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Anthropic CCA-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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
Topic 2: 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. Error handling and validation 2. Tool descriptions and selection logic
Topic 3: 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
Topic 4: 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. Custom commands and workflows 2. CLAUDE.md and rules system
Topic 5: 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

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q83-Q88):

NEW QUESTION # 83
Which of the following strategies effectively manage context degradation in long-running exploratory sessions? Choose 2 correct answers.

A. Persisting key findings and intermediate states in external scratchpad files that survive context resets.
B. Delegating verbose exploration output to subagents to keep the main coordinator's context clean.
C. Continuously summarizing 'case facts' blocks to free up space.
D. Increasing the model's temperature parameter to encourage creative context recovery.

Answer: A,B

Explanation:
Delegating verbose exploration to subagents (who return only concise summaries to the main thread) and using external scratchpad files to persist key findings across context boundaries effectively mitigate context degradation. Summarizing 'case facts' defeats their purpose, and temperature adjustments do not fix context limits.

NEW QUESTION # 84
Why is providing more than 6 examples in a few-shot prompt generally considered an anti-pattern?

A. It disables the model's ability to use the tool_use block.
B. It causes the model to ignore system prompts.
C. It forces the Claude API to throw a schema validation error.
D. It bloats the prompt context window without adding proportional benefit.

Answer: D

Explanation:
Providing too many examples (more than 4-6) wastes valuable context window tokens and bloats the prompt without offering a proportional increase in output quality or instruction adherence.

NEW QUESTION # 85
A developer needs to safely explore two completely different architectural approaches for refactoring a database module without polluting the main conversation's context. Which Claude Code session command should be used?

A. / clear
B. / compact
C. --resume
D. fork_session

Answer: D

Explanation:
The fork_session command creates a parallel exploration branch that inherits the current context baseline but diverges from that point forward. This allows a developer to safely explore divergent approaches without the exploration noise polluting the main session's context window.

NEW QUESTION # 86
When re-running an automated PR review after new commits, what is the best practice to avoid posting duplicate comments on existing issues?

A. Use the Message Batches API to automatically deduplicate comments across runs.
B. Include prior review findings in the context and instruct Claude to report only new or still-unaddressed issues.
C. Run grep via Bash to parse previous GitHub comments and filter them out programmatically.
D. Clear the context window completely using /clear before every run.

Answer: B

Explanation:
To avoid duplicate comments, best practice dictates including prior review findings in the context when re-running reviews after new commits, explicitly instructing Claude to report only new or unaddressed issues.

NEW QUESTION # 87
Which prompt revision is MOST likely to improve output quality?

A. "Help me."
B. Use only uppercase letters.
C. "Summarize this."
D. Include objective, context, constraints, and expected format.

Answer: D

Explanation:
High-quality prompts clearly specify the objective, necessary background information, operational constraints, and desired response format. Claude performs best when expectations are explicit and well structured.

NEW QUESTION # 88
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