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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.
You're implementing the escalation logic for when the agent should call escalate_to_human . Your team proposes four different approaches for triggering escalation.
Which approach will most reliably identify cases that genuinely require human intervention?

A) Instruct the agent to escalate when the customer requests a human, when the issue requires policy exceptions, or when the agent cannot make meaningful progress.
B) Configure the agent to escalate after three consecutive tool calls that fail to resolve the customer's stated issue, ensuring a reasonable attempt before involving a human.
C) Implement sentiment analysis that monitors for frustration indicators (negative language, repeated questions, exclamation marks) and triggers escalation when the frustration score exceeds a configured threshold.
D) Build a rules engine that maps specific issue types, customer segments, and product categories to escalation decisions, removing the need for model judgment calls.

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) Lower the confidence threshold from 85% to 70%, routing a larger volume of extractions to human review.
C) Add a verification pass that re-extracts from each high-confidence document, flagging cases where the two extraction attempts produce different results.
D) Implement heuristic rules that flag documents containing comparison tables or appendices for review regardless of confidence score.

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 wants Claude to follow a detailed code review checklist (8 items covering API changes, test coverage, documentation, security, etc.) when reviewing pull requests. The team also uses Claude extensively for other tasks: writing new features, debugging production issues, and generating documentation. Currently, developers paste the checklist at the start of each review session.
Which approach best addresses this workflow need?

A) Add the checklist to the project's CLAUDE.md file under a "Code Review" section.
B) Create a dedicated review subagent with the checklist embedded in its configuration.
C) Create a /review slash command containing the checklist, invoked when starting reviews.
D) Configure plan mode as the default for code review sessions.

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 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) Route extractions where the model indicates low confidence or where source documents contain ambiguous or contradictory information.
C) Randomly sample 5% of extractions for review.
D) Route extractions containing specific high-priority entity types (e.g., financial figures, dates) for human review, regardless of extraction confidence.

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 system has been operating with 100% human review for 3 months. Analysis shows that extractions with model confidence #90% have 97% accuracy overall. To reduce reviewer workload, you plan to automate high- confidence extractions.
Before deploying, what validation step is most critical?

A) Analyze accuracy by document type and field to verify high-confidence extractions perform consistently across all segments, not just in aggregate.
B) Verify that 97% accuracy meets requirements for all downstream systems that consume the extracted data.
C) Compare accuracy at different confidence thresholds (85%, 90%, 95%) to find the optimal cutoff that maximizes automation while minimizing errors.
D) Run a two-week pilot routing 25% of high-confidence extractions directly to downstream systems and monitor error reports.

Solutions: