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Anthropic CCA-F Exam Syllabus Topics:

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

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q111-Q116):

NEW QUESTION # 111
When generating complex mock data setups for integration tests, natural language descriptions often produce inconsistent structural results from Claude. What is the most effective technique to fix this?

A. Use the /compact command before requesting the test generation.
B. Increase the max_tokens parameter to give the model more space to reason.
C. Provide 2-3 concrete input/output examples demonstrating the exact expected transformation.
D. Ask Claude to evaluate its own test generation output before returning it.

Answer: C

Explanation:
Concrete input/output (few-shot) examples are the most effective way to communicate expected transformations when prose descriptions are interpreted inconsistently. They demonstrate the exact structural formatting required for complex data setups.

NEW QUESTION # 112
Why is returning an empty array [ ] for a database timeout considered an architectural anti-pattern in error propagation?

A. It exceeds the context window limits of the coordinator.
B. It misleads the coordinator into concluding the data does not exist, rather than knowing an access failure occurred.
C. It violates strict JSON schema syntax requirements for tool results.
D. It automatically triggers an infinite retry loop within the subagent.

Answer: B

Explanation:
Silently suppressing errors by returning empty results as success is a catastrophic anti-pattern If a database is down (timeout) and returns l], the agent mistakenly concludes that no data exists, rather than understanding it failed to access the system.

NEW QUESTION # 113
An architect is designing an MCP tool that occasionally fails due to downstream rate limits. Which fields should be included in a well- designed structured error response to help the agent recover? Choose 2 correct answers.

A. temperature _ adjustment
B. system_prompt_override
C. isError: true
D. isRetryable: true

Answer: C,D

Explanation:
A structured error response should explicitly signal a failure using the 'isError' flag and indicate if the agent should attempt the call again using the 'isRetryable' boolean. It should also include the error category and context. System prompt overrides and temperature adjustments are not part of standard tool error metadata.

NEW QUESTION # 114
You are designing an MCP tool to look up customer orders. Which approach to tool descriptions is recommended to ensure reliable tool selection by Claude?

A. Include the input format, example queries, edge cases, and boundaries explaining when to use it versus similar tools.
B. Keep the description under 10 words to save context window tokens.
C. Use the description solely to list the required JSON properties.
D. Leave the description blank and rely on Claude's semantic understanding of the tool's name.

Answer: A

Explanation:
Tool descriptions act as the primary mechanism LLMs use for tool selection. A well-designed tool description must be detailed and include input formats, example queries, edge cases, and explicit boundary instructions explaining when to use it versus similar tools to prevent misrouting.

NEW QUESTION # 115
You need to create a reusable behavior for complex refactoring. To prevent the exploration noise from polluting the main session's context window, which configuration should you use in your SKILL. md?

A. Use a standard custom command in . claude/commands/ instead of a skill.
B. Set context: fork in the YAML frontmatter.
C. Add the /compact command at the end of the skill's instructions.
D. Set isolation: true in the YAML frontmatter.

Answer: B

Explanation:
The 'context: fork' frontmatter option runs the skill in an isolated sub-agent context. This allows it to perform verbose exploration without polluting the main conversation session. Regular commands run in the main context.

NEW QUESTION # 116
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