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Anthropic CCA-F Exam Syllabus Topics:

Section	Weight	Objectives
Tool Design & MCP Integration	18%	- Model Context Protocol (MCP) 1. Tools, resources, and prompts integration 2. MCP server and client setup - Tool definition and best practices 1. Tool descriptions and selection logic 2. Error handling and validation
Prompt Engineering & Structured Output	20%	- Structured data generation 1. JSON schema enforcement 2. Output validation and reliability - Advanced prompting techniques 1. System prompts and role framing 2. Few-shot and chain-of-thought prompting
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
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
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

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q84-Q89):

NEW QUESTION # 84
What is the primary architectural difference between enforcing a business rule via a system prompt versus using an Agent SDK hook?

A. Hooks provide 100% reliable deterministic enforcement, whereas prompts are probabilistic and can occasionally be ignored by the LLM.
B. Prompts execute after the tool runs, while hooks execute before the prompt is processed.
C. Prompts are deterministic, while hooks rely on the model's probabilistic reasoning.
D. Prompts require an active MCP server connection, while hooks run strictly within the Claude desktop application.

Answer: A

Explanation:
The critical distinction is that hooks execute as deterministic code, making them 100% reliable for critical policies, compliance, and security blocks. Prompts rely on the LLM's probabilistic pattern matching, meaning they can and occasionally will be ignored by the model.

NEW QUESTION # 85
Which factor MOST directly affects API cost?

A. CPU clock frequency
B. Number of processed tokens
C. Internet speed
D. Screen resolution

Answer: B

Explanation:
LLM pricing primarily depends on input and output tokens processed during inference. Efficient prompt design and limiting unnecessary context can reduce operational costs without sacrificing response quality.

NEW QUESTION # 86
When providing Claude with a 50-page document, you notice it accurately recalls details from the first and last pages but misses a critical instruction buried on page 25. What is this phenomenon called?

A. Context truncation
B. The "lost in the middle" effect
C. Attention dilution
D. Progressive summarization

Answer: B

Explanation:
The "lost in the middle" effect describes how models reliably process information at the beginning and end of long inputs but may omit findings or instructions buried in middle sections.

NEW QUESTION # 87
A development team wants to enforce TypeScript strict mode, specific ESLint rules, and API design standards for all developers working on a shared repository. Where is the correct location to configure these team-wide standards in Claude Code?

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

Answer: D

Explanation:
Team-wide standards that should be shared via version control belong in the project-level .claude/CLAUDE.md file. User-level config is for personal preferences (like dark theme or vim bindings) and is not shared. Directory-level config is scoped to specific module folders.

NEW QUESTION # 88
Production monitoring shows that follow-up queries like "summarize what we learned about market trends" consistently take 40+ seconds. Investigation reveals the coordinator spawns the synthesis subagent for each summarization request, passing 80K+ tokens of accumulated findings. The coordinator already has these findings in its context from orchestrating the research.
What's the most effective way to improve response time for these follow-up summaries?

A. Pre-generate and cache summaries at multiple granularities whenever new findings accumulate.
B. Have the coordinator handle straightforward summarization requests directly using its existing context, reserving subagent spawning for complex analysis.
C. Enable prompt caching on the synthesis subagent to reduce the overhead of repeatedly transferring the same research findings.
D. Spawn the synthesis subagent with reduced context and have it request specific findings from the coordinator on-demand.

Answer: B

Explanation:
The coordinator already holds the accumulated findings and can perform simple summarizations directly. Avoiding repeated spawning of the synthesis subagent for straightforward queries reduces token processing and latency, improving response speed while maintaining accuracy.

NEW QUESTION # 89
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