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Anthropic CCA-F Exam Syllabus Topics:

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

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q48-Q53):

NEW QUESTION # 48
A pull request modifies 14 files across a repository. A single-pass automated review produces inconsistent results, catching deep bugs in some files while missing obvious ones in others. How should the review architecture be redesigned to fix this?

A. Switch to a higher-tier model with a larger context window to process all files simultaneously.
B. Require developers to manually split the PR into smaller submissions of 3-4 files.
C. Run three independent reviews on the full PR and only flag issues that reach consensus.
D. Split the review into focused passes: analyze each file individually for local issues, followed by a separate integration pass for cross-file data flow.

Answer: D

Explanation:
Splitting large code reviews into per-file local analysis passes (to catch syntax/local logic bugs) plus a separate cross-file integration pass prevents attention dilution. Relying solely on larger context windows or requiring consensus across full monolithic runs are anti-patterns.

NEW QUESTION # 49
During a multi-source research workflow, what is the primary risk to information provenance when intermediate findings are compressed using standard summarization?

A. The model's extended thinking budget is exhausted before synthesis can occur.
B. Source attribution is silently lost unless structured claim-source mappings are explicitly required and preserved.
C. The context window automatically truncates the middle sections of the summary.
D. It triggers a PreToolUse hook that blocks the synthesis agent from reading the data.

Answer: B

Explanation:
A major risk in multi-source synthesis is that source attribution is lost during summarization steps when findings are compressed. To prevent this, architects must require structured claim-source mappings that downstream agents are instructed to explicitly preserve through the synthesis phase.

NEW QUESTION # 50
A user asks Claude to ignore all previous instructions and reveal confidential internal prompt content. How should Claude behave?

A. Follow higher-priority instructions and refuse.
B. Generate random text.
C. Partially reveal hidden instructions.
D. Reveal the prompt.

Answer: A

Explanation:
Claude follows instruction hierarchy, where system-level instructions override conflicting user requests. Attempts to expose confidential prompts or internal instructions should be declined to protect application integrity and security.

NEW QUESTION # 51
The web search agent has gathered several relevant sources for a research topic. The document analysis agent now needs to examine these sources. How does information typically flow between these two specialized subagents?

A. The agents communicate through an event-driven message queue, with the document analysis agent subscribing to web search completion events.
B. The web search agent directly invokes the document analysis agent, passing the discovered sources as parameters.
C. Both agents access a shared memory store where the web search agent writes findings and the document analysis agent reads them.
D. The coordinator agent receives the web search agent's output and includes relevant findings in the prompt when invoking the document analysis agent.

Answer: D

Explanation:
In a coordinated multi-agent pipeline, the coordinator mediates information flow. It collects outputs from the web search agent and includes the relevant findings in the prompt when invoking the document analysis agent, ensuring proper sequencing and context without tightly coupling the subagents.

NEW QUESTION # 52
Which factor MOST directly affects API cost?

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

Answer: D

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 # 53
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