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Anthropic CCA-F Exam Syllabus Topics:

Section	Weight	Objectives
Tool Design & MCP Integration	18%	- Model Context Protocol (MCP) 1. MCP client/server integration patterns 2. Tool schema design
Agentic Architecture & Orchestration	27%	- Agentic loop design and execution lifecycle 1. Tool use decision flow (tool_use vs end_turn) 2. Multi-agent coordination and delegation patterns - System orchestration patterns 1. Hub-and-spoke agent systems 2. Task decomposition strategies
Claude Code Workflows & Configuration	20%	- Claude Code operational patterns 1. CI/CD and workflow integration 2. Plan mode vs execution mode
Context Management & Reliability	15%	- Long-context optimization 1. Context window management 2. Reliability and retry strategies
Prompt Engineering & Structured Output	20%	- Reliable structured generation 1. Schema-guided outputs 2. Prompt patterns for agents

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q105-Q110):

NEW QUESTION # 105
When planning complex multi-flle architectural changes, such as a monolith-to-microservices migration, why is it recommended to use plan mode rather than direct execution?

A. Plan mode overrides the token limit context window by executing across multiple continuous sessions automatically.
B. Plan mode restricts Claude to using only read-only tools to explore the codebase, design a strategy, and get developer approval before making costly modifications.
C. Plan mode utilizes the Message Batches API to reduce token costs by 50 percent.
D. Plan mode automatically generates missing unit tests for legacy code before executing.

Answer: B

Explanation:
Plan mode is designed for complex tasks requiring architectural decisions. It leverages read-only tools to explore and outline a plan, allowing developers to review the strategy before committing to file modifications. Direct execution is better suited for simple, well-defined fixes.

NEW QUESTION # 106
To prevent an exploratory skill from making destructive changes to the file system, you must restrict the tools it can access. How is this enforced in a SKILL.md file?

A. By setting permissions: read-only in the YAML frontmatter.
B. By defining a PreToolUse hook inside the skill definition.
C. By omitting the Write and Edit instructions from the markdown text.
D. By using the allowed-tools frontmatter field to explicitly list permitted tools like Read, Grep, Glob.

Answer: D

Explanation:
To restrict which tools Claude can use when a skill is active, the 'allowed-tools' metadata field must be used in the YAML frontmatter. Relying on markdown text instructions is probabilistic and insecure for preventing destructive actions.

NEW QUESTION # 107
Your structured data extraction pipeline implements a validation-retry loop. When an extracted value fails a business logic check (e.g., line items do not sum to the total), how should the retry prompt be structured?

A. Return a generic message such as There were errors in your extraction. Please try again.
B. Automatically increase the model temperature and resubmit the original prompt.
C. Send a follow-up request including the original document, the failed extraction, and the specific validation error details.
D. Switch to a more capable model and restart the session without the failed extraction history.

Answer: C

Explanation:
A successful validation-retry loop requires appending specific error details (not generic messages) to the prompt and supplying both the source document and the failed extraction. Generic retry messages give the model no signal for what to fix.

NEW QUESTION # 108
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. Use the /clear command immediately after the tool call returns.
B. Place the raw tool output at the very end of the prompt to leverage the recency effect.
C. Trim the verbose tool outputs to only the relevant fields before they accumulate in the conversation context.
D. Apply prompt chaining to summarize the JSON object into natural language.

Answer: C

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 # 109
Why is configuring the exact same Claude session to review its newly generated pull request code considered an architectural anti- pattern?

A. Tools invoked during the first pass are permanently locked and cannot be reused in the review pass.
B. The Message Batches API does not support multi-turn tool calling within a single request.
C. The reviewer retains the reasoning context and assumptions from the generation phase, creating confirmation bias.
D. It exceeds the maximum token limit for the context window during the generation phase.

Answer: C

Explanation:
Same-session self-review is an anti-pattern because the model retains the context and reasoning from the generation phase, leading to a blind spot or confirmation bias. Independent review instances with fresh contexts are much more effective at objectively evaluating code.

NEW QUESTION # 110
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