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Anthropic CCA-F Exam Syllabus Topics:

Section	Weight	Objectives
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
Context Management & Responsible AI	15%	- Safety and compliance 1. Refusal handling and risk mitigation 2. Constitutional AI principles - Context window optimization 1. Token management and truncation strategies 2. Context retention and summarization
Prompt Engineering & Structured Output	20%	- Structured data generation 1. Output validation and reliability 2. JSON schema enforcement - Advanced prompting techniques 1. Few-shot and chain-of-thought prompting 2. System prompts and role framing
Agentic Architecture & Orchestration	27%	- Anthropic Agent SDK usage 1. Session state and context handling 2. Task spawning and tool management - Agent design patterns 1. Hub-and-spoke multi-agent systems 2. Agent loops and control flow
Claude Code Configuration & Workflows	20%	- CI/CD and development integration 1. Plan mode vs direct execution 2. Pipeline automation and review - Configuration files and structure 1. CLAUDE.md and rules system 2. Custom commands and workflows

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q14-Q19):

NEW QUESTION # 14
A properly structured SKILL.md file relies on YAML frontmatter metadata to inform Claude of its capabilities. Which of the following are REQUIRED fields in the skill frontmatter? Choose 2 correct answers.

A. model
B. allowed-tools
C. name
D. description

Answer: C,D

Explanation:
The 'name' and 'description' fields are the only strictly required fields in the SKILL.md frontmatter. Claude relies on the description for semantic matching to determine when to trigger the skill. Options like allowed-tools and model are powerful, but optional.

NEW QUESTION # 15
You are building developer productivity tools using the Claude Agent SDK. The agent helps engineers explore unfamiliar codebases, understand legacy systems, generate boilerplate code, and automate repetitive tasks. It uses the built-in tools (Read, Write, Bash, Grep, Glob) and integrates with Model Context Protocol (MCP) servers.
Your agent has spent 25 minutes exploring a game engine's rendering subsystem -reading shader code, buffer management, and frame synchronization logic. An engineer now asks it to understand how the physics engine integrates with rendering for collision debug overlays. You notice recent responses reference "typical rendering patterns" rather than the specific VulkanPipeline and FrameGraph classes it discovered earlier.
What's the most effective approach?

A. Use /clear to reset context completely, then start fresh with physics exploration using file paths from the project's CLAUDE.md.
B. Spawn a sub-agent to explore physics independently, then manually synthesize its findings with the rendering knowledge accumulated in the main conversation.
C. Summarize key rendering findings, then spawn a sub-agent for physics exploration with that summary in its initial context.
D. Continue in the current context with more targeted prompts referencing the specific classes by name.

Answer: C

Explanation:
The current context is showing degradation and losing project-specific details. A focused summary preserves the relevant VulkanPipeline and FrameGraph findings, while a fresh sub- agent provides clean context for exploring physics and relating it to rendering.

NEW QUESTION # 16
When designing escalation patterns for an autonomous customer support agent, which of the following represent architectural anti- patterns that should be avoided? Choose 2 correct answers.

A. Relying on the model's self-reported confidence score to determine if human review is needed.
B. Escalating cases immediately when a customer explicitly requests to speak to a human.
C. Escalating cases when the agent detects a policy gap not covered by its instructions.
D. Escalating cases automatically based on high levels of negative customer sentiment.

Answer: A,D

Explanation:
Using self-reported model confidence scores and customer sentiment are heavily tested anti-patterns. Model confidence is poorly calibrated for routing, and sentiment does not correlate with task complexity. Explicit human requests and policy gaps are valid escalation triggers.

NEW QUESTION # 17
An agent queries an external API that returns 40+ fields of metadata per document, but the agent only needs the status and creation_date. How should this be handled to manage the context window effectively?

A. Apply prompt chaining to summarize the JSON object into natural language.
B. Trim the verbose tool outputs to only the relevant fields before they accumulate in the conversation context.
C. Place the raw tool output at the very end of the prompt to leverage the recency effect.
D. Use the /clear command immediately after the tool call returns.

Answer: B

Explanation:
Tool results accumulate in context and consume tokens disproportionately to their relevance. Trimming verbose tool outputs to only relevant fields before they accumulate in context preserves the token budget.

NEW QUESTION # 18
A developer at quicktechie.com is building a highly experimental MCP server testing a new vector database. They want to use it locally in Claude Code without exposing the configuration to the rest of the team. Which configuration file should they use?

A. .claude/rules/mcp.md
B. ~/.claude.json
C. .mcp.json at the project root
D. mcp_local.config

Answer: B

Explanation:
Personal, experimental, or user-specific MCP configurations should be placed in the user-scoped -/.claude.json file, preventing them from being shared with the team via the project repository's .mcp.json.

NEW QUESTION # 19
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