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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 agents' role descriptions in their system prompts conflict with having access to tools outside those roles.
C) The coordinator cannot track which capabilities each subagent has, leading to misrouted tasks.
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) Have the coordinator answer straightforward summarization requests directly from its existing context, reserving subagent invocation for complex analysis.
D) Pregenerate and cache summaries at multiple levels of detail whenever new findings accumulate.

3. 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 Claude Code yesterday to investigate authentication flows in a legacy monolith, building up significant context over a 2-hour session. Today she wants to continue that specific investigation. She's worked on three other codebases since then and knows the session was named "auth-deep-dive".
How should she resume?

A) Use --session-id with the UUID from yesterday's session transcript file
B) Start fresh and re-read the same files
C) Use --resume auth-deep-dive to load that specific session by name
D) Use --continue to pick up where the most recent conversation left off

4. 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.
Anthropic's tool use documentation states: "Write instructive error messages. Instead of generic errors like
'failed', include what went wrong and what Claude should try next." A billing dispute agent uses lookup_order , which catches all exceptions and returns a tool_result with is_error: true and the message
"Tool execution failed". Monitoring shows two failure modes: the agent retries the identical call until hitting the turn limit, or it immediately calls escalate_to_human without trying alternative tools.
Which change follows the documented recommendation and gives Claude the information it needs to select the correct recovery action for each error type?

A) Remove is_error: true and return the error details as normal tool content, so Claude reasons about the response as data rather than treating it as a flagged failure condition that biases retry behavior.
B) Add an error classification step in the agentic loop that intercepts tool errors before Claude sees them, then routes to hardcoded retry or escalation logic.
C) Return error-type-specific messages with is_error: true , e.g., "Order not found-try get_customer to search by phone" for data errors and "Database timeout (transient)-retry should succeed" for infrastructure errors.
D) Implement retry logic with exponential backoff inside each tool implementation so transient errors are resolved transparently within the tool before any failure result is surfaced to Claude in the agentic loop.

5. 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 to write a data migration script, but the initial output doesn't correctly handle records with null values in required fields.
What's the most effective way to iterate toward a working solution?

A) Manually edit the generated code to fix the null handling, then continue working with Claude on other parts.
B) Provide a test case with example input containing null values and the expected output, then ask Claude to fix it.
C) Add "think harder about edge cases" to your prompt and request a complete rewrite of the migration logic.
D) Describe the null value problem in detail and ask Claude to regenerate the entire script with improved edge case handling.

Solutions: