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| Section | Weight | Objectives |
|---|---|---|
| Agentic Architecture & Orchestration | 27% | - Error recovery, guardrails and safety patterns - Agentic loop design and stop_reason handling - Session state management and workflow enforcement - Task decomposition and dynamic subagent selection - Multi-agent patterns: coordinator-subagent and hub-and-spoke |
| Claude Code Configuration & Workflows | 20% | - Custom slash commands and plan mode vs direct execution - CI/CD integration and non-interactive mode parameters - Hooks vs advisory instructions - CLAUDE.md hierarchy, precedence and @import rules - Path-specific rules and .claude/rules/ configuration |
| Prompt Engineering & Structured Output | 20% | - Explicit criteria definition and few-shot prompting - Validation, parsing and retry loop strategies - System prompt design and persona alignment - JSON schema design and structured output enforcement |
| Tool Design & MCP Integration | 18% | - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - Tool schema design and interface boundaries - MCP tool, resource and prompt implementation - Tool distribution and permission controls - Error handling and tool response formatting |
| Context Management & Reliability | 15% | - Idempotency, consistency and failure resilience - Context pruning and summarization strategies - Context window optimization and prioritization - Token budget management and cost control |
Question 1
Your order management system requires tools for three distinct operations: issuing refunds (requires amount and reason), canceling orders (requires reason), and requesting reshipments (requires shipping address). Each operation shares an order_id parameter but has different additional requirements. You notice during testing that with your current unified tool design, the agent frequently omits required parameters or includes irrelevant ones. What design change will most effectively improve parameter accuracy?
A. Keep one unified tool but add JSON Schema if-then-else conditionals to enforce that parameters like amount are required only when the operation type is "refund".
B. Keep one unified tool with a nested operation_details object parameter whose internal structure varies by operation type, documented in the tool description.
C. Keep one unified tool with all parameters marked optional, but add detailed few-shot examples in the system prompt showing correct parameter combinations for each operation type.
D. Split into three separate tools (issue_refund, cancel_order, request_reshipment), each defining only the parameters required for that specific operation.
Question 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 used the agent yesterday to analyze a legacy authentication module, identifying two distinct refactoring approaches: extracting a microservice versus refactoring in-place. Today, they want to explore both approaches in depth - having the agent propose specific code changes for each - before deciding which to implement. What's the most effective way to structure this exploration?
A. Resume yesterday's session and explore both approaches sequentially within the same conversation thread.
B. Start two fresh sessions, manually providing a summary of yesterday's analysis findings to establish context.
C. Use fork_session to create two branches from yesterday's analysis, exploring one approach in each fork.
D. Resume yesterday's session to explore the first approach, then start a new session for the second, manually recreating the original context.
Question 3
After 30+ turns, your conversational assistant shows noticeably slower responses and occasionally produces less coherent outputs. Investigation reveals: (1) average conversations reach 50,000 tokens by turn 35, (2) production logs show 94% of user messages only reference the previous 3-5 exchanges, (3) the 6% of queries referencing earlier context typically ask about information the user could easily re-state. Your goal is to improve response speed and quality while maintaining good user experience. What's the most effective approach?
A. Build a retrieval system that stores all conversation turns and uses semantic search to pull in relevant historical context only when the current query appears to reference past information.
B. Enable prompt caching and continue sending the complete conversation history, using cached prefixes to reduce per-request costs while preserving all context.
C. Implement a sliding window keeping only the system prompt and last 8-10 turns. When users reference earlier context, acknowledge the limitation and ask them to re-state the relevant information.
D. Implement a summarization layer that progressively compresses older conversation turns into a running summary while keeping the most recent 5-6 turns verbatim, maintaining full historical context in condensed form.
Question 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.
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. Implement heuristic rules that flag documents containing comparison tables or appendices for review regardless of confidence score.
C. Add a verification pass that re-extracts from each high-confidence document, flagging cases where the two extraction attempts produce different results.
D. Lower the confidence threshold from 85% to 70%, routing a larger volume of extractions to human review.
Question 5
Claude produces occasional factual inaccuracies despite clear prompts. Which architectural enhancement is MOST effective?
A. Increase temperature.
B. Add grounding through trusted retrieval.
C. Remove examples.
D. Shorten responses only.
Solutions:
| Question 1 Answer: D | Question 2 Answer: C | Question 3 Answer: D | Question 4 Answer: A | Question 5 Answer: B |
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