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Anthropic CCAR-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Prompt Engineering & Structured Output	20%	- Validation, parsing and retry loop strategies - Explicit criteria definition and few-shot prompting - JSON schema design and structured output enforcement - System prompt design and persona alignment
Topic 2: Claude Code Configuration & Workflows	20%	- Path-specific rules and .claude/rules/ configuration - Custom slash commands and plan mode vs direct execution - CI/CD integration and non-interactive mode parameters - Hooks vs advisory instructions - CLAUDE.md hierarchy, precedence and @import rules
Topic 3: Agentic Architecture & Orchestration	27%	- Multi-agent patterns: coordinator-subagent and hub-and-spoke - Agentic loop design and stop_reason handling - Error recovery, guardrails and safety patterns - Task decomposition and dynamic subagent selection - Session state management and workflow enforcement
Topic 4: Tool Design & MCP Integration	18%	- Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - MCP tool, resource and prompt implementation - Tool schema design and interface boundaries - Error handling and tool response formatting - Tool distribution and permission controls
Topic 5: Context Management & Reliability	15%	- Token budget management and cost control - Context pruning and summarization strategies - Idempotency, consistency and failure resilience - Context window optimization and prioritization

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Anthropic Claude Certified Architect - Foundations Sample Questions (Q189-Q194):

NEW QUESTION # 189
You are building a multi-agent research system using the Claude Agent SDK. A coordinator agent delegates to specialized subagents: one searches the web, one analyzes documents, one synthesizes findings, and one generates reports. The system researches topics and produces comprehensive, cited reports.
After the web-search agent finds 25 sources containing 120,000 tokens of raw content, the document-analysis agent extracts 15,000 tokens of key insights, and the synthesis agent produces a coherent 3,000-token narrative draft, the coordinator must pass context to the report-generation agent for the final output with proper source citations.
What context-passing strategy provides the best balance of completeness and efficiency?

A. Pass the synthesis draft together with a structured source index that maps key claims to their source URLs and relevant excerpts.
B. Pass only the synthesis draft and have a separate post-processing pipeline match claims to sources and insert citations after the report is generated.
C. Pass the full accumulated context from all prior agents.
D. Pass a condensed summary of all prior stages that preserves the main findings and attributes them to sources by name only.

Answer: A

Explanation:
Option A gives the report generator the finished narrative context plus the evidence required to verify and cite its claims. The 3,000-token synthesis draft supplies organization and conclusions, while the source index preserves URLs, supporting excerpts, and claim-to-source mappings without transferring 120,000 tokens of raw search material. Anthropic's effective context-engineering guidance recommends curating the smallest high-signal context and having subagents return condensed, distilled results rather than flooding later stages with their complete working history. Anthropic's citation guidance likewise depends on retaining specific supporting passages and source locations. Option B is complete but inefficient and introduces context pollution that can reduce attention to relevant evidence. Option C separates citation insertion from the reasoning that produced the claims, making incorrect or unsupported matches more likely. Option D retains source names but discards the passages needed to demonstrate that each source actually supports the associated claim. The structured index should use stable source IDs and include only the excerpts necessary for the report's factual claims, with validation that every cited claim maps to at least one evidence entry.

NEW QUESTION # 190
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?

A. You manually set the tool_choice parameter to "none" after the final expected tool call to force Claude to stop requesting tools.
B. You check the stop_reason field in each API response - the loop continues while it equals
"tool_use" and exits when it changes to "end_turn" or another terminal value.
C. You track the number of tool calls made and exit the loop once a preconfigured maximum is reached.
D. You check whether Claude's response contains a text content block - if text is present, the agent has produced its final answer and the loop should exit.

Answer: B

Explanation:
Continue the agentic loop when stop_reason is tool_use, execute the requested tool, and return its result to Claude. Exit when Claude returns a terminal reason such as end_turn; other reasons like max_tokens require separate handling.

NEW QUESTION # 191
Your control_device tool manages smart home devices through external APIs. When a device doesn't respond within the timeout period, the tool returns an error. Production logs show that the agent simply tells users "the device is not responding" without offering helpful next steps. Which error response structure would best enable the agent to provide useful follow-up?

A. Set is_error: true with a brief "Device offline" message and provide a separate tool the agent can call to retrieve context-specific troubleshooting suggestions.
B. Set is_error: true with a structured technical error containing the device ID, timeout duration, and raw API response code for debugging purposes.
C. Set is_error: false with an optimistic message indicating the command was dispatched successfully but device acknowledgment is still pending.
D. Set is_error: true with a message explaining the likely cause and suggesting troubleshooting steps the agent can offer the user.

Answer: D

Explanation:
Including the likely cause and actionable troubleshooting steps in the error response allows the agent to communicate helpful guidance to the user, rather than just reporting the failure. This improves user experience and supports effective problem resolution.

NEW QUESTION # 192
You're implementing the escalation logic for when the agent should call escalate_to_human. Your team proposes four different approaches for triggering escalation. Which approach will most reliably identify cases that genuinely require human intervention?

A. Instruct the agent to escalate when the customer requests a human, when the issue requires policy exceptions, or when the agent cannot make meaningful progress.
B. Implement sentiment analysis that monitors for frustration indicators (negative language, repeated questions, exclamation marks) and trigger escalation when the frustration score exceeds a configured threshold.
C. Build a rules engine that maps specific issue types, customer segments, and product categories to escalation decisions, removing the need for model judgment calls.
D. Configure the agent to escalate after three consecutive tool calls that fail to resolve the customer's stated issue, ensuring a reasonable attempt before involving a human.

Answer: A

Explanation:
Escalation should be based on clear operational triggers: explicit user request, authority or policy limits, and lack of meaningful progress. This combines user intent, business constraints, and task outcome signals, making escalation more reliable than relying only on sentiment, fixed retry counts, or rigid rules.

NEW QUESTION # 193
You are using Claude Code to accelerate software development. Your team uses it for code generation, refactoring, debugging, and documentation. You need to integrate it into your development workflow with custom slash commands, CLAUDE.md configurations, and understand when to use plan mode vs direct execution.
A security audit requires updating your authentication library from v2 to v3. The migration guide documents breaking changes: authenticate() now returns a Promise instead of accepting a callback, the User type has restructured fields, and three deprecated methods were removed.
Grep shows the library is imported in 45 files across several modules.
What's the most effective approach?

A. Create a custom slash command encapsulating the migration transformations, then execute it against each file without prior codebase exploration.
B. Paste the migration guide's breaking changes into your prompt and use direct execution to update all usages across the 45 files.
C. Update the dependency version, run the test suite, and use Claude Code to fix each failure as it appears.
D. Enter plan mode to explore library usage across modules, map affected code paths, then create a migration strategy before implementing.

Answer: D

Explanation:
This migration is a high-impact, cross-module change with several independent breaking changes. Before editing, Claude must determine how the callback-based API is currently used, where the restructured User fields propagate, whether deprecated methods are wrapped or re- exported, and which downstream modules depend on the affected behavior. Plan mode is therefore the correct starting point because it allows Claude to inspect the repository, identify affected code paths, and produce an implementation strategy without modifying source files.
Anthropic recommends separating exploration and planning from implementation when the approach is uncertain, the change affects multiple files, or the developer is unfamiliar with the impacted code. Direct execution is better reserved for small, clearly scoped changes that can be described as a simple diff.

NEW QUESTION # 194
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