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Anthropic CCA-F Exam Syllabus Topics:

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

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q67-Q72):

NEW QUESTION # 67
In a multi-agent orchestration, the synthesis agent occasionally misuses a web search tool instead of synthesizing the provided findings. How should you prevent this cross-role misuse according to the principle of scoped tool access?

A. Remove the web search tool from the synthesis agent's allowed tool set entirely.
B. Add a capitalized warning in the system prompt instructing the synthesis agent to ignore web search capabilities.
C. Use the fork session command to isolate the web search tool execution.
D. Implement a PostToolUse hook to silently drop web search results when executed by the synthesis agent.

Answer: A

Explanation:
Agents with tools outside their specialization tend to misuse them. The most robust architectural fix for cross-role tool misuse is scoped tool access: explicitly removing the irrelevant tool from that specific subagent's allowed array so it strictly focuses on its assigned role.

NEW QUESTION # 68
A developer is using a multi-agent system to analyze a massive legacy codebase. The coordinator agent is running out of context window space because the search tool returns verbose file contents and exploration noise. What is the best pattern to mitigate this context degradation?

A. Share the full coordinator conversation history with all subagents to ensure maximum context propagation.
B. Delegate the verbose file-by-file exploration to a subagent, which then returns only a synthesized summary of its findings back to the coordinator's context.
C. Force the coordinator agent to use the Bash tool to run grep manually instead of reading files.
D. Switch from dynamic adaptive decomposition to static prompt chaining to force a linear review path.

Answer: B

Explanation:
Subagent delegation is a core mitigation for context degradation. By delegating verbose exploration tasks to subagents, the subagent's isolated context absorbs the exploration noise, and only the clean, synthesized results are returned to the coordinator.

NEW QUESTION # 69
To systematically analyze why developers dismiss certain automated code review findings in a quicktechie.com application, which architectural pattern should be implemented?

A. Execute a multi-pass review on every pull request to ensure consensus.
B. Use the Message Batches API to delay findings until end-of-week review.
C. Run a separate classifier model to predict which findings will be ignored.
D. Add a detected_pattern field to the structured findings to track and aggregate dismissal reasons.

Answer: D

Explanation:
Feedback loop design requires tracking which code constructs trigger specific findings. By including a 'detected_pattern' field in the extraction or review output, teams can systematically analyze pattern clusters when developers dismiss findings, identifying systematic false positives.

NEW QUESTION # 70
In a multi-pass review architecture, how can you effectively automate the routing of identified findings for human review calibration?

A. Use the Message Batches API to delay findings until the end-of-week manual review.
B. Rely strictly on the model's self-reported conversational confidence score in the output.
C. Escalate any finding that flags more than 5 lines of code.
D. Run verification passes where the model outputs field-level confidence scores alongside findings to enable calibrated review routing.

Answer: D

Explanation:
To optimize human-in-the-loop workflows, models should output structured, field-level confidence scores that are calibrated against labeled validation sets. This allows reliable routing of low-confidence or high-risk findings for human review.

NEW QUESTION # 71
A developer is designing a hub-and-spoke multi-agent system. How should context be passed from the coordinator down to a newly spawned subagent?

A. The subagent automatically inherits the coordinator's full conversation history by default.
B. Pass ONLY the explicit context, findings, and parameters specifically relevant to the subagent's assigned subtask.
C. Provide the coordinator's complete context window inside the Task tool prompt for maximum context visibility.
D. Write all context to a shared scratchpad file that all agents read globally before executing their turns.

Answer: B

Explanation:
Context isolation is a core principle in multi-agent orchestration. A subagent does not automatically inherit the parent's context and should never be passed the full conversation history, as this causes context pollution. It should only receive explicit, relevant context specific to its task.

NEW QUESTION # 72
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