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Anthropic CCA-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Context Management & Responsible AI	15%	- Context window optimization 1. Context retention and summarization 2. Token management and truncation strategies - Safety and compliance 1. Constitutional AI principles 2. Refusal handling and risk mitigation
Topic 2: Tool Design & MCP Integration	18%	- Tool definition and best practices 1. Tool descriptions and selection logic 2. Error handling and validation - Model Context Protocol (MCP) 1. MCP server and client setup 2. Tools, resources, and prompts integration
Topic 3: Agentic Architecture & Orchestration	27%	- Anthropic Agent SDK usage 1. Session state and context handling 2. Task spawning and tool management - Agent design patterns 1. Hub-and-spoke multi-agent systems 2. Agent loops and control flow
Topic 4: Prompt Engineering & Structured Output	20%	- Advanced prompting techniques 1. Few-shot and chain-of-thought prompting 2. System prompts and role framing - Structured data generation 1. JSON schema enforcement 2. Output validation and reliability
Topic 5: Claude Code Configuration & Workflows	20%	- CI/CD and development integration 1. Pipeline automation and review 2. Plan mode vs direct execution - Configuration files and structure 1. Custom commands and workflows 2. CLAUDE.md and rules system

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Anthropic Claude Certified Architect Foundations (CCA-F) CCA-F Prüfungsfragen mit Lösungen (Q48-Q53):

48. Frage
A developer needs to perform a monolith-to-microservices migration spanning 45+ files. Why is Plan Mode recommended over Direct Execution for this task?

A. It enables safe codebase exploration and design to determine architectural approaches before making costly file modifications.
B. It uses the Message Batches API to reduce token costs by 50 percent during execution.
C. It bypasses the token limit context window by executing across multiple continuous sessions automatically.
D. It automatically forks the session into a persistent subagent.

Antwort: A

Begründung:
Plan mode is designed for complex tasks involving large-scale changes, multiple valid approaches, and architectural decisions. It enables safe codebase exploration and design before committing to changes, which prevents costly rework compared to Direct Execution.

49. Frage
You are designing a prompt to extract structured data from unstructured documents. You decide to use tool_use with a strict JSON schem a. What specific guarantee does this approach provide?

A. It eliminates JSON syntax errors and guarantees structural compliance, but does not prevent semantic errors.
B. It guarantees the model will not hallucinate missing information.
C. It eliminates the need for validation-retry loops.
D. It guarantees both structural compliance and the semantic correctness of the extracted data.

Antwort: A

Begründung:
Using tool_use with JSON schemas guarantees structural compliance (all required fields present, valid types, syntax correct) but does NOT guarantee semantic correctness (the actual values may still be inaccurate or hallucinated). You still need semantic validation rules.

50. Frage
A PreToolUse hook is configured to intercept and block unauthorized file reads based on restricted file paths. How does the hook properly communicate the specific blocking reason back to Claude so the model can adapt?

A. By overriding the context window entirely with an 'access denied' templated message.
B. By returning an exit code of 2 and writing the explanation to standard error, which is fed back as context.
C. By completely terminating the session and initiating a new one with a revised system prompt.
D. By silently returning an empty JSON array [ ] to the context window.

Antwort: B

Begründung:
In Claude Code and Agent SDK hooks, returning an exit code of 2 (or explicitly returning a 'blocked' flag with a reason string) and providing an explanation feeds the exact error context back to Claude. This allows the model to understand why the action was blocked and adjust its next steps accordingly.

51. Frage
In a coordinator-subagent research system, a web search subagent times out while attempting to retrieve information on a complex topic. How should this failure be propagated back to the coordinator agent?

A. Implement automatic retry logic indefinitely within the subagent, returning a generic search unavailable status if it ultimately fails.
B. Propagate the timeout exception directly to a top-level handler to immediately terminate the entire research workflow.
C. The subagent should catch the timeout and return an empty JSON array L] marked as successful so the workflow continues smoothly.
D. Return structured error context to the coordinator, including the failure type, the attempted query, any partial results, and potential alternative approaches.

Antwort: D

Begründung:
Structured error propagation is critical. Returning structured error context (failure type, what was attempted, partial results) enables the coordinator to make an intelligent recovery decision. Silently suppressing the error by returning an empty result is a severe anti-pattern, and returning generic messages hides context needed for recovery.

52. Frage
Why is providing more than 6 examples in a few-shot prompt generally considered an anti-pattern?

A. It bloats the prompt context window without adding proportional benefit.
B. It forces the Claude API to throw a schema validation error.
C. It causes the model to ignore system prompts.
D. It disables the model's ability to use the tool_use block.

Antwort: A

Begründung:
Providing too many examples (more than 4-6) wastes valuable context window tokens and bloats the prompt without offering a proportional increase in output quality or instruction adherence.

53. Frage
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