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Anthropic CCAR-F Exam Syllabus Topics:

Anthropic Claude Certified Architect - Foundations Sample Questions:

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

A) Both agents automatically access a shared memory store in which the web-search agent writes its findings.
B) The coordinator receives the web-search agent's output and includes the relevant sources and findings in the prompt used to invoke the document-analysis agent.
C) The web-search agent directly invokes the document-analysis agent and passes the discovered sources as parameters.
D) The agents communicate through an event-driven message queue, with the document-analysis agent subscribing to web-search completion events.

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

3. You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JavaScript Object Notation (JSON) schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
The system routes documents with extraction confidence below 85% to human review. A quarterly audit reveals that 12% of high-confidence extractions (#85%) also contain errors-cases where the model finds plausible-but-incorrect values. Error sources vary: comparison tables showing competitor specs, appendices referencing different product variants, and ambiguous phrasing the model misinterprets. You need a sustainable strategy to catch these high-confidence errors and measure whether improvements reduce the error rate over time.
What approach is most effective?

A) Lower the confidence threshold from 85% to 70%, routing a larger volume of extractions to human review.
B) Implement heuristic rules that flag documents containing comparison tables or appendices for review regardless of confidence score.
C) Implement stratified random sampling reviewing a fixed percentage of high-confidence extractions weekly, enabling error rate measurement and novel pattern detection.
D) Add a verification pass that re-extracts from each high-confidence document, flagging cases where the two extraction attempts produce different results.

4. 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's CLAUDE.md includes a rule: "Use 4-space indentation and always run Prettier formatting." Despite this, code reviews reveal that roughly 30% of files Claude Code generates use inconsistent formatting-sometimes 2-space indentation, sometimes missing trailing commas. Adding emphasis ("IMPORTANT: You MUST use Prettier formatting") reduces violations to about 15%, but doesn't eliminate them.
What is the most effective way to ensure all generated code is consistently formatted?

A) Split the formatting rules into path-scoped .claude/rules/ files that load when Claude works on matching file types.
B) Extract the formatting rules into a dedicated skill that Claude loads automatically when generating code, with more detailed examples of correct formatting.
C) Add a Stop hook with a prompt-based check that evaluates whether generated code follows formatting standards and prompts Claude to fix violations.
D) Configure a PostToolUse hook with an Edit|Write matcher that automatically runs Prettier on each file Claude modifies.

5. 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.
You have configured the system so that all four subagents have access to the complete set of 18 tools. During testing, agents frequently call tools outside their specialization-the synthesis agent attempts web searches, and the report generator tries to analyze documents.
What is the primary cause of this poor tool-selection behavior?

A) The coordinator cannot track which capabilities each subagent has, leading to misrouted tasks.
B) The tool definitions consume too much context-window space, leaving insufficient room for task content.
C) The agents' role descriptions in their system prompts conflict with having access to tools outside those roles.
D) Choosing from 18 tools instead of four or five relevant tools increases decision complexity beyond reliable selection thresholds.

Solutions: