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Anthropic CCA-F Exam Syllabus Topics:

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

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q108-Q113):

NEW QUESTION # 108
In a Continuous Integration (CI) pipeline, you run a script where a single Claude session generates a block of code and then immediately reviews its own changes in the same session. Why is this considered an architectural anti-pattern?

A. It exceeds the maximum token limit for the context window, causing the session to silently truncate the generated code.
B. Same-session reviews trigger rate limiting automatically, breaking the CI/CD pipeline.
C. The reviewer retains the reasoning context from the code generation phase, creating confirmation bias and reducing the likelihood of catching subtle issues.
D. The Message Batches API does not support multi-turn tool calling within a single request.

Answer: C

Explanation:
Same-session self-review is an anti-pattern because the model retains its original reasoning context, creating a blind spot. Independent review instances without prior reasoning context are much more effective at objectively evaluating code.

NEW QUESTION # 109
When using the Claude API, how must a system prompt be formatted to successfully utilize prompt caching?

A. Provide the system prompt as a standard string and set prompt_caching: true at the top level of the API request.
B. Include the system prompt inside an XML block labeled .
C. Structure the system prompt as a dictionary with a text block and include a cache_control field set to {"type": " ephemeral " }
D. Use the Message Batches API to ensure the system prompt is automatically cached across all batched requests.

Answer: C

Explanation:
To cache a system prompt, it cannot be passed as a simple string. It must be structured as a text block (a dictionary) that explicitly contains the cache_control field set to {"type": "ephemeral"}.

NEW QUESTION # 110
A developer needs to safely explore two completely different architectural approaches for refactoring a database module without polluting the main conversation's context. Which Claude Code session command should be used?

A. / compact
B. / clear
C. --resume
D. fork_session

Answer: D

Explanation:
The fork_session command creates a parallel exploration branch that inherits the current context baseline but diverges from that point forward. This allows a developer to safely explore divergent approaches without the exploration noise polluting the main session's context window.

NEW QUESTION # 111
A developer grants a single Claude agent access to 18 different tools for a complex refactoring task. Production logs indicate that the agent frequently selects the wrong tool. What is the most effective architectural fix for this issue?

A. Expand the tool descriptions to include more exhaustive examples and edge cases for all 18 tools.
B. Reduce the number of tools per agent to 4-5 by distributing the rest across specialized subagents.
C. Implement a routing layer that pre-selects tools based on keyword matching in the user's prompt.
D. Switch to a higher-tier model with a larger context window to better process the 18 tool schemas.

Answer: B

Explanation:
Tool selection reliability degrades significantly when an agent is given too many tools (e.g., 18). The optimal approach is to scope tool access by keeping 4-5 tools per agent and distributing the rest across specialized subagents tailored to specific roles.

NEW QUESTION # 112
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. Provide 2-3 concrete input/output examples demonstrating the exact expected transformation.
B. Increase the max_tokens parameter to give the model more space to reason.
C. Use the /compact command before requesting the test generation.
D. Ask Claude to evaluate its own test generation output before returning it.

Answer: A

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 # 113
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