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Anthropic CCA-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Context Management & Reliability	15%	- System reliability 1. Escalation strategies (fail vs retry vs human) 2. Error propagation handling - Context window optimization 1. Managing long conversation degradation 2. "Lost in the middle" mitigation strategies
Topic 2: Agentic Architecture & Orchestration	27%	- Multi-agent orchestration 1. Parallel task decomposition 2. Coordinator / sub-agent patterns - Agentic loops & lifecycle design 1. Stop reason handling and control flow 2. Tool use loop execution (request → tool_use → result → next step)
Topic 3: Prompt Engineering & Structured Output	20%	- Instruction design 1. Few-shot prompting strategies 2. Deterministic output formatting (e.g., JSON) - Tool-augmented prompting 1. Validation and retry loops 2. Tool-use driven reasoning patterns
Topic 4: Tool Design & MCP Integration	18%	- Tool interface design 1. Clear tool descriptions and boundaries 2. Structured tool input/output schemas - Model Context Protocol (MCP) 1. Tool selection and routing strategies 2. MCP server integration patterns
Topic 5: 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. Automated pipeline integration 2. Command vs plan mode usage

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q71-Q76):

NEW QUESTION # 71
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. Spawn the subagent with only a brief task description, relying on automatic context inheritance from the coordinator
B. Pass reference identifiers and configure the subagent with read access to a shared memory store where other subagents deposited their results
C. Provide the subagent with tool definitions that allow it to request outputs from other subagents via callbacks
D. Include the complete findings from both subagents directly in the synthesis subagent's prompt

Answer: D

Explanation:
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.

NEW QUESTION # 72
Which statement BEST describes Retrieval-Augmented Generation?

A. It permanently retrains Claude.
B. It supplies relevant external knowledge during inference.
C. It changes token pricing.
D. It compresses model weights.

Answer: B

Explanation:
RAG retrieves relevant documents at inference time and includes them within the prompt. The model itself is unchanged, allowing organizations to incorporate current enterprise knowledge without retraining foundation models.

NEW QUESTION # 73
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. Reduce the number of tools per agent to 4-5 by distributing the rest across specialized subagents.
B. Implement a routing layer that pre-selects tools based on keyword matching in the user's prompt.
C. Switch to a higher-tier model with a larger context window to better process the 18 tool schemas.
D. Expand the tool descriptions to include more exhaustive examples and edge cases for all 18 tools.

Answer: A

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 # 74
You are using Claude Code to accelerate software development. Your team uses it for code generation, refactoring, debugging, and documentation. You need to integrate it into your development workflow with custom slash commands, CLAUDE.md configurations, and understand when to use plan mode vs direct execution.
You're implementing a caching layer for API responses to speed up the /products endpoint. You have a rough idea-Redis with a 5-minute TTL-but you're new to production caching and aren't sure what other considerations a robust implementation requires. What's the most effective way to start your iterative workflow?

A. Start with a minimal request: "Add Redis caching to /products with 5-minute TTL." Add features and fix issues through follow-up prompts as problems surface during testing.
B. Use plan mode to analyze the current/products endpoint implementation, then provide your caching requirements once Claude explains how the existing code is structured.
C. Write a specification with your known requirements and "TBD" markers for uncertain areas, having Claude propose solutions for each TBD as it implements.
D. Ask Claude to interview you about the caching requirements before implementing, surfacing considerations like invalidation strategies, cache layers, consistency guarantees, and failure modes.

Answer: D

Explanation:
Because the requirements are incomplete and production caching involves hidden design choices, an interview helps surface invalidation, consistency, failure handling, and other trade-offs before implementation. Anthropic recommends this approach for larger features when important requirements may not yet be identified.

NEW QUESTION # 75
When authoring a complex skill, relying on a massive 2000-line SKILL. md file wastes context window tokens. Which of the following describe the recommended "progressive disclosure" architectural pattern? Choose 2 correct answers.

A. Keep the SKILL .md file under 500 lines and link to supporting reference files that Claude reads only when needed.
B. Configure an automatic /compact command to execute every 10 turns within the skill definition.
C. Use the fork_session command explicitly on line 1 of the markdown to split the file structure.
D. Store executable validation code in a scripts/ directory and instruct Claude to run them rather than read their contents.

Answer: A,D

Explanation:
Progressive disclosure in skill architecture involves keeping the main SKILL.md file small (under 500 lines) and linking to supporting files (in directories like references/ and scripts!) that Claude reads or executes only when relevant. Instructing Claude to run scripts rather than read them prevents the script's raw source code from consuming context tokens.

NEW QUESTION # 76
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