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Anthropic CCAR-F Exam Syllabus Topics:

Anthropic Claude Certified Architect - Foundations Sample Questions:

1. 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 tool definitions consume too much context-window space, leaving insufficient room for task content.
B) The coordinator cannot track which capabilities each subagent has, leading to misrouted tasks.
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.

2. Production monitoring shows that follow-up queries such as "summarize what we learned about market trends" consistently take more than 40 seconds. Investigation reveals that the coordinator spawns the synthesis subagent for every summarization request, passing more than 80,000 tokens of accumulated findings. The coordinator already has these findings in its context from orchestrating the research. What is the most effective way to improve response time for these follow-up summaries?

A) Spawn the synthesis subagent with reduced context and allow it to request specific findings from the coordinator on demand.
B) Enable prompt caching for the synthesis subagent to reduce the overhead of repeatedly transferring the same research findings.
C) Pregenerate and cache summaries at multiple levels of detail whenever new findings accumulate.
D) Have the coordinator answer straightforward summarization requests directly from its existing context, reserving subagent invocation for complex analysis.

3. You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JSON schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Your extraction uses tool use with a JSON schema in which property_type is defined as an enum: house, apartment, condo, or townhouse. After deployment, 8% of extractions fail schema validation. Investigation reveals that listings mention many uncommon property types-"studio," "loft," "duplex," "mobile home,"
"tiny house," and "converted warehouse"-and new types continue appearing regularly.
What is the most effective long-term solution?

A) Add few-shot examples demonstrating how to map unexpected property types to the closest existing enum value.
B) Change property_type from an enum to a free-form string and implement a normalization step in post- processing.
C) Add an other value to the enum with a separate property_type_detail string field for specifics when other is selected.
D) Continuously expand the enum to include newly observed property types and add monitoring for additional edge cases.

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.
You've asked Claude Code to build a PDF report generation feature. The initial implementation queries the database correctly, but the output has formatting issues: table columns are too narrow causing content truncation, dates display without proper formatting, and page break handling is incorrect. You've noticed these issues interact-changing column widths affects how dates render, and page breaks depend on content height.
What's the most effective approach for iterating toward a working solution?

A) Address the column width issue first with specific measurements, verify it works, then fix date formatting within the corrected columns, then adjust page breaks-testing after each change.
B) Start fresh with a detailed prompt specifying all formatting requirements upfront.
C) Show Claude an example of a correctly formatted report and ask it to match that output, rather than listing the specific technical issues.
D) Provide all three issues in a single detailed message with exact specifications for each, allowing Claude to address them together in one update.

5. You are building a customer support resolution agent using the Claude Agent SDK. The agent handles high- ambiguity requests like returns, billing disputes, and account issues. It has access to your backend systems through custom Model Context Protocol (MCP) tools ( get_customer , lookup_order , process_refund , escalate_to_human ). Your target is 80%+ first-contact resolution while knowing when to escalate.
Compliance requires that refunds exceeding $500 must automatically escalate to a human agent-this rule cannot be left to model discretion. Despite clear system prompt instructions, production logs show the agent occasionally processes high-value refunds directly (3% failure rate).
How should you achieve guaranteed compliance?

A) Strengthen the system prompt with emphatic language: "CRITICAL POLICY: Refunds over $500 MUST trigger human escalation. NEVER process these directly."
B) Add few-shot examples to the prompt showing correct escalation behavior at various refund amounts ($400, $500, $600).
C) Modify the refund tool to return an error with message "Amount exceeds policy limit-please escalate" when the threshold is exceeded.
D) Implement a hook to intercept tool calls, when the refund process amount exceeds $500, block it and invoke human escalation.

Solutions: