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| Section | Weight | Objectives |
|---|---|---|
| Context Management & Reliability | 15% | - Token budget management and cost control - Context window optimization and prioritization - Context pruning and summarization strategies - Idempotency, consistency and failure resilience |
| Claude Code Configuration & Workflows | 20% | - Custom slash commands and plan mode vs direct execution - CI/CD integration and non-interactive mode parameters - Path-specific rules and .claude/rules/ configuration - Hooks vs advisory instructions - CLAUDE.md hierarchy, precedence and @import rules |
| Tool Design & MCP Integration | 18% | - MCP tool, resource and prompt implementation - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - Tool distribution and permission controls - Tool schema design and interface boundaries - Error handling and tool response formatting |
| Agentic Architecture & Orchestration | 27% | - Multi-agent patterns: coordinator-subagent and hub-and-spoke - Error recovery, guardrails and safety patterns - Session state management and workflow enforcement - Task decomposition and dynamic subagent selection - Agentic loop design and stop_reason handling |
| Prompt Engineering & Structured Output | 20% | - JSON schema design and structured output enforcement - System prompt design and persona alignment - Validation, parsing and retry loop strategies - Explicit criteria definition and few-shot prompting |
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NEW QUESTION # 119
When analyzing complex legal cases that cite multiple precedents, the document-analysis subagent processes each precedent sequentially. A landmark case citing 12 precedents takes more than three minutes to analyze completely. What is the most effective way to reduce this latency while preserving the coordinator's ability to monitor and debug the system?
Answer: D
Explanation:
Option A parallelizes independent precedent analysis while preserving centralized control. The coordinator can partition the 12 precedents into balanced groups, provide each worker with identical extraction and citation requirements, monitor completion or failure, and aggregate the structured results before invoking synthesis.
Anthropic's multi-agent research architecture uses an orchestrator-worker pattern in which a lead agent coordinates specialized subagents operating in parallel. Parallel execution is valuable when tasks are substantially independent, as each precedent can be analyzed without waiting for the preceding case.
Maintaining the fan-out at the coordinator also produces a clear execution trace showing each assignment, status, and returned result.
Option B introduces nested delegation and makes tool usage, permissions, failures, and costs harder for the coordinator to observe. Option C compounds those problems through recursive spawning and risks excessive agent and token consumption. Option D may be appropriate for a large distributed processing platform, but it adds infrastructure without inherently improving the coordinator's reasoning-level observability or defining how results are associated with the case. Coordinator-managed parallel workers provide the required latency reduction with the simplest debuggable architecture.
NEW QUESTION # 120
Claude produces occasional factual inaccuracies despite clear prompts. Which architectural enhancement is MOST effective?
Answer: B
Explanation:
Grounding responses with trusted enterprise documents helps Claude generate answers supported by authoritative sources. Retrieval significantly reduces unsupported statements while improving factual consistency across knowledge-intensive applications.
NEW QUESTION # 121
The coordinator agent has AgentDefinitions configured for all four specialized subagents, each with appropriate descriptions, prompts, and tool restrictions. During testing, you notice the coordinator correctly reasons about when to delegate - it generates messages like "I'll ask the web search agent to find sources on this topic" - but no subagent execution ever occurs. The coordinator then proceeds as if the delegation happened and continues with incomplete information. Logs show no errors. What is the most likely cause?
Answer: D
NEW QUESTION # 122
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 implementing a new payment processing module that must follow your project's established patterns for database transactions, error handling, and audit logging. You've identified three existing modules that exemplify these patterns: db_utils.py , error_handlers.py , and audit_logger.py . This is a one-off integration task-these patterns are well-documented in your team wiki and don't need additional project-level documentation.
What's the most effective approach?
Answer: C
Explanation:
Direct @ references provide Claude with the exact implementations it must imitate. Anthropic documents that referencing a file with @ includes the full file content in the conversation, and multiple files can be referenced in one message. This gives Claude immediate access to the real transaction boundaries, exception structures, audit fields, naming conventions, and helper APIs used by the project. ( https://code.claude.com/docs/en
/common-workflows )
Option B is inappropriate because the task is explicitly one-off and the conventions are already documented elsewhere. CLAUDE.md is loaded into every session and should contain concise information that broadly applies to the project. Adding detailed implementation material for a single integration would consume context unnecessarily. Anthropic recommends moving occasional procedures to skills and keeping CLAUDE.
md limited to persistent, widely applicable guidance. ( https://code.claude.com/docs/en/memory ) Option C loses precision because a natural-language summary may omit subtle but important code behavior.
Option D asks Claude to rediscover files that have already been identified, increasing exploration time and context usage.
The most effective prompt should reference all three modules, identify which pattern each demonstrates, specify the new module's required behavior, and request focused tests proving that the established conventions were followed.
Official references/topics: @ file references, rich prompt context, CLAUDE.md scope, pattern-based implementation.
NEW QUESTION # 123
You are integrating Claude Code into your Continuous Integration/Continuous Deployment (CI/CD) pipeline. The system runs automated code reviews, generates test cases, and provides feedback on pull requests. You need to design prompts that provide actionable feedback and minimize false positives.
Your pipeline includes a release-notes generation step that classifies and summarizes approximately 200 commits at the end of each weekly release cycle. Each commit is currently sent as a separate Messages API request using a Sonnet-tier Claude model. The release notes are not needed until the following morning, providing approximately 12 hours of acceptable latency.
Your team must reduce the per-token API cost while retaining the same model, prompts, and output quality.
Which approach satisfies all these constraints?
Answer: C
Explanation:
Option B applies Anthropic's dedicated asynchronous bulk-processing mechanism while preserving the existing model and prompt for every commit. The Message Batches API accepts independent Messages API requests, each identified by a unique custom_id, and charges both input and output usage at 50% of standard API prices. The approximately 12-hour latency allowance makes the release-notes workload well suited to batching because immediate results are unnecessary.
NEW QUESTION # 124
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