Anthropic CCAR-F

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Anthropic Claude Certified Architect – Foundations Sample Questions:

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

2. 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're tasked with adding real-time updates to the application. This could be implemented using WebSockets, Server-Sent Events, or polling, each with different complexity, browser support, and infrastructure requirements.
What's the most effective way to begin this task?

A) Use direct execution with a prompt asking Claude to analyze all approaches and implement the one it determines is best.
B) Start direct execution with WebSockets, then refactor if infrastructure issues arise.
C) Enter plan mode to explore the architecture, evaluate trade-offs, and present options for team approval before implementing.
D) Use direct execution to implement polling first, then evaluate whether to upgrade to WebSockets later.

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.
1.5An engineer asks the agent to understand how the caching layer works before adding a new cache invalidation trigger. After initial Grep searches, the agent has identified that caching logic spans 15 files including decorators, middleware, and service classes (~6,000 lines total).
What's the most effective next step for building understanding while managing context constraints?

A) Use Glob to find files matching common caching patterns ( cache*.py , caching/ ), prioritize the largest files by reading them first, then check smaller files for gaps.
B) Use Grep to search for "invalidate" and "expire" patterns across all files, then Read only those specific line ranges with minimal surrounding context.
C) Analyze imports and class hierarchies to identify the base cache class. Read that file to understand the interface, then trace specific invalidation implementations.
D) Use the Read tool to sequentially load all 15 files, building complete understanding across the full caching implementation.

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.
When the agent calls lookup_order and receives order details showing the item was purchased 45 days ago, how does the agentic loop determine whether to call process_refund or escalate_to_human next?

A) The agent executes the remaining steps in a tool sequence planned at the start of the request.
B) The order details are added to the conversation and the model reasons about which action to take.
C) The orchestration layer automatically routes to the next tool based on the order's status field.
D) The agent follows a pre-configured decision tree mapping order attributes to specific tool calls.

5. 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.
Your extraction pipeline processes contracts that frequently include amendments. When a contract contains both original terms and later amendments (e.g., original clause specifies "30-day payment terms" while Amendment 1 changes this to "45 days"), the model inconsistently extracts one value or the other with no indication of which applies.
What's the most effective approach to improve extraction accuracy for documents with amendments?

A) Redesign the schema so amended fields capture multiple values, each with source location and effective date.
B) Add prompt instructions to always extract the most recent amendment value and ignore superseded original terms.
C) Implement post-extraction validation using pattern matching to detect amendments and flag those extractions for manual review.
D) Preprocess documents with a classifier that identifies and removes superseded sections before the main extraction step.

Solutions: