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Anthropic CCA-F Exam Overview:

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

1. You are building developer productivity tools using the Claude Agent SDK. The agent helps engineers explore unfamiliar codebases, understand legacy systems, generate boilerplate code, and automate repetitive tasks. It uses the built-in tools (Read, Write, Bash, Grep, Glob) and integrates with Model Context Protocol (MCP) servers.
Your agent needs to insert a new helper function into the middle of a 150-line utility module, between two existing functions. The Edit tool fails because its old_string parameter cannot find unique text to match - the file has repetitive docstrings, variable names, and structural patterns.
What's the most reliable way to complete this insertion?

A) Use Bash to append the function definition to the end of the file using heredoc syntax
B) Use Edit's replace_all parameter to target a common pattern and embed the new function in the replacement text
C) Use Read to load the file, add the function at the appropriate location, then Write the updated file
D) Use Edit with an extremely long old_string capturing 30+ lines of context to guarantee uniqueness

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

3. 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.
A user is expanding the research system beyond its single web search agent by adding specialized data sources. They add a financial API agent that returns structured JSON with revenue, margins, and growth rates; a news monitoring agent that returns prose summaries of recent developments; and a patent analysis agent that returns structured lists of technology areas. The synthesis agent combines these into executive briefings. Currently, it converts everything to bullet points, causing financial comparisons to lose tabular clarity and news summaries to lose narrative flow. What change would most improve briefing quality?

A) Update the synthesis agent to render each content type appropriately - financial data as tables, news as prose
B) Standardize all subagent outputs to JSON with fields for claim, evidence, source, and confidence
C) Standardize all subagent outputs to prose summaries with inline citations
D) Add a format conversion layer between subagents and synthesis that transforms all outputs to a common intermediate representation

4. A company is building its first production Claude application. Which principle should guide the initial deployment?

A) Optimize only for response speed.
B) Disable monitoring to reduce costs.
C) Start with a simple design and iteratively evaluate improvements.
D) Maximize prompt complexity immediately.

5. Production monitoring shows that follow-up queries like "summarize what we learned about market trends" consistently take 40+ seconds. Investigation reveals the coordinator spawns the synthesis subagent for each summarization request, passing 80K+ tokens of accumulated findings. The coordinator already has these findings in its context from orchestrating the research.
What's the most effective way to improve response time for these follow-up summaries?

A) Enable prompt caching on the synthesis subagent to reduce the overhead of repeatedly transferring the same research findings.
B) Pre-generate and cache summaries at multiple granularities whenever new findings accumulate.
C) Spawn the synthesis subagent with reduced context and have it request specific findings from the coordinator on-demand.
D) Have the coordinator handle straightforward summarization requests directly using its existing context, reserving subagent spawning for complex analysis.

Solutions: