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Anthropic Claude Certified Architect – Foundations Sample Questions:

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

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A) Add prompt instructions to always extract the most recent amendment value and ignore superseded original terms.
B) Preprocess documents with a classifier that identifies and removes superseded sections before the main extraction step.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

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.
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) Add routing instructions to the system prompt specifying that dependency-related questions should use MCP tools rather than Grep.
B) Expand MCP tool descriptions to detail capabilities and outputs-e.g., "Builds dependency graph showing direct imports, transitive dependencies, and cycles."
C) 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.
D) Remove Grep from available tools when the MCP server is connected to eliminate functional overlap.

4. 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 invoices and extracts line items, subtotals, tax amounts, and grand totals.
During evaluation, you discover that in 18% of extractions, the sum of extracted line item amounts doesn't match the extracted grand total-sometimes due to OCR errors in the source document, sometimes due to extraction mistakes by the model. Downstream accounting systems reject records with mismatched totals.
What's the most effective approach to improve extraction reliability?

A) Extract line items and totals independently, then use a separate validation model to reconcile discrepancies by determining which extracted values are most likely correct.
B) Add a "calculated_total" field where the model sums extracted line items alongside a "stated_total" field. Flag records for human review when values differ.
C) Add few-shot examples demonstrating invoices where extracted line items sum correctly to the stated total, encouraging the model to produce mathematically consistent extractions.
D) Implement post-processing that automatically adjusts line item amounts proportionally when their sum doesn't match the stated total.

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 pipeline uses a tool called extract_metadata with a JSON schema for paper details. You've also defined lookup_citations and verify_doi tools for enrichment. During testing, you notice that when users include requests like "extract the metadata and tell me how cited it is," Claude sometimes calls lookup_citations first, which fails because it needs the DOI that extract_metadata would provide.
What's the most effective way to ensure structured metadata extraction happens first?

A) Set tool_choice to {"type": "tool", "name": "extract_metadata"} for every API call in the pipeline, ensuring Claude always extracts metadata before any enrichment can occur.
B) Set tool_choice to "any" so Claude must use a tool, combined with system prompt instructions prioritizing extract_metadata .
C) Set tool_choice to "auto" and reorder the tool definitions so extract_metadata appears first in the tools array, since Claude prioritizes earlier-listed tools.
D) Set tool_choice to {"type": "tool", "name": "extract_metadata"} and process the enrichment requests in subsequent turns after receiving the extracted metadata.

Solutions: