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| Section | Weight | Objectives |
|---|---|---|
| Claude Code Configuration & Workflows | 20% | - Hooks vs advisory instructions - Path-specific rules and .claude/rules/ configuration - CI/CD integration and non-interactive mode parameters - CLAUDE.md hierarchy, precedence and @import rules - Custom slash commands and plan mode vs direct execution |
| Context Management & Reliability | 15% | - Context window optimization and prioritization - Token budget management and cost control - Idempotency, consistency and failure resilience - Context pruning and summarization strategies |
| Prompt Engineering & Structured Output | 20% | - Explicit criteria definition and few-shot prompting - Validation, parsing and retry loop strategies - JSON schema design and structured output enforcement - System prompt design and persona alignment |
| Tool Design & MCP Integration | 18% | - Tool schema design and interface boundaries - MCP tool, resource and prompt implementation - Error handling and tool response formatting - Tool distribution and permission controls - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 |
| Agentic Architecture & Orchestration | 27% | - Multi-agent patterns: coordinator-subagent and hub-and-spoke - 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 |
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102. Frage
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?
Antwort: C
Begründung:
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.
103. Frage
You are building a multi-agent research system using the Claude Agent SDK. A coordinator agent delegates to specialized subagents: one searches the web, one analyzes documents, one synthesizes findings, and one generates reports. The system researches topics and produces comprehensive, cited reports.
When researching "renewable energy adoption," the web search agent returns recent statistics (2024: 35% adoption) while the document analysis agent extracts data from internal reports (2021: 18% adoption). The synthesis agent incorrectly flags these as contradictory sources rather than recognizing the data shows growth over time. What change would best enable the synthesis agent to correctly interpret such temporal differences?
Antwort: B
Begründung:
Temporal metadata lets the synthesis agent distinguish measurements from different time periods and interpret 18% in 2021 and 35% in 2024 as a trend rather than a contradiction.
104. Frage
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?
Antwort: D
Begründung:
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.
Option A may reduce wall-clock completion time, but concurrency does not alter the API's per-token price.
Option C changes the task structure and risks exceeding context or output limits, mixing commit-level classifications, complicating retries, and making it harder to associate errors with individual commits. A single large request also does not automatically consume fewer tokens because the model must still process all commit content. Option D violates the requirement to retain the same model tier and output-quality profile.
Batch results may arrive in an order different from submission order, so the pipeline must associate every response with its original commit through custom_id. This provides lower cost without changing the individual review prompts. Anthropic Message Batches documentation
105. Frage
Your code-review prompts include both implementation changes and the corresponding test file, but the review comments fail to identify untested code paths. The model correctly flags functions that have no tests at all, but it fails to recognize when conditional branches or error-handling paths within tested functions lack coverage. What is the most effective way to improve branch-level gap detection without overcomplicating the pipeline?
Antwort: A
Begründung:
The current prompt asks for testing analysis at too high a level. Claude recognizes the obvious absence of an entire test but has not been instructed to construct a path-level inventory. Option B turns the desired behavior into an explicit verification procedure: enumerate each condition, alternative branch, early return, exception handler, and failure path, then locate a test assertion that exercises its behavior.
Anthropic's prompting best practices emphasize clear, specific instructions and explicit sequential steps when a task requires a defined analysis process. This change keeps the existing single review call while making the missing evaluation criterion unambiguous.
Option A improves proximity between implementation and tests but does not tell Claude what coverage relationship to inspect. Option C could work, but it adds orchestration, latency, cost, and another handoff before testing whether a direct instruction solves the observed failure. Option D may improve recognition of examples resembling the demonstration, but a few cases cannot enumerate every branch structure. Explicit path enumeration generalizes across unfamiliar code and creates auditable output: each reported gap can name the uncovered condition, expected behavior, and missing assertion.
106. Frage
What is the PRIMARY purpose of few-shot prompting?
Antwort: D
Begründung:
Few-shot prompting provides representative examples that illustrate the desired task and response style. Claude learns from these examples within the prompt, improving consistency without requiring model retraining.
107. Frage
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