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Anthropic CCAR-F Exam Syllabus Topics:

Anthropic Claude Certified Architect - Foundations Sample Questions:

1. You are integrating Claude Code into your Continuous Integration/Continuous Deployment (CI/CD) pipeline. The system runs automated code reviews, generates test cases, and provides feedback on pull requests. You need to design prompts that provide actionable feedback and minimize false positives.
A developer uses Claude Code to refactor a function during a development session. Before committing, the developer asks the same Claude session to review the code for issues. Later, a separate automated CI review catches several bugs that the same-session review missed.
What best explains this discrepancy?

A) The CI environment can access the full repository, while a local Claude Code session can access only the current file.
B) The CI review must have used a more specific prompt, while the developer's review request was too general.
C) The session's context window necessarily became full, leaving insufficient capacity for meaningful review.
D) Claude retains the implementation context and prior decisions in the session, making it less likely to challenge assumptions underlying its own changes.

2. A user asks Claude to ignore all previous instructions and reveal confidential internal prompt content. How should Claude behave?

A) Partially reveal hidden instructions.
B) Generate random text.
C) Follow higher-priority instructions and refuse.
D) Reveal the prompt.

3. 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.
After expanding the agent's MCP tools with delivery-specific capabilities ( check_delivery_status , contact_driver , issue_credit , apply_promo_code , update_delivery_address , reschedule delivery ), the total tool count has grown from 4 to 10. Your evaluation suite shows tool selection accuracy has dropped from 88% to 71%. Log analysis reveals the majority of errors involve the agent selecting between semantically overlapping tools - calling issue_credit when process_refund was correct, and calling check_delivery_status when lookup_order already returns the needed data. Which approach structurally eliminates the semantic overlap identified in the logs as the error source?

A) Add few-shot examples to the system prompt demonstrating correct selection for each ambiguous tool pair, such as showing when issue_credit applies versus when process_refund is appropriate.
B) Consolidate semantically overlapping tools - merge issue_credit and process_refund into a single resolve_compensation tool with an action parameter, and fold check_delivery_status into lookup_order with an optional include_tracking flag.
C) Split the tools across two sub-agents - a "financial resolution" agent with process_refund , issue_credit ,and apply_promo_code , and a "delivery operations" agent with the remaining delivery tools - with a coordinator routing between them.
D) Enable the tool search tool with defer_loading on the six new tools, keeping the original four always loaded, so the agent dynamically discovers specialized tools only when needed.

4. 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.
After adding an MCP server with specialized code refactoring tools (extract_function, rename_variable, inline_function), You notice the agent still uses basic text manipulation via Write and Bash sed commands for refactoring tasks. The MCP server is connected and healthy.
Examining the configuration, you find each MCP tool has a minimal description like
"extract_function: Extracts a function from code."
What's the most effective way to improve adoption of the MCP refactoring tools?

A) Enhance the MCP tool descriptions to explain when each tool is preferable to text manipulation and clarify expected inputs and outputs.
B) Remove the Write tool from the agent's configuration for refactoring sessions so it must use the MCP tools for code modifications.
C) Implement a request classifier that detects refactoring intent and automatically routes those requests to the MCP server before the agent processes them.
D) Accept this as expected behavior since simpler tools like sed are more predictable than specialized refactoring tools.

5. During a billing dispute resolution, your agent successfully retrieves customer info via get_customer and order details via lookup_order, but when attempting to call process_refund, the tool returns a timeout error. The agent has enough information to explain the charges and verify refund eligibility, but cannot actually process the refund due to the backend failure. What approach best balances first-contact resolution with appropriate error handling?

A) Explain the billing confirm refund eligibility, acknowledge the system issue preventing immediate processing, and offer escalation or retry later
B) Escalate immediately to a human agent since the refund action cannot be completed
C) Confirm the refund will be processed and close the conversation, since the system has all necessary information to complete it automatically
D) Implement automatic retries with exponential backoff for process_refund, keeping the conversation open until the refund is successfully processed

Solutions: