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Anthropic CCA-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Context Management & Responsible AI	15%	- Context window optimization 1. Token management and truncation strategies 2. Context retention and summarization - Safety and compliance 1. Refusal handling and risk mitigation 2. Constitutional AI principles
Topic 2: Claude Code Configuration & Workflows	20%	- Configuration files and structure 1. CLAUDE.md and rules system 2. Custom commands and workflows - CI/CD and development integration 1. Pipeline automation and review 2. Plan mode vs direct execution
Topic 3: Prompt Engineering & Structured Output	20%	- Advanced prompting techniques 1. System prompts and role framing 2. Few-shot and chain-of-thought prompting - Structured data generation 1. Output validation and reliability 2. JSON schema enforcement
Topic 4: 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 5: Agentic Architecture & Orchestration	27%	- Agent design patterns 1. Agent loops and control flow 2. Hub-and-spoke multi-agent systems - Anthropic Agent SDK usage 1. Session state and context handling 2. Task spawning and tool management

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q72-Q77):

NEW QUESTION # 72
A customer raises three separate issues during one session: a refund inquiry (turns 1-15), a subscription question (turns 16-30), and a payment method update (turns 31-45). At turn 48, the customer asks "What happened with my refund?" The conversation is approaching context limits.
What strategy best maintains the agent's ability to address all issues throughout the session?

A. Implement sliding window context that retains the most recent 30 turns.
B. Extract and persist structured issue data (order IDs, amounts, statuses) into a separate context layer.
C. Rely on MCP tools to re-fetch relevant information on demand when the customer references earlier issues.
D. Summarize earlier turns into a narrative description, preserving full message history only for the active issue.

Answer: B

Explanation:
Persisting structured issue data separately allows the agent to reference critical details (like order IDs, amounts, and statuses) without relying on the full conversation history. This preserves the ability to address past issues even as token limits constrain the active conversational context.

NEW QUESTION # 73
A PreToolUse hook is configured to intercept and block unauthorized file reads based on restricted file paths. How does the hook properly communicate the specific blocking reason back to Claude so the model can adapt?

A. By returning an exit code of 2 and writing the explanation to standard error, which is fed back as context.
B. By completely terminating the session and initiating a new one with a revised system prompt.
C. By silently returning an empty JSON array [ ] to the context window.
D. By overriding the context window entirely with an 'access denied' templated message.

Answer: A

Explanation:
In Claude Code and Agent SDK hooks, returning an exit code of 2 (or explicitly returning a 'blocked' flag with a reason string) and providing an explanation feeds the exact error context back to Claude. This allows the model to understand why the action was blocked and adjust its next steps accordingly.

NEW QUESTION # 74
After 30+ turns, your conversational assistant shows noticeably slower responses and occasionally produces less coherent outputs. Investigation reveals: (1) average conversations reach 50,000 tokens by turn 35, (2) production logs show 94% of user messages only reference the previous 3-5 exchanges, (3) the 6% of queries referencing earlier context typically ask about information the user could easily re-state. Your goal is to improve response speed and quality while maintaining good user experience. What's the most effective approach?

A. Build a retrieval system that stores all conversation turns and uses semantic search to pull in relevant historical context only when the current query appears to reference past information.
B. Implement a summarization layer that progressively compresses older conversation turns into a running summary while keeping the most recent 5-6 turns verbatim, maintaining full historical context in condensed form.
C. Implement a sliding window keeping only the system prompt and last 8-10 turns. When users reference earlier context, acknowledge the limitation and ask them to re-state the relevant information.
D. Enable prompt caching and continue sending the complete conversation history, using cached prefixes to reduce per-request costs while preserving all context.

Answer: B

Explanation:
Progressively summarizing older conversation turns condenses historical context while keeping recent interactions verbatim, reducing token load and maintaining coherence. This approach balances response speed, memory efficiency, and user experience by preserving essential context without requiring users to repeatedly provide information.

NEW QUESTION # 75
A developer wants to create a reusable behavior for performing extensive, multi-step codebase refactoring. The behavior needs to explore the codebase without polluting the main conversation's context window. Which extension mechanism should be used?

A. A skill defined in a SKILL.md file with context: fork in its frontmatter.
B. A PostToolUse hook running a background Bash script.
C. A project-scoped CLAUDE .md configuration file.
D. A custom command in . claude/commands/.

Answer: A

Explanation:
Skills are designed for complex, multi-step behaviors. By adding 'context: fork' to the SKILL.md frontmatter, the skill runs in an isolated sub-agent context, ensuring that its verbose exploration and intermediate steps do not pollute the main session's context window. Custom commands run directly in the main context.

NEW QUESTION # 76
The configuration hierarchy in Claude Code resolves settings from multiple levels. Which of the following are valid configuration layers where CLAUDE.md files can be placed? Choose 2 correct answers.

A. Directory-level (e.g., src/api/CLAUDE .md) for module-scoped conventions.
B. Cloud-level (e.g., s3 : //config/CLAUDE .md) for distributed synchronization.
C. Global system-level (e.g., /etc/c1aude/CLAl_JDE. md) for machine-wide mandates.
D. User-level (e.g., 4. claude/CLAUDE. md) for personal preferences.

Answer: A,D

Explanation:
The three valid configuration layers in Claude Code are User-level (-/.claude/CLAUDE.md), Project-level (.claude/CLAUDE.md), and Directory-level (scoped to specific subdirectories). Global system-level and cloud-level configs are not standard mechanisms.

NEW QUESTION # 77
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