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Anthropic CCAR-F Exam Syllabus Topics:

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

Anthropic Claude Certified Architect - Foundations Sample Questions:

1. The automated review consistently flags patterns your team uses intentionally-force-unwrapping optionals in test files, using large coordinator classes that follow your established architecture, and importing internally maintained modules marked as deprecated in the public SDK. Developers are dismissing approximately 30% of all findings as project-specific false positives. Which approach prevents the model from generating these findings in the first place by supplying the project's conventions as persistent context during every review?

A) Build post-processing keyword filters that suppress findings containing terms such as "force unwrap,"
"large class," or "deprecated import" before results reach developers.
B) Configure the review to analyze only the changed lines in the diff without surrounding file context, reducing the amount of code the model evaluates during each review.
C) Document the team's accepted patterns and intentional conventions in the project's CLAUDE.md file so the model receives this context during every review.
D) Have developers add inline suppression comments at flagged lines and preprocess diffs to exclude suppressed lines before sending code to the model.


2. 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.
The coordinator agent has AgentDefinition objects configured for all four specialized subagents, each with appropriate descriptions, prompts, and tool restrictions. During testing, you notice that the coordinator correctly reasons about when to delegate-it generates messages such as, "I'll ask the web-search agent to find sources on this topic"-but no subagent execution ever occurs. The coordinator then proceeds as if the delegation happened and continues with incomplete information. Logs show no errors.
What is the most likely cause?

A) The AgentDefinition objects are configured correctly, but the coordinator's system prompt does not explicitly list the available subagent types, preventing the model from knowing that they can be invoked.
B) The coordinator's allowedTools configuration does not include Agent-formerly named Task-so it cannot invoke the tool required to spawn subagents.
C) Subagent context isolation means task descriptions from the coordinator do not automatically reach subagents; you must configure explicit context forwarding in ClaudeAgentOptions.
D) The coordinator's max_tokens setting is too low, causing the subagent tool invocation to be truncated before the subagent type can be specified.


3. 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.
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?

A) Structure the coordinator to emit both Agent tool calls-formerly Task tool calls-in a single response rather than across separate conversation turns.
B) Create an asynchronous orchestration layer that starts separate coordinator-subagent pairs in parallel and aggregates their results.
C) Add instructions explaining the performance benefits of parallel execution and requesting that the coordinator invoke both subagents simultaneously.
D) Switch both subagents to a Haiku-tier model instead of Sonnet to reduce their individual execution times.


4. You are building developer productivity tools using the Claude Agent SDK. The agent helps engineers explore unfamiliar codebases, understand legacy systems, generate boilerplate code, and automate repetitive tasks. It uses the built-in tools (Read, Write, Bash, Grep, Glob) and integrates with Model Context Protocol (MCP) servers.
Your codebase exploration tool stores session IDs to allow engineers to continue investigations across work sessions. An engineer spent an hour yesterday analyzing a legacy authentication module, building context about its architecture and dependencies. They want to continue today. The session ID is valid, but version control shows 3 of the 12 files the agent previously read were modified overnight by a teammate's merge.
What approach best balances efficiency and accuracy?

A) Resume the session without informing the agent about the changed files
B) Resume the session and inform the agent which specific files changed for targeted re-analysis
C) Start a fresh session to ensure the agent works with current codebase state without stale assumptions
D) Resume the session and immediately have the agent re-read all 12 previously analyzed files


5. 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.
Testing reveals that when source documents are missing certain specifications, the model fabricates plausible- sounding values to satisfy your schema's required fields. For example, a document mentioning only dimensions receives a fabricated "weight: 2.3 kg" in the extraction output.
What schema design change most effectively addresses this hallucination behavior?

A) Change fields that may not exist in source documents from required to optional, allowing the model to omit them.
B) Implement semantic validation that verifies each extracted value appears in or can be inferred from the source document text.
C) Add a "confidence" field alongside each specification where the model self-reports its certainty, then filter out low-confidence extractions.
D) Add explicit instructions to the prompt stating "only extract information explicitly stated in the document; use placeholder text for missing values."


Solutions:

Question # 1
Answer: C
Question # 2
Answer: B
Question # 3
Answer: A
Question # 4
Answer: B
Question # 5
Answer: A

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