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1. To systematically analyze why developers dismiss certain automated code review findings in a quicktechie.com application, which architectural pattern should be implemented?
A) Run a separate classifier model to predict which findings will be ignored.
B) Add a detected_pattern field to the structured findings to track and aggregate dismissal reasons.
C) Execute a multi-pass review on every pull request to ensure consensus.
D) Use the Message Batches API to delay findings until end-of-week review.
2. In a financial application, identity verification must be completed before processing transactions. What is the most architecturally sound method to enforce this prerequisite workflow?
A) Write a capitalized warning in the prompt: 'NEVER process a transaction before verifying identity'.
B) Set tool_choice:"any" so the agent can evaluate the financial risk dynamically before acting.
C) Provide negative few-shot examples in the system prompt demonstrating the agent refusing to process unverified transactions.
D) Implement programmatic prerequisites that block downstream tool calls (e.g., process_transaction) until the identity verification step has successfully completed.
3. Your agent achieves a low first-contact resolution rate because it attempts to autonomously handle complex policy exceptions, while simultaneously escalating straightforward tasks. What is the most effective approach to improve this escalation calibration?
A) Deploy a separate classifier model to predict which requests need escalation before the main agent begins.
B) Add explicit escalation criteria to the system prompt with few-shot examples demonstrating when to escalate versus resolve autonomously.
C) Require the agent to self-report a confidence score (1-10) and route to humans when confidence falls below 7.
D) Implement sentiment analysis to detect frustration levels and automatically escalate when they are high.
4. Using the FastMCP Python SDK, which decorators are used to expose capabilities to an MCP client? Choose 2 correct answers.
A) @mcp.action
B) @mcp.resource
C) @mcp.agent
D) @mcp.tool
5. The data extraction system needs to categorize document types using an enum schema field. However, new and unexpected document types occasionally appear in production. How should you design the schema to handle this ambiguity?
A) Use a rigid enum containing only known document types to force the model to pick the closest match.
B) Include an 'other' enum value paired with a 'document_type_detail' string field, guided by 2-4 few-shot examples covering edge cases.
C) Deploy a PreToolUse hook to intercept and reject any document types not pre-configured in the schema.
D) Use an open-ended string field instead of an enum to maximize flexibility.
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: D | Question # 3 Answer: B | Question # 4 Answer: B,D | Question # 5 Answer: B |
Jeremy
Malcolm
Omar
Ryan
Virgil
Ann
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