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Anthropic CCAR-F Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Claude Code Configuration & Workflows20%- CI/CD integration and non-interactive mode parameters
- CLAUDE.md hierarchy, precedence and @import rules
- Path-specific rules and .claude/rules/ configuration
- Hooks vs advisory instructions
- Custom slash commands and plan mode vs direct execution
Topic 2: Agentic Architecture & Orchestration27%- Agentic loop design and stop_reason handling
- Multi-agent patterns: coordinator-subagent and hub-and-spoke
- Task decomposition and dynamic subagent selection
- Session state management and workflow enforcement
- Error recovery, guardrails and safety patterns
Topic 3: Prompt Engineering & Structured Output20%- System prompt design and persona alignment
- Explicit criteria definition and few-shot prompting
- Validation, parsing and retry loop strategies
- JSON schema design and structured output enforcement
Topic 4: Tool Design & MCP Integration18%- MCP tool, resource and prompt implementation
- Tool distribution and permission controls
- Model Context Protocol (MCP) architecture and JSON-RPC 2.0
- Tool schema design and interface boundaries
- Error handling and tool response formatting
Topic 5: Context Management & Reliability15%- Context window optimization and prioritization
- Context pruning and summarization strategies
- Token budget management and cost control
- Idempotency, consistency and failure resilience

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Anthropic Claude Certified Architect - Foundations Sample Questions (Q161-Q166):

NEW QUESTION # 161
Your expense reimbursement agent processes employee requests using a
process_reimbursement tool. Company policy requires that reimbursements above $500 must be approved by a manager before funds are disbursed. The agent handles hundreds of requests daily, and you need the threshold enforcement to be tamper-proof regardless of how the agent is prompted. Which design ensures the $500 approval threshold cannot be bypassed?

Answer: B

Explanation:
Enforcing the approval threshold within the tool itself makes it tamper-proof and independent of agent behavior or prompts. The tool controls disbursement and ensures manager approval is required for amounts over $500, preventing accidental or intentional bypass.


NEW QUESTION # 162
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.
After expanding the agent's MCP tools with delivery-specific capabilities ( check_delivery_status , contact_driver , issue_credit , apply_promo_code , update_delivery_address , reschedule delivery ), the total tool count has grown from 4 to 10. Your evaluation suite shows tool selection accuracy has dropped from 88% to 71%. Log analysis reveals the majority of errors involve the agent selecting between semantically overlapping tools - calling issue_credit when process_refund was correct, and calling check_delivery_status when lookup_order already returns the needed data. Which approach structurally eliminates the semantic overlap identified in the logs as the error source?

Answer: C

Explanation:
Merging issue_credit and process_refund into one compensation tool, and folding delivery-status lookup into lookup_order, removes competing tools with overlapping purposes. This directly eliminates the ambiguity causing incorrect tool selection, rather than merely hiding tools, routing between agents, or adding examples.


NEW QUESTION # 163
Your agent uses three tools: get_property_details(property_id) returns data including street address, get_price_history(property_id) returns historical pricing, and get_neighborhood_info(address) returns area statistics. You observe that get_neighborhood_info always requires get_property_details first just to extract the address, even when users specify the property by ID. This creates unnecessary latency and failure coupling - if the first call fails, the neighborhood request also fails. What tool design change best addresses this?

Answer: A

Explanation:
Allowing the neighborhood tool to accept the same identifier the user provides removes the need for the agent to chain through another tool just to extract an intermediate value. Resolving the address internally reduces latency, avoids unnecessary dependency on a prior tool call, and makes the workflow more reliable.


NEW QUESTION # 164
Your automated review CI jobs take 18 seconds to initialize before Claude begins analyzing code. Profiling reveals that the delay comes from automatically discovering hooks, MCP servers, plugins, skills, and multiple nested CLAUDE.md files throughout your monorepo. You need to reduce startup time while ensuring that reviews still enforce your team's coding standards, which are documented in the root-level CLAUDE.md file.
What is the most effective approach?

Answer: B

Explanation:
Claude Code's --bare option is specifically designed for faster scripted execution. It skips automatic discovery of CLAUDE.md files, hooks, skills, plugins, MCP servers, and auto-memory while retaining essential built-in capabilities such as Bash, file reading, and file editing. Because --bare also prevents automatic loading of the root CLAUDE.md, the required standards must be added explicitly.
Option C accomplishes both objectives. --append-system-prompt-file ./CLAUDE.md loads the project standards into the current invocation while preserving Claude Code's default coding-agent system prompt and tool-use guidance. According to the official Claude Code CLI reference , append flags add file contents to the default system prompt, whereas replacement flags discard that default guidance.
Option A could work functionally but duplicates the standards in every command and creates configuration drift. Option B replaces the complete default prompt, removing useful coding, safety, and tool-use instructions. Option D improves prompt-cache reuse across different machines, but it does not disable hooks, plugins, skills, MCP servers, or CLAUDE.md discovery and therefore does not directly address the measured initialization delay.


NEW QUESTION # 165
Why is evaluation important after deploying a Claude application?

Answer: B

Explanation:
Continuous evaluation allows organizations to measure accuracy, safety, consistency, and user satisfaction. Regular testing identifies regressions and supports iterative improvement of prompts, retrieval pipelines, and application workflows.


NEW QUESTION # 166
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