 4. Structured output and JSON schemas

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                                          Anthropic Claude Certified Architect - Foundations Sample Questions (Q10-Q15):

                                          NEW QUESTION # 10
                                          Production monitoring shows that the research phase takes longer than expected. Analysis reveals that the coordinator invokes the web-search subagent, waits for its response, and then invokes the document-analysis subagent. These tasks are independent; neither requires the other's output. How should you modify the system to run these subagents concurrently?

                                          Answer: D

                                          Explanation:
                                          Option A exposes both independent invocations in the same assistant turn, allowing the Agent SDK or application tool runner to execute them concurrently. The coordinator can then receive both results together and continue with synthesis only after the independent research branches have completed.
                                          Anthropic's parallel tool-use documentation explains that a response may contain multiple tool-use blocks.
                                          Independent, read-only operations can be executed concurrently to reduce latency, after which all corresponding tool results should be returned together. The term "Agent" is used here because current Claude Agent SDK releases renamed the earlier "Task" tool.
                                          Option B may shorten individual execution but does not eliminate the sequential waiting pattern and could reduce research quality. Option C expresses the desired behavior but does not correct an orchestration implementation that processes only one tool call per turn. Option D introduces unnecessary coordinators, duplicated context, and substantially more complex state management. A single coordinator issuing both independent Agent calls preserves centralized monitoring and result association while removing the avoidable serial dependency. The runtime must process every returned tool call concurrently rather than stopping after the first one.


                                          NEW QUESTION # 11
                                          You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JavaScript Object Notation (JSON) schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
                                          The system routes documents with extraction confidence below 85% to human review. A quarterly audit reveals that 12% of high-confidence extractions (#85%) also contain errors-cases where the model finds plausible-but-incorrect values. Error sources vary: comparison tables showing competitor specs, appendices referencing different product variants, and ambiguous phrasing the model misinterprets. You need a sustainable strategy to catch these high-confidence errors and measure whether improvements reduce the error rate over time.
                                          What approach is most effective?

                                          Answer: C

                                          Explanation:
                                          Stratified random sampling provides both an ongoing quality-control mechanism and an unbiased measurement framework. By reviewing a fixed proportion of high-confidence outputs across meaningful strata-such as document type, field, source format, and business risk-the organization can estimate the residual error rate, compare performance across releases, and discover failure patterns that were not anticipated when existing rules were designed.
                                          Anthropic recommends measurable success criteria and evaluations that mirror the real-world task distribution, including edge cases. Evaluation volume and repeatability are important because improvements must be demonstrated empirically rather than inferred from isolated examples. ( https://docs.anthropic.com/en
                                          /docs/build-with-claude/develop-tests ) A weekly sampling program creates a stable benchmark and allows confidence intervals, trend analysis, regression detection, and error-taxonomy updates.
                                          Option A detects only disagreements between stochastic extractions. Two attempts may produce the same plausible but incorrect answer, so agreement is not equivalent to factual correctness. Option B addresses known patterns but will miss new failure modes and may generate excessive false positives. Option C actually lowers the review threshold in the wrong direction: documents between 70% and 85% would be treated as automated rather than reviewed if the routing rule remains "below threshold," and changing thresholds alone does not measure high-confidence error prevalence.
                                          Official references/topics: Evaluation Design; Representative Test Distributions; Continuous Reliability Measurement; Human-in-the-Loop Sampling.


                                          NEW QUESTION # 12
                                          You are building a customer support resolution agent using the Claude Agent SDK. The agent handles high- ambiguity requests like returns, billing disputes, and account issues. It has access to your backend systems through custom Model Context Protocol (MCP) tools ( get_customer , lookup_order , process_refund , escalate_to_human ). Your target is 80%+ first-contact resolution while knowing when to escalate.
                                          A customer contacts the agent about a warranty claim on a power drill. Resolving this requires multiple sequential tool calls: get_customer to look up their account, lookup_order to find the purchase details, and then either process_refund or escalate_to_human depending on warranty eligibility. You're implementing the agentic loop that orchestrates these steps using the Claude API.
                                          What is the primary mechanism your application uses to determine whether to continue the loop or stop?

                                          Answer: A


                                          NEW QUESTION # 13
                                          In production, final reports frequently contain claims without proper source attribution.
                                          Investigation shows that while the web search and document analysis agents correctly attach citations to their outputs, the synthesis agent loses track of which sources support which conclusions when combining findings. What's the most effective architectural change?

                                          Answer: A

                                          Explanation:
                                          Structured claim-to-source mappings ensure that the synthesis agent can merge findings without losing attribution. By preserving these explicit links through the workflow, the final report can include accurate citations for all claims, maintaining reliability and traceability.


                                          NEW QUESTION # 14
                                          Your pipeline runs:
                                          PROMPT= " You are a code reviewer. "
                                          PROMPT= " $PROMPT Analyze the provided diff "
                                          PROMPT= " $PROMPT for bugs, security issues, "
                                          PROMPT= " $PROMPT and style violations. "
                                          claude -p \
                                          --dangerously-skip-permissions \
                                          --system-prompt " $PROMPT " < diff.txt
                                          The reviews complete and return feedback, but Claude comments only on the piped diff-it never reads surrounding files in the checked-out repository to understand broader context, even when the diff modifies a function called by many other modules. Which change to the invocation will cause Claude to read related repository files while still applying your custom review instructions?

                                          Answer: D

                                          Explanation:
                                          The --system-prompt flag replaces Claude Code's complete default system prompt. Although this does not technically remove the available tools, it discards the default coding-agent guidance that tells Claude how to gather repository context, navigate code, use tools, and verify findings. The replacement prompt says only to analyze the supplied diff, so the observed diff-only behaviour is consistent with the configured instructions.
                                          Option B preserves the standard Claude Code identity and tool guidance while adding the custom review criteria. The Claude Code CLI reference states that --append-system-prompt appends instructions to the default prompt, whereas --system-prompt replaces it. Anthropic specifically recommends appending when Claude should remain a coding assistant that follows additional per-invocation rules.
                                          Option A is incorrect because print mode does not inherently disable Read, Glob, or Grep; --allowedTools restricts or pre-authorizes tools but does not restore overwritten behavioural guidance. Option C is incorrect because replacement system prompts remain compatible with tools, although the replacement must supply suitable tool instructions. Option D changes where the diff appears but does not restore Claude Code's default code-navigation behaviour. Appending the review instructions directly addresses the configuration error.


                                          NEW QUESTION # 15
                                          ......

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