Anthropic CCAR-F PDF Demo - CCAR-F Zertifizierungsfragen

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Anthropic CCAR-F Exam Syllabus Topics:

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

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Anthropic Claude Certified Architect - Foundations CCAR-F Prüfungsfragen mit Lösungen (Q80-Q85):

80. Frage
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. Subagent context isolation means task descriptions from the coordinator do not automatically reach subagents; you must configure explicit context forwarding in ClaudeAgentOptions.
B. The coordinator's allowedTools configuration does not include Agent-formerly named Task-so it cannot invoke the tool required to spawn subagents.
C. The coordinator's max_tokens setting is too low, causing the subagent tool invocation to be truncated before the subagent type can be specified.
D. 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.

Antwort: B

Begründung:
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.

81. Frage
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 submits two requests:
- Request A: "Rename the getUserData function to fetchUserProfile
everywhere it's used."
- Request B: "Improve error handling throughout the data processing
module - add try/catch blocks, meaningful error messages, and ensure
failures don't silently corrupt data."
For which request does specifying an explicit multi-phase workflow (such as analyze -> propose
-> implement with review) most improve outcome quality?

A. Request B, the error handling task
B. Request A, the function rename task
C. Neither request benefits significantly
D. Both requests benefit equally

Antwort: A

Begründung:
Improving error handling is broad and ambiguous, requiring analysis of failure paths, design decisions, and review before implementation. A simple function rename is deterministic and can usually be executed directly.

82. Frage
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.
You've configured your Claude agent with three MCP servers: one for git operations, one for Jira ticket management, and one for documentation search.
When a user asks the agent to "create a branch for JIRA-123 and add documentation links to the ticket," how does the agent access tools across these servers?

A. The agent automatically selects the most relevant server based on the request and loads only that server' s tools.
B. You must specify which MCP server to use for each turn, and the agent can only access one server's tools at a time.
C. The agent queries each server sequentially to determine which handles each tool, routing calls based on tool name prefixes.
D. Tools from all configured MCP servers are discovered at connection time and available simultaneously to the agent.

Antwort: D

Begründung:
MCP allows a Claude application to connect to multiple servers and expose their enabled tools within the same agentic interaction. Anthropic's MCP connector documentation explicitly supports connecting to multiple MCP servers in one request. Once connected, Claude can invoke a server's tool when the user's request corresponds to the capability described by that tool. ( https://docs.anthropic.com/en/docs/agents-and- tools/mcp-connector ) In this scenario, the git server can provide the branch-creation operation, the documentation server can locate the relevant links, and the Jira server can update ticket JIRA-123. The agent can coordinate these capabilities without requiring a separate conversational turn that restricts it to only one server. Tool descriptions and schemas tell Claude what each operation does and what inputs it requires.
Option B incorrectly describes sequential capability discovery. Server identity and tool definitions are established through the MCP configuration rather than discovered by querying every server for each operation. Option C is incorrect because connecting several servers does not inherently restrict the agent to a single selected server. Option D imposes a one-server-per-turn limitation that MCP does not require.
Access can still be controlled through allowlists, denylists, permissions, and per-tool configuration. Those controls restrict which tools are enabled; they do not change the fundamental ability to make tools from multiple configured servers available together.
Official references/topics: MCP Server Configuration, Multi-Server Tool Access, Tool Discovery, MCP Tool Permissions.

83. Frage
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 via callbacks
B. Spawn the subagent with only a brief task description, relying on automatic context inheritance from the coordinator
C. Pass reference identifiers and configure the subagent with read access to a shared memory store where other subagents deposited their results
D. Include the complete findings from both subagents directly in the synthesis subagent's prompt

Antwort: D

Begründung:
The synthesis subagent requires the actual content from prior subagents to perform its task.
Including the complete findings in its prompt ensures it has the necessary context to accurately synthesize information without relying on indirect references or external state.

84. Frage
A developer wants Claude to explain every reasoning step internally before answering. Why might this request be inappropriate?

A. Claude requires GPU acceleration.
B. JSON cannot contain reasoning.
C. Internal reasoning should not be relied upon as an application feature.
D. Temperature prevents explanations.

Antwort: C

Begründung:
Applications should evaluate Claude based on observable outputs rather than expecting access to internal reasoning processes. Prompting should request useful explanations or evidence instead of relying on hidden reasoning mechanisms.

85. Frage
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