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Anthropic CCA-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Prompt Engineering & Structured Output	20%	- Advanced prompting techniques 1. Few-shot and chain-of-thought prompting 2. System prompts and role framing - Structured data generation 1. JSON schema enforcement 2. Output validation and reliability
Topic 2: Tool Design & MCP Integration	18%	- Tool definition and best practices 1. Tool descriptions and selection logic 2. Error handling and validation - Model Context Protocol (MCP) 1. MCP server and client setup 2. Tools, resources, and prompts integration
Topic 3: Context Management & Responsible AI	15%	- Context window optimization 1. Context retention and summarization 2. Token management and truncation strategies - Safety and compliance 1. Refusal handling and risk mitigation 2. Constitutional AI principles
Topic 4: Claude Code Configuration & Workflows	20%	- Configuration files and structure 1. Custom commands and workflows 2. CLAUDE.md and rules system - CI/CD and development integration 1. Plan mode vs direct execution 2. Pipeline automation and review
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. Task spawning and tool management 2. Session state and context handling

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q104-Q109):

NEW QUESTION # 104
You are building a customer support resolution agent using the Claude Agent SDK. The agent handles high-ambiguity requests like returns, billing disputes, and account issues. It has access to your backend systems through custom Model Context Protocol (MCP) tools ( get_customer, lookup_order , process_refund , escalate_to_human ). Your target is 80%+ first-contact resolution while knowing when to escalate.
After expanding the agent's MCP tools with delivery-specific capabilities ( check_delivery_status , contact_driver , issue_credit , apply_promo_code , update_delivery_address , reschedule delivery ), the total tool count has grown from 4 to 10. Your evaluation suite shows tool selection accuracy has dropped from 88% to 71%. Log analysis reveals the majority of errors involve the agent selecting between semantically overlapping tools - calling issue_credit when process_refund was correct, and calling check_delivery_status when lookup_order already returns the needed data. Which approach structurally eliminates the semantic overlap identified in the logs as the error source?

A. Split the tools across two sub-agents - a "financial resolution" agent with process_refund , issue_credit ,and apply_promo_code , and a "delivery operations" agent with the remaining delivery tools - with a coordinator routing between them.
B. Add few-shot examples to the system prompt demonstrating correct selection for each ambiguous tool pair, such as showing when issue_credit applies versus when process_refund is appropriate.
C. Enable the tool search tool with defer_loading on the six new tools, keeping the original four always loaded, so the agent dynamically discovers specialized tools only when needed.
D. Consolidate semantically overlapping tools - merge issue_credit and process_refund into a single resolve_compensation tool with an action parameter, and fold check_delivery_status into lookup_order with an optional include_tracking flag.

Answer: D

Explanation:
Merging issue_credit and process_refund into one compensation tool, and folding delivery-status lookup into lookup_order, removes competing tools with overlapping purposes. This directly eliminates the ambiguity causing incorrect tool selection, rather than merely hiding tools, routing between agents, or adding examples.

NEW QUESTION # 105
When passing context between agents in a multi-agent orchestration, why is it highly recommended to use structured data formats?

A. To separate content from metadata (such as source URLs, document names, and page numbers) to preserve proper attribution.
B. To bypass the maximum token limit of the subagent's isolated context window.
C. To automatically trigger JSON schema validation by the Claude API.
D. To compress the token count of the prompt by 50% using the Message Batches API.

Answer: A

Explanation:
Using structured data formats to separate content from metadata (like source URLs, document names, and publication dates) when passing context between agents ensures that downstream synthesis agents preserve proper source attribution and information provenance.

NEW QUESTION # 106
An architect needs Claude to produce structured JSON that downstream software can parse automatically. Which prompting strategy is BEST?

A. Use multiple unrelated examples.
B. Ask Claude to "respond normally."
C. Increase maximum output tokens only.
D. Explicitly specify the required JSON schema and formatting rules.

Answer: D

NEW QUESTION # 107
When using Claude Code for test generation and feature implementation, what is the recommended Test-Driven Development (TDD) iterative refinement pattern?

A. Write implementation write tests -> prompt Claude to 'make it better' -> refactor.
B. Run the /test slash command to automatically infer and generate missing unit tests for the entire project.
C. Use Plan Mode to write all tests simultaneously, then execute them in a single batch.
D. Write a failing test -> implement code to pass the test -> run tests -> refine implementation while keeping tests green.

Answer: D

NEW QUESTION # 108
When implementing a self-evaluation loop for structured data extraction, your agent reports 96% aggregate accuracy, but downstream users complain about frequent errors on complex contracts. What monitoring methodology resolves this evaluation visibility gap?

A. Executing the evaluation passes inside a PreToolUse hook.
B. Switching entirely to the Message Batches API for asynchronous processing.
C. Increasing the overall sampling size randomly to capture a broader dataset.
D. Tracking accuracy by specific document type and field segment (stratified metrics).

Answer: D

Explanation:
Aggregate accuracy metrics can mask severe per-document-type failures (e.g., contracts failing frequently while simple receipts succeed nearly 100% of the time). Tracking accuracy using stratified metrics (per document type and field) reveals these hidden failures before automating high-confidence extractions.

NEW QUESTION # 109
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