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's exploration subagent spent 30 minutes analyzing a legacy payment system, reading 47 files and documenting data flows. The session was interrupted when the engineer's connection dropped. While away, a teammate merged a PR that renamed two utility functions. The engineer wants to continue the same exploration.
What's the most effective approach?

A) Resume the subagent from its previous transcript without mentioning the changes-the architecture understanding remains valid.
B) Launch a fresh subagent and include the prior transcript in the initial prompt for context.
C) Resume the subagent from its previous transcript and inform it about the renamed functions.
D) Launch a fresh subagent with a summary of prior findings.

2. 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.
The system routes documents with extraction confidence below 85% to human review. A quarterly audit reveals that 12% of high-confidence extractions (#85%) also contain errors-cases where the model finds plausible-but-incorrect values. Error sources vary: comparison tables showing competitor specs, appendices referencing different product variants, and ambiguous phrasing the model misinterprets. You need a sustainable strategy to catch these high-confidence errors and measure whether improvements reduce the error rate over time.
What approach is most effective?

A) Implement stratified random sampling reviewing a fixed percentage of high-confidence extractions weekly, enabling error rate measurement and novel pattern detection.
B) Implement heuristic rules that flag documents containing comparison tables or appendices for review regardless of confidence score.
C) Lower the confidence threshold from 85% to 70%, routing a larger volume of extractions to human review.
D) Add a verification pass that re-extracts from each high-confidence document, flagging cases where the two extraction attempts produce different results.

3. 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.
Your team frequently migrates React components to Vue. You've written a step-by-step workflow for Claude Code to follow during each migration, and you want every developer on the team to invoke it by typing
/migrate-component . The workflow should stay in sync as the team iterates on it.
Where should you place the skill file?

A) As a detailed instruction block in the project's root CLAUDE.md file.
B) In .claude/skills/migrate-component/SKILL.md at the project root, committed to version control.
C) In the project's .claude/settings.json using a skillOverrides entry to register and define the workflow.
D) In ~/.claude/skills/migrate-component/SKILL.md on each developer's machine.

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.
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 Grep to search for "invalidate" and "expire" patterns across all files, then Read only those specific line ranges with minimal surrounding context.
B) 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.
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.

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.
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 agent follows a pre-configured decision tree mapping order attributes to specific tool calls.
D) The orchestration layer automatically routes to the next tool based on the order's status field.

Solutions: