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| Section | Weight | Objectives |
|---|---|---|
| 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 |
| Tool Design & MCP Integration | 18% | - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - Tool distribution and permission controls - Tool schema design and interface boundaries - Error handling and tool response formatting - MCP tool, resource and prompt implementation |
| Agentic Architecture & Orchestration | 27% | - Task decomposition and dynamic subagent selection - Error recovery, guardrails and safety patterns - Session state management and workflow enforcement - Multi-agent patterns: coordinator-subagent and hub-and-spoke - Agentic loop design and stop_reason handling |
| Claude Code Configuration & Workflows | 20% | - Path-specific rules and .claude/rules/ configuration - Custom slash commands and plan mode vs direct execution - Hooks vs advisory instructions - CI/CD integration and non-interactive mode parameters - CLAUDE.md hierarchy, precedence and @import rules |
| Prompt Engineering & Structured Output | 20% | - JSON schema design and structured output enforcement - System prompt design and persona alignment - Explicit criteria definition and few-shot prompting - Validation, parsing and retry loop strategies |
>> Anthropic CCAR-F Exam Answers <<
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NEW QUESTION # 141
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.
You're tasked with adding real-time updates to the application. This could be implemented using WebSockets, Server-Sent Events, or polling, each with different complexity, browser support, and infrastructure requirements.
What's the most effective way to begin this task?
Answer: D
Explanation:
The implementation approach is unresolved and carries architectural consequences. WebSockets, Server-Sent Events, and polling differ in connection lifecycle, bidirectional communication, proxy compatibility, scaling requirements, deployment topology, reconnection behavior, and operational complexity. Claude should therefore inspect the existing application and infrastructure before modifying code.
Anthropic recommends separating exploration and planning from implementation when the approach is uncertain, the work affects multiple files, or the developer is unfamiliar with the relevant architecture. In plan mode, Claude can read files and analyze the system without making changes, produce a detailed proposal, and allow the developer or team to review the plan before execution. ( https://code.claude.com/docs/en/best- practices ) Option A commits to polling before determining whether its latency and load characteristics meet the requirement. Option B delegates a consequential architectural decision and immediate implementation to a single execution step without a review checkpoint. Option D prematurely selects the most infrastructure- sensitive option and assumes later refactoring will be inexpensive.
Option C creates the correct sequence: examine the frontend and backend architecture, document requirements, compare alternatives, identify infrastructure constraints, obtain approval, and only then implement the selected design with appropriate tests and observability.
Official references/topics: Plan mode, explore-plan-implement workflow, architectural trade-off analysis, review checkpoints.
NEW QUESTION # 142
A customer sends: "This is frustrating. I've explained my issue twice and nothing is being resolved. I want to talk to a real person NOW." The agent has not yet called any tools to investigate the customer's account.
What should the agent do?
Answer: B
Explanation:
Option C respects the customer's explicit request for human assistance. The agent should not attempt to retain the interaction, gather additional account information, or require the customer to repeat the request. Because the escalation decision has already been made by the customer, further autonomous investigation would create unnecessary delay and disregard clear human direction.
Anthropic's trustworthy-agent framework emphasizes maintaining meaningful human control over agent autonomy. Anthropic's customer-support implementation guidance also recommends defining interaction branches and measuring escalation efficiency as part of the system's success criteria.
Option A deliberately delays escalation despite an unambiguous request. Option B performs unnecessary tool calls and may expose or retrieve account information that is not required before transferring the conversation.
Option D similarly introduces another conversational barrier. Since the current exchange is short and contains no tool-generated evidence, passing the available conversation history gives the human agent the immediate context-the customer's frustration, repeated unsuccessful attempts, and explicit transfer request-without pretending that account investigation has occurred.
NEW QUESTION # 143
Your multi-agent research pipeline crashed after processing 12 of 28 documents. The web-search agent had identified relevant sources, the document analyzer had partially completed extraction, and the synthesizer had begun identifying patterns. You need to resume processing without repeating work or losing fidelity in the prior findings. What state-management approach best balances information fidelity with context efficiency when restoring agent state?
Answer: A
Explanation:
Option A combines lossless persistence with selective restoration. Each agent should checkpoint structured records containing completed document identifiers, extracted findings, source provenance, unresolved work, verification status, and schema version. A coordinator-level manifest can record which stages completed and where their artifacts are stored. On recovery, the coordinator skips completed documents and injects only the state required by each restarted agent.
Anthropic's effective context-engineering guidance recommends structured notes stored outside the active context window and reloaded when needed. This preserves critical dependencies and progress without replaying an entire execution history.
Option B fragments recovery ownership across agents and makes cross-agent consistency, versioning, and dependency validation difficult. Option C preserves extensive logs but wastes context on redundant tool calls, obsolete instructions, and intermediate messages. Option D is useful for relevance retrieval but semantic search can omit exact details and should not serve as the authoritative recovery record. Structured checkpoints and a validated manifest provide deterministic recovery, efficient context reconstruction, auditability, and precise provenance. Writes should be atomic so partially produced exports are never mistaken for completed work.
NEW QUESTION # 144
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?
Answer: B
Explanation:
Option C matches the distinction between reasoning about delegation and executing it. Defining subagents makes their descriptions available for selection, but the coordinator must still invoke the SDK's subagent- spawning tool. Current Claude Agent SDK documentation calls this the Agent tool; Task was its earlier name and remains relevant to older SDK configurations. Anthropic's Subagents in the SDK documentation instructs developers to include Agent in allowedTools so subagent invocations are approved automatically. Without that permission, an invocation can fall through to a permission callback or be denied under a non-interactive permission mode. Option A is unlikely because the configured subagent descriptions already tell Claude when each agent should be selected, although explicit prompting can improve invocation reliability. Option B misstates context isolation: context must be included in the spawning prompt, but that issue occurs after an invocation is attempted and does not explain the absence of all subagent executions. Option D would normally produce truncation evidence or incomplete output rather than consistent verbal promises with no tool call. The configuration should therefore permit Agent, explicitly request delegation where necessary, and log subagent invocation events.
NEW QUESTION # 145
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.
A developer asks the agent to investigate why a specific API endpoint intermittently returns 500 errors. The codebase has 200+ files and the developer doesn't know which components are involved. The agent must trace the error through routing, middleware, business logic, and database layers. What task decomposition approach would be most effective?
Answer: A
Explanation:
The relevant components and failure path are initially unknown, so the investigation should adapt as evidence emerges. This matches the orchestrator-workers pattern, where an agent dynamically decomposes a complex task rather than following a fixed sequence or launching broad parallel work prematurely.
NEW QUESTION # 146
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