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Anthropic CCA-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Prompt Engineering & Structured Output	20%	- Reliable structured generation 1. Prompt patterns for agents 2. Schema-guided outputs
Topic 2: Claude Code Workflows & Configuration	20%	- Claude Code operational patterns 1. Plan mode vs execution mode 2. CI/CD and workflow integration
Topic 3: Tool Design & MCP Integration	18%	- Model Context Protocol (MCP) 1. MCP client/server integration patterns 2. Tool schema design
Topic 4: 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)
Topic 5: Context Management & Reliability	15%	- Long-context optimization 1. Reliability and retry strategies 2. Context window management

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q77-Q82):

NEW QUESTION # 77
You are building a multi-agent research system using the Claude Agent SDK. A coordinator agent delegates to specialized subagents: one searches the web, one analyzes documents, one synthesizes findings, and one generates reports. The system researches topics and produces comprehensive, cited reports.
When researching "renewable energy adoption," the web search agent returns recent statistics (2024: 35% adoption) while the document analysis agent extracts data from internal reports (2021: 18% adoption). The synthesis agent incorrectly flags these as contradictory sources rather than recognizing the data shows growth over time. What change would best enable the synthesis agent to correctly interpret such temporal differences?

A. Add a conflict resolution agent that automatically discards older data when newer data exists for the same metric.
B. Require subagents to include publication or data collection dates in their structured outputs.
C. Instruct the synthesis agent to always treat the most recent data as authoritative and place older findings in a separate historical appendix.
D. Configure the web search agent to only return results from the past 6 months.

Answer: B

Explanation:
Temporal metadata lets the synthesis agent distinguish measurements from different time periods and interpret 18% in 2021 and 35% in 2024 as a trend rather than a contradiction.

NEW QUESTION # 78
The synthesis agent completes its initial pass but flags that three key research questions remain unanswered because the web search and document analysis agents didn't find relevant information on those specific subtopics. The coordinator currently proceeds directly to report generation, producing reports with incomplete coverage. What change would most effectively improve research completeness?

A. Give the synthesis agent direct access to web search tools so it can autonomously fill knowledge gaps without returning control to the coordinator.
B. Have the coordinator evaluate synthesis output for gaps, then re-delegate to web search and document analysis with targeted queries before invoking synthesis again.
C. Increase the initial breadth of queries sent to web search and document analysis to reduce the probability of missing relevant information.
D. Have the report generation agent note which research questions couldn't be answered, so users understand the limitations of the final output.

Answer: B

Explanation:
The coordinator should treat unanswered research questions as gaps in the workflow and route them back to the appropriate specialist agents with targeted follow-up tasks. This creates an iterative research loop that improves completeness before the final report is generated.

NEW QUESTION # 79
You are using Claude Code to accelerate software development. Your team uses it for code generation, refactoring, debugging, and documentation. You need to integrate it into your development workflow with custom slash commands, CLAUDE.md configurations, and understand when to use plan mode vs direct execution.
Your team has connected a custom MCP server that provides DevOps workflow templates. The server exposes several MCP prompts (such as deploy_checklist and incident_response) in addition to tools. How do these MCP prompts become accessible within Claude Code?

A. They are surfaced as @-mentionable resources alongside files, fetched and attached to your message when referenced.
B. They are automatically prepended to every conversation as additional system-level context, influencing Claude's behavior throughout the session.
C. They appear as slash commands (e.g., mcp_servername_deploy_checklist) that you can invoke, with arguments passed after the command name.
D. They are added to Claude Code's tool registry alongside the server's tools, invoked automatically by the model when relevant to the task.

Answer: C

Explanation:
Claude Code dynamically exposes MCP server prompts as commands using the format
/mcp__servername__promptname, such as /mcp__devops__deploy_checklist. Any prompt arguments are passed afterward, separated by spaces.

NEW QUESTION # 80
After the web search agent and document analysis agent complete their tasks, the coordinator invokes the synthesis agent. However, the synthesis agent responds that it cannot complete the task because no research findings were provided. What is the most likely cause of this issue?

A. The subagents need to share a single API connection to enable automatic context sharing between invocations.
B. The synthesis agent's context window is not large enough to hold the combined outputs from both previous agents.
C. The coordinator did not include the outputs from the previous agents in the synthesis agent's prompt.
D. The synthesis agent needs tools that can fetch results directly from the other agents' conversation histories.

Answer: C

Explanation:
The synthesis agent relies on the coordinator to provide relevant findings from prior subagents. If the coordinator fails to include these outputs in the synthesis prompt, the agent receives no actionable context and cannot produce a meaningful summary or analysis.

NEW QUESTION # 81
Your product search tool queries an external catalog API and returns matching items. In production, you observe the agent frequently retries searches immediately after receiving zero results, treating "no matches found" as a failure requiring retry. The external API returns HTTP 200 with an empty results array - a valid response. How should you restructure the tool's result to help the agent correctly interpret empty result sets?

A. Return a result object with isError: true and a message explaining no products matched.
B. Return a structured result with a success boolean and results array, reserving isError: true for actual execution failures only.
C. Add a suggestions field containing alternative search strategies when results are empty, helping guide the agent toward more productive follow-up queries.
D. Return a natural language string describing the outcome, allowing the agent to interpret the result contextually based on the message content.

Answer: B

Explanation:
A zero-result search is a successful tool execution with an empty result set, not an error.
Returning a structured success indicator with the results array helps the agent distinguish valid
"no matches found" outcomes from actual tool or API failures, preventing unnecessary retries.

NEW QUESTION # 82
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