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Anthropic Claude Certified Architect – Foundations Sample Questions:

1. 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.
The agent verifies customer identity through a multi-step process before resetting passwords. During testing, you notice that after the customer answers the third verification question, the agent asks them to provide their name again, as if the earlier exchange never happened.
What's the most likely cause of this behavior?

A) The conversation history isn't being passed in subsequent API requests.
B) The verification tool is clearing the agent's internal state after each successful validation step.
C) Claude's memory retention is limited to two conversational turns by default, requiring explicit configuration to extend it.
D) The prompt lacks instructions telling Claude to remember information across multiple exchanges.

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.
Your extraction pipeline validates outputs against JSON schemas, but you need to implement human review given limited reviewer capacity (they can handle approximately 5% of total extraction volume).
What's the most effective basis for selecting which extractions to route for human review?

A) Route extractions for review only when downstream systems report data quality issues or processing failures.
B) Randomly sample 5% of extractions for review.
C) Route extractions where the model indicates low confidence or where source documents contain ambiguous or contradictory information.
D) Route extractions containing specific high-priority entity types (e.g., financial figures, dates) for human review, regardless of extraction confidence.

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.
A customer raises three separate issues during one session: a refund inquiry (turns 1-15), a subscription question (turns 16-30), and a payment method update (turns 31-45). At turn 48, the customer asks "What happened with my refund?" The conversation is approaching context limits.
What strategy best maintains the agent's ability to address all issues throughout the session?

A) Implement sliding window context that retains the most recent 30 turns.
B) Rely on MCP tools to re-fetch relevant information on demand when the customer references earlier issues.
C) Summarize earlier turns into a narrative description, preserving full message history only for the active issue.
D) Extract and persist structured issue data (order IDs, amounts, statuses) into a separate context layer.

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.
After integrating a local MCP server providing code analysis tools ( analyze_dependencies , find_dead_code , calculate_complexity ), you verify the server is healthy and tools appear in the tools/list response. However, you observe that the agent consistently uses Grep to search for import statements instead of calling analyze_dependencies -even when users explicitly ask about "code dependencies." Examining tool definitions reveals:
* MCP analyze_dependencies - "Analyzes dependency graph"
* Built-in Grep - "Search file contents for a pattern using regular expressions. Returns matching lines with line numbers and surrounding context." What's the most effective approach to improve the agent's selection of MCP tools?

A) Remove Grep from available tools when the MCP server is connected to eliminate functional overlap.
B) Add routing instructions to the system prompt specifying that dependency-related questions should use MCP tools rather than Grep.
C) Expand MCP tool descriptions to detail capabilities and outputs-e.g., "Builds dependency graph showing direct imports, transitive dependencies, and cycles."
D) Split analyze_dependencies into granular tools ( list_imports , resolve_transitive_deps , detect_circular_deps ) so each has a focused purpose less likely to overlap with Grep.

Solutions: