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Anthropic CCA-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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)
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: 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. MCP server integration patterns 2. Tool selection and routing strategies
Topic 4: Prompt Engineering & Structured Output	20%	- Instruction design 1. Deterministic output formatting (e.g., JSON) 2. Few-shot prompting strategies - Tool-augmented prompting 1. Tool-use driven reasoning patterns 2. Validation and retry loops
Topic 5: Claude Code Configuration & Workflows	20%	- Claude Code configuration 1. Project-level vs user-level configuration 2. CLAUDE.md hierarchy and scoping - 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 (Q42-Q47):

NEW QUESTION # 42
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 new payment processing module that must follow your project's established patterns for database transactions, error handling, and audit logging. You've identified three existing modules that exemplify these patterns: db_utils.py, error_handlers.py, and audit_logger.py. This is a one-off integration task - these patterns are well-documented in your team wiki and don't need additional project-level documentation. What's the most effective approach?

A. Ask Claude to explore your codebase to find and understand the transaction, error handling, and logging patterns before generating the new module.
B. Use @ references to include the three modules directly in your prompt, giving Claude concrete code examples of the patterns to follow.
C. Describe the patterns from the three modules in natural language in your prompt, explaining the transaction handling approach, error format, and logging conventions Claude should follow.
D. Add documentation of each pattern to your CLAUDE.md file, establishing them as project conventions that Claude will apply automatically.

Answer: B

Explanation:
This is a one-off task, so directly attaching the relevant files gives Claude concrete, authoritative examples without permanently expanding CLAUDE.md. Claude Code supports @ references specifically for bringing files or directories into the current task's context.

NEW QUESTION # 43
Which of the following scenarios represent appropriate, architecturally sound use cases for Agent SDK hooks? Choose 2 correct answers.

A. Normalizing heterogeneous numeric status codes from different MCP tools before appending them to the conversation context.
B. Intercepting outgoing tool calls to deterministically block policy-violating actions, such as unauthorized financial transactions.
C. Guaranteeing that a model-extracted JSON object contains no semantic reasoning errors.
D. Enforcing the conversational tone, politeness, and persona the agent should adopt.

Answer: A,B

Explanation:
Hooks are ideal for programmatic interventions like data normalization CPostToolUse*) and policy enforcement/interception ('PreToolUse'PPostToolUse'). Conversational tone is best handled by prompts, and hooks cannot inherently guarantee the semantic accuracy of an LLM's complex reasoning.

NEW QUESTION # 44
In a multi-agent research system, subagent A retrieves revenue data from a verified financial database, while subagent B extracts conflicting revenue data from an outdated PDF. How should the system handle this uncertainty before presenting it to a human reviewer?

A. Silently drop the PDF data and return the database value since databases are always correct.
B. Average the two values to provide a balanced estimate to the human reviewer.
C. Restart both subagents using a higher temperature setting to generate a consensus value.
D. Maintain structured claim-source mappings with information provenance (source, confidence, timestamp) so the coordinator or human can make an informed resolution.

Answer: D

Explanation:
When handling conflicting information, it is an anti-pattern to silently resolve conflicts or average them. The system must maintain information provenance (claim-source mappings including source, confidence, and timestamp) to provide an audit trail and enable informed conflict resolution.

NEW QUESTION # 45
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. Leave the description blank and rely on Claude's semantic understanding of the tool's name.
B. Include the input format, example queries, edge cases, and boundaries explaining when to use it versus similar tools.
C. Use the description solely to list the required JSON properties.
D. Keep the description under 10 words to save context window tokens.

Answer: B

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 # 46
Which configuration ensures Claude will strictly invoke a specific data extraction tool rather than returning conversational text?

A. Option A
B. Option D
C. Option C
D. Option B

Answer: C

Explanation:
Forcing a specific tool with tool_choice: {"type": "tool", "name": "..."} ensures a particular tool runs, which guarantees schema compliance for strict extraction tasks. The 'any' setting forces the model to use a tool, but allows it to choose which one.

NEW QUESTION # 47
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