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Anthropic CCA-F Exam Syllabus Topics:

Section	Weight	Objectives
Claude Code Configuration & Workflows	20%	- CI/CD and workflow integration 1. Automated pipeline integration 2. Command vs plan mode usage - Claude Code configuration 1. Project-level vs user-level configuration 2. CLAUDE.md hierarchy and scoping
Agentic Architecture & Orchestration	27%	- Agentic loops & lifecycle design 1. Tool use loop execution (request → tool_use → result → next step) 2. Stop reason handling and control flow - Multi-agent orchestration 1. Coordinator / sub-agent patterns 2. Parallel task decomposition
Tool Design & MCP Integration	18%	- Model Context Protocol (MCP) 1. MCP server integration patterns 2. Tool selection and routing strategies - Tool interface design 1. Structured tool input/output schemas 2. Clear tool descriptions and boundaries
Prompt Engineering & Structured Output	20%	- Tool-augmented prompting 1. Tool-use driven reasoning patterns 2. Validation and retry loops - Instruction design 1. Deterministic output formatting (e.g., JSON) 2. Few-shot prompting strategies
Context Management & Reliability	15%	- Context window optimization 1. Managing long conversation degradation 2. "Lost in the middle" mitigation strategies - System reliability 1. Escalation strategies (fail vs retry vs human) 2. Error propagation handling

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Anthropic Claude Certified Architect Foundations (CCA-F) CCA-F Prüfungsfragen mit Lösungen (Q30-Q35):

30. Frage
You are implementing a validation-retry loop for a structured data extraction prompt. The extraction fails a business logic validation (e.g., line items do not sum to the total). Which techniques represent best practices for the follow-up retry prompt? Choose 2 correct answers.

A. Append specific validation errors, detailing which field failed and the nature of the discrepancy.
B. Include the original document and the failed extraction in the retry prompt to guide self-correction.
C. Increase the model temperature to 1.0 so it thinks of a different answer.
D. Return a generic error message like There were errors in your extraction. Please try again.

Antwort: A,B

Begründung:
A successful validation-retry loop requires appending specific error details (not generic messages) to the prompt and supplying both the source document and the failed extraction. This provides the exact context the model needs to correct its semantic mistake.

31. Frage
After integrating a local MCP server providing code analysis tools (analyze_dependencies, find_dead_code, calculate _complexity), you verify the server is healthy and tools appear in the tools/list response. However, you observe that the agent consistently uses Grep to search for import statements instead of calling analyze_dependencies -even when users explicitly ask about "code dependencies." Examining tool definitions reveals:
MCP: analyze_ dependencies - "Analyzes dependency graph"
Built-in: Grep - "Search file contents for a pattern using regular
expressions. Returns matching lines with line numbers and surrounding
context."
What's the most effective approach to improve the agent's selection of MCP tools?

A. Split analyze_dependencies into granular tools ( list_imports, resolve_transitive_deps, detect_circular_deps) so each has a focused purpose less likely to overlap with Grep.
B. Add routing instructions to the system prompt specifying that dependency-related questions should use MCP tools rather than Grep.
C. Remove Grep from available tools when the MCP server is connected to eliminate functional overlap.
D. Expand MCP tool descriptions to detail capabilities and outputs - e.g., "Builds dependency graph showing direct imports, transitive dependencies, and cycles."

Antwort: D

Begründung:
Claude selects tools primarily from their names and descriptions. A specific description distinguishing dependency-graph analysis-including direct imports, transitive dependencies, and cycles-from simple text search makes analyze_dependencies the clearer choice.

32. Frage
Production logs reveal inconsistent error handling: when lookup_order fails, the agent sometimes retries 5+ times (wasteful when the order ID doesn't exist), sometimes escalates immediately (premature for temporary network issues), and sometimes asks users for clarification (inappropriate when the issue is a backend permission error). Investigation shows your MCP tool returns uniform error responses: {"isError": true, "content": [{"type": "text", "text": "Operation failed"}]}. The agent cannot distinguish between error types. What's the most effective improvement?

A. Add few-shot examples to the system prompt demonstrating how to interpret error message patterns and select appropriate responses for each.
B. Enhance error responses with structured metadata: include errorCategory (transient/validation/permission), isRetryable boolean, and a description of what caused the failure.
C. Implement retry logic with exponential backoff in your MCP server for all errors, returning to the agent only after retries are exhausted.
D. Create an analyze_error MCP tool the agent calls after any failure to determine the error category and recommended action.

Antwort: B

Begründung:
Structured error metadata gives the agent the information needed to choose the right recovery path. By distinguishing transient, validation, and permission failures, and by explicitly indicating whether retry is appropriate, the agent can avoid wasteful retries, premature escalation, and unnecessary user clarification.

33. Frage
A financial services company plans to integrate Claude into an internal document analysis platform. The architects want to minimize exposure of confidential client information while maintaining high-quality responses. Which approach BEST aligns with Anthropic's recommended architecture?

A. Store every prompt permanently for auditing.
B. Remove unnecessary sensitive information before sending prompts whenever possible.
C. Send every document without preprocessing to maximize context.
D. Replace Claude with a smaller local model regardless of task complexity.

Antwort: B

Begründung:
Minimizing unnecessary sensitive information before sending prompts follows the principle of data minimization. Claude performs best when provided only with relevant context. Removing unrelated confidential information reduces privacy risk while preserving model performance and supporting secure enterprise AI architecture.

34. Frage
Claude produces occasional factual inaccuracies despite clear prompts. Which architectural enhancement is MOST effective?

A. Remove examples.
B. Add grounding through trusted retrieval.
C. Shorten responses only.
D. Increase temperature.

Antwort: B

Begründung:
Grounding responses with trusted enterprise documents helps Claude generate answers supported by authoritative sources. Retrieval significantly reduces unsupported statements while improving factual consistency across knowledge-intensive applications.

35. Frage
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