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Anthropic CCAR-F Exam Syllabus Topics:

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

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Anthropic Claude Certified Architect - Foundations Sample Questions (Q127-Q132):

NEW QUESTION # 127
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 and document-analysis subagents complete their tasks, the coordinator needs to spawn the synthesis subagent to synthesize the findings.
What is the correct approach for providing the synthesis subagent with the information it needs?

A. Provide the subagent with tool definitions that allow it to request outputs from other subagents through callbacks.
B. Spawn the subagent with only a brief task description, relying on automatic context inheritance from the coordinator.
C. Include the complete findings from both subagents directly in the synthesis subagent's prompt.
D. Pass reference identifiers and configure the subagent with read access to a shared memory store where the other subagents deposited their results.

Answer: C

Explanation:
Option B follows the Claude Agent SDK's subagent context model. A normal subagent begins with a fresh context window and does not inherit the parent agent's conversation history or prior tool results. Anthropic's Subagents in the SDK documentation states that the information passed from parent to subagent is the spawning tool's prompt string; required file paths, decisions, errors, or findings must therefore be included in that prompt. In this scenario, the coordinator should supply both agents' relevant findings, source metadata, and explicit synthesis instructions. "Complete findings" means the full required result artifacts, not every intermediate search trace. Option C incorrectly assumes automatic context inheritance. Option A introduces callbacks that are neither necessary nor the standard handoff mechanism. Option D can be a valid custom architecture when a shared store has deliberately been implemented, but the question does not establish such infrastructure, and identifiers alone do not give the subagent information. The prompt should use clear sections or structured objects to distinguish web findings, document findings, sources, unresolved conflicts, and expected output. This preserves context isolation while providing everything the synthesis task actually requires.

NEW QUESTION # 128
A customer returns 4 hours after the initial session about the same billing dispute. The previous
32-turn session contains lookup_order results showing "Status: PENDING, Expected resolution:
24-48 hours." In testing, you observe that when resuming sessions with stale tool results, the agent often references the outdated data in responses (e.g., "I see your refund is still being processed") even after subsequent fresh tool calls return different information. What approach most reliably handles returning customers?

A. Start a new session, inject a structured summary of the previous interaction (issue type, actions taken, resolution status), then make fresh tool calls before engaging.
B. Resume with full history and configure the agent to automatically re-call all previously-used tools at session start to ensure data freshness.
C. Resume with full history but filter out previous tool_result messages before resuming, keeping only the human/assistant turns so the agent must re-fetch needed data.
D. Resume with full history and add a system prompt instruction telling the agent to always prefer the most recent tool results when multiple calls to the same tool exist in context.

Answer: A

Explanation:
Providing a concise structured summary preserves essential context from the previous session while ensuring the agent retrieves fresh, up-to-date tool results. This prevents reliance on stale data and allows accurate, current responses to the returning customer.

NEW QUESTION # 129
You have configured the system so that all four subagents have access to the complete set of 18 tools. During testing, agents frequently call tools outside their specialization-the synthesis agent attempts web searches, and the report generator tries to analyze documents. What is the primary cause of this poor tool-selection behavior?

A. Choosing from 18 tools instead of four or five relevant tools increases decision complexity beyond reliable selection thresholds.
B. The tool definitions consume too much context-window space, leaving insufficient room for task content.
C. The agents' role descriptions in their system prompts conflict with having access to tools outside those roles.
D. The coordinator cannot track which capabilities each subagent has, leading to misrouted tasks.

Answer: C

Explanation:
Option A identifies the direct configuration contradiction. Each system prompt defines a specialized responsibility, but the corresponding tool allowlist presents unrelated actions as available capabilities. The synthesis agent may therefore interpret web searching as an acceptable way to fill evidence gaps, while the report generator may perform document analysis instead of limiting itself to report production.
Anthropic's subagent documentation defines subagents as focused workers with their own prompts and specific tool access. Its tool-definition guidance also recommends reducing selection ambiguity by presenting clear, relevant tools. The appropriate correction is least-capability configuration: expose search tools to the search agent, document tools to the analyzer, synthesis utilities to the synthesizer, and formatting or output tools to the report generator.
Option B may affect token efficiency but does not explain the role-specific misuse pattern. Option C concerns delegation, whereas the improper calls occur after successful delegation. Option D is tempting, but Anthropic does not define four or five tools as a universal reliability threshold; 18 tools alone does not prove threshold failure. The decisive defect is misalignment between declared roles and permitted capabilities.

NEW QUESTION # 130
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, and Glob--and integrates with Model Context Protocol (MCP) servers.
An engineer who recently joined the team asks the agent to explain the authentication and authorization architecture before making security improvements. The codebase contains more than 800 files across multiple services.
What exploration strategy will most effectively build understanding while respecting context limits?

A. Read all CLAUDE.md and README files first, and then ask the engineer to identify the 10?5 most important authentication files.
B. Use Grep to locate authentication entry points, read those files, and then follow imports and function calls incrementally to map the authentication flow.
C. Launch parallel subagents to explore every service simultaneously, and then synthesize their findings into an architectural overview.
D. Read all files containing auth, login, permission, or token in their filenames or contents.

Answer: B

Explanation:
Option D builds an evidence-based architectural map without indiscriminately loading hundreds of files. Authentication entry points may include route handlers, middleware registration, token- validation functions, session constructors, or identity-provider callbacks. After Grep identifies those anchors, targeted Read operations can establish their responsibilities, while imports, call sites, and configuration references reveal the downstream authorization flow. Anthropic's large- codebase guidance recommends scoping Claude to the portion of a repository touched by the task because unrelated instructions and file reads consume tokens and degrade performance. Its common workflows guidance similarly recommends beginning broadly and narrowing into specific components.

NEW QUESTION # 131
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.
An engineer asks the agent to find all callers of a function before removing it. The function is defined in a core library but is also exposed through wrapper modules that rename the function for domain- specific use (e.g., calculateTax in the library becomes computeOrderTax in the orders module).
What exploration strategy will most reliably identify all callers?

A. Search for the function name in project documentation to understand intended usage patterns and navigate to documented integration points.
B. Use Grep to find all files that import from the library or wrapper modules, then read each file to check whether it uses the function.
C. Use Grep to search for the function's original name across the codebase.
D. Read the library and wrapper modules to identify all exposed names for the function, then Grep for each name across the codebase.

Answer: D

Explanation:
The search must first establish the function's complete naming surface. Reading the core definition and every wrapper or re-export reveals aliases such as computeOrderTax, names introduced through intermediate modules, and potentially different import paths. Once those names are known, the agent can search the entire codebase for each exposed identifier and inspect the resulting call sites.
Claude Code's Grep tool searches patterns inside file contents, whereas Read provides the surrounding implementation needed to determine whether a wrapper delegates to, renames, or conditionally invokes the original function. These tools are complementary: Read establishes semantic identity; Grep provides broad reference discovery.

NEW QUESTION # 132
......

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