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Anthropic CCA-F Exam Syllabus Topics:

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

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

2. Your agent is handling a billing dispute. After calling get_customer and lookup_order, it identifies that the dispute involves a promotional pricing error requiring manager approval - beyond the agent's authorization level. How should the workflow handle this mid-process escalation?

A) Compile a structured handoff with customer details, order info, and the identified issue before calling escalate_to_human.
B) Attempt the refund with process_refund anyway, escalating only if the system rejects the transaction.
C) Persist the complete conversation and tool response history to a database, then call escalate_to_human with a reference ID.
D) Call escalate_to_human passing only the customer's original message.

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

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

4. A developer wants Claude to explain every reasoning step internally before answering. Why might this request be inappropriate?

A) Claude requires GPU acceleration.
B) JSON cannot contain reasoning.
C) Temperature prevents explanations.
D) Internal reasoning should not be relied upon as an application feature.

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 asks your agent to identify untested code paths in a legacy payment processing module spanning 45 files. After reading the first 8 source files, the agent's responses are becoming noticeably less accurate - it's forgetting previously discussed code patterns and hasn't yet located all test files or traced critical payment flows. What's the most effective approach to complete this investigation?

A) Spawn subagents to investigate specific questions (e.g., "find all test files for payment processing," "trace refund flow dependencies") while the main agent coordinates findings and preserves high-level understanding.
B) Document all current findings in a summary report, clear context completely, then use that report as the sole reference for continuing the investigation.
C) Clear context with /clear, then selectively re-read only the most critical files discovered so far, writing key findings to a scratchpad file that persists between context resets.
D) Switch to using Grep to search for specific function names instead of reading full files, reducing the content loaded into context for remaining exploration.

Solutions: