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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Agentic Architecture & Orchestration | 27% | - Agentic architecture patterns
|
| Topic 2: Tool Design & MCP Integration | 18% | - Tool integration
|
| Topic 3: Context Management & Reliability | 15% | - Context handling
|
| Topic 4: Prompt Engineering & Structured Output | 20% | - Prompt design
|
| Topic 5: Claude Code Configuration & Workflows | 20% | - Claude Code
|
1. 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) Implement post-extraction validation using pattern matching to detect amendments and flag those extractions for manual review.
B) Preprocess documents with a classifier that identifies and removes superseded sections before the main extraction step.
C) Add prompt instructions to always extract the most recent amendment value and ignore superseded original terms.
D) Redesign the schema so amended fields capture multiple values, each with source location and effective date.
2. 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 asks your agent to identify untested code paths in a legacy payment processing module spanning
45 files. After reading the first 8 source files, the agent's responses are becoming noticeably less accurate-it' s forgetting previously discussed code patterns and hasn't yet located all test files or traced critical payment flows.
What's the most effective approach to complete this investigation?
A) Switch to using Grep to search for specific function names instead of reading full files, reducing the content loaded into context for remaining exploration.
B) Clear context with /clear , then selectively re-read only the most critical files discovered so far, writing key findings to a scratchpad file that persists between context resets.
C) Document all current findings in a summary report, clear context completely, then use that report as the sole reference for continuing the investigation.
D) Spawn subagents to investigate specific questions (e.g., "find all test files for payment processing,"
"trace refund flow dependencies") while the main agent coordinates findings and preserves high-level understanding.
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.
You're tasked with adding real-time updates to the application. This could be implemented using WebSockets, Server-Sent Events, or polling, each with different complexity, browser support, and infrastructure requirements.
What's the most effective way to begin this task?
A) Use direct execution with a prompt asking Claude to analyze all approaches and implement the one it determines is best.
B) Enter plan mode to explore the architecture, evaluate trade-offs, and present options for team approval before implementing.
C) Start direct execution with WebSockets, then refactor if infrastructure issues arise.
D) Use direct execution to implement polling first, then evaluate whether to upgrade to WebSockets later.
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.
During a billing dispute resolution, your agent successfully retrieves customer info via get_customer and order details via lookup_order , but when attempting to call process_refund , the tool returns a timeout error.
The agent has enough information to explain the charges and verify refund eligibility, but cannot actually process the refund due to the backend failure.
What approach best balances first-contact resolution with appropriate error handling?
A) Explain the billing, confirm refund eligibility, acknowledge the system issue preventing immediate processing, and offer escalation or retry later.
B) Implement automatic retries with exponential backoff for process_refund , keeping the conversation open until the refund is successfully processed.
C) Confirm the refund will be processed and close the conversation, since the system has all necessary information to complete it automatically.
D) Escalate immediately to a human agent since the refund action cannot be completed.
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.
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) Analyze imports and class hierarchies to identify the base cache class. Read that file to understand the interface, then trace specific invalidation implementations.
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) Use the Read tool to sequentially load all 15 files, building complete understanding across the full caching implementation.
D) 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.
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: D | Question # 3 Answer: B | Question # 4 Answer: A | Question # 5 Answer: A |
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