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Anthropic CCAR-F Exam Syllabus Topics:

Anthropic Claude Certified Architect - Foundations Sample Questions:

Question 1

An organization wants predictable outputs for automated invoice classification. Which API parameter should generally be LOWER?

A. Prompt length
B. Context window
C. Max tokens
D. Temperature

Question 2

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, and Glob--and integrates with Model Context Protocol (MCP) servers.
You are building a security-scanning workflow.
When engineers need to locate every occurrence of a dangerous function such as eval() across a large codebase, which tool should the agent use for content searching?

A. Use Grep to search for the regular-expression pattern eval\( across all files in the codebase.
B. Read the project's main entry file and follow import statements to trace where eval() might be used.
C. Use Glob with a pattern such as **/eval* to locate files, and then read each matching file.
D. Use Bash to run ls -R | grep eval and search the recursively listed filenames.

Question 3

Users report that final reports sometimes lack depth on specific subtopics. Investigation shows that the document-analysis agent frequently identifies evidence gaps--for example, noting that
"the retrieved sources discuss API authentication but lack details about token-refresh patterns." Under the current strict pipeline, this insight is not actionable because searching has already finished. What is the most effective architectural change?

A. Have the coordinator look for general gap indicators in the analysis output and run additional searches without repeating the analysis stage.
B. Add a research-planning agent before the initial search phase to decompose every topic into detailed subquestions.
C. Require the analysis agent to return specific evidence gaps to the coordinator, which launches targeted searches and invokes analysis again until the defined coverage criteria are satisfied.
D. Have the synthesis agent assign confidence scores to each report section and flag insufficiently supported sections for manual review.

Question 4

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

Question 5

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.
An engineer used the agent yesterday to analyze a legacy authentication module, identifying two distinct refactoring approaches: extracting a microservice versus refactoring in-place. Today, they want to explore both approaches in depth - having the agent propose specific code changes for each - before deciding which to implement. What's the most effective way to structure this exploration?

A. Resume yesterday's session to explore the first approach, then start a new session for the second, manually recreating the original context.
B. Resume yesterday's session and explore both approaches sequentially within the same conversation thread.
C. Use fork_session to create two branches from yesterday's analysis, exploring one approach in each fork.
D. Start two fresh sessions, manually providing a summary of yesterday's analysis findings to establish context.

Solutions: