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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prompt Engineering & Structured Output | 20% | - Validation, parsing and retry loop strategies - JSON schema design and structured output enforcement - Explicit criteria definition and few-shot prompting - System prompt design and persona alignment |
| Topic 2: Context Management & Reliability | 15% | - Token budget management and cost control - Context pruning and summarization strategies - Context window optimization and prioritization - Idempotency, consistency and failure resilience |
| Topic 3: Tool Design & MCP Integration | 18% | - MCP tool, resource and prompt implementation - Error handling and tool response formatting - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - Tool distribution and permission controls - Tool schema design and interface boundaries |
| Topic 4: Agentic Architecture & Orchestration | 27% | - Task decomposition and dynamic subagent selection - Agentic loop design and stop_reason handling - Session state management and workflow enforcement - Multi-agent patterns: coordinator-subagent and hub-and-spoke - Error recovery, guardrails and safety patterns |
| Topic 5: Claude Code Configuration & Workflows | 20% | - CLAUDE.md hierarchy, precedence and @import rules - Hooks vs advisory instructions - Path-specific rules and .claude/rules/ configuration - Custom slash commands and plan mode vs direct execution - CI/CD integration and non-interactive mode parameters |
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130. 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.
After deploying automated code review, developers report that approximately 35% of findings are false positives following consistent patterns: style suggestions that contradict team conventions, security warnings for patterns that are safe in the deployment environment, and performance suggestions that would degrade this particular use case.
You want to reduce false positives while enabling the model to generalize its judgment to novel code patterns it has not seen before.
Which approach is most effective?
Antwort: D
Begründung:
Option B demonstrates the decision boundary Claude must learn. Carefully selected examples can show structurally similar code producing different outcomes based on project context--for example, an approved authentication wrapper versus an unsafe direct call, or a deliberate performance trade- off versus an accidental quadratic operation. These contrasts help Claude apply the underlying judgment to new code rather than merely memorizing prohibited phrases.
Anthropic identifies relevant, diverse, and clearly structured examples as one of the most reliable methods for improving output accuracy and consistency. It recommends several examples that mirror the real use case and cover important edge conditions.
131. 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.
The coordinator agent has AgentDefinition objects configured for all four specialized subagents, each with appropriate descriptions, prompts, and tool restrictions. During testing, you notice that the coordinator correctly reasons about when to delegate-it generates messages such as, "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?
Antwort: A
Begründung:
Option C matches the distinction between reasoning about delegation and executing it. Defining subagents makes their descriptions available for selection, but the coordinator must still invoke the SDK's subagent- spawning tool. Current Claude Agent SDK documentation calls this the Agent tool; Task was its earlier name and remains relevant to older SDK configurations. Anthropic's Subagents in the SDK documentation instructs developers to include Agent in allowedTools so subagent invocations are approved automatically. Without that permission, an invocation can fall through to a permission callback or be denied under a non-interactive permission mode. Option A is unlikely because the configured subagent descriptions already tell Claude when each agent should be selected, although explicit prompting can improve invocation reliability. Option B misstates context isolation: context must be included in the spawning prompt, but that issue occurs after an invocation is attempted and does not explain the absence of all subagent executions. Option D would normally produce truncation evidence or incomplete output rather than consistent verbal promises with no tool call. The configuration should therefore permit Agent, explicitly request delegation where necessary, and log subagent invocation events.
132. Frage
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, and Glob-and integrates with Model Context Protocol (MCP) servers.
Your agent needs to insert a new helper function into the middle of a 150-line utility module, between two existing functions. The Edit tool fails because its old_string parameter cannot find unique text to match-the file has repetitive docstrings, variable names, and structural patterns.
What is the most reliable way to complete this insertion?
Antwort: C
Begründung:
Option D is the closest match to Claude Code's documented editing procedure. The Claude Code tools reference explains that Edit performs exact string replacement and requires old_string to appear exactly once.
When the text occurs multiple times, the prescribed response is to include sufficient surrounding context to identify one occurrence uniquely. For this insertion, the match should span a distinctive boundary between the preceding function and the following function. It does not literally need 30 lines; it needs the smallest exact block that is demonstrably unique, but option D is the only answer expressing that method.
Option A would modify every occurrence of a repeated pattern and could insert the helper function multiple times. Option B places the function at the end rather than at the required architectural location. Option C can technically work, but Write replaces the complete file and increases the change surface. Anthropic explicitly states that Write creates or overwrites full files, while partial modifications should use Edit. A targeted, uniquely anchored Edit preserves all unrelated content and produces a smaller, safer diff.
133. Frage
A user expands the research system beyond its original web-search agent by adding specialized data sources.
A financial API agent returns structured JSON containing revenue, margins, and growth rates. A news- monitoring agent returns prose summaries of recent developments. A patent-analysis agent returns structured lists of technology areas. The synthesis agent combines these results into executive briefings. Currently, it converts everything into bullet points, causing financial comparisons to lose tabular clarity and news summaries to lose their narrative flow. What change would most improve briefing quality?
Antwort: C
Begründung:
Option C preserves the semantic strengths of each information type instead of forcing heterogeneous evidence into one presentation structure. Financial values share comparable fields and therefore belong in a table. News developments require connected prose to preserve chronology and causality. Patent technology areas are naturally represented as grouped or prioritized lists.
Anthropic's prompting best practices recommend giving explicit output-format instructions and matching the requested format to the intended communication goal. The synthesis prompt should therefore include rendering rules for each recognized content type while maintaining common provenance fields behind the presentation.
Option A damages numerical comparability and makes trends harder to scan. Option B can be useful as an internal exchange schema, but presenting the entire briefing as uniform JSON does not produce an effective executive document. Option D normalizes transport but does not correct the synthesis agent's bullet-only rendering policy; a common representation can still be displayed badly. The correct design separates structured internal interchange from audience-facing presentation. The synthesis agent should preserve citations and metadata while selecting the most legible form for each class of evidence.
134. Frage
The web search agent has gathered several relevant sources for a research topic. The document analysis agent now needs to examine these sources. How does information typically flow between these two specialized subagents?
Antwort: A
Begründung:
In a coordinated multi-agent pipeline, the coordinator mediates information flow. It collects outputs from the web search agent and includes the relevant findings in the prompt when invoking the document analysis agent, ensuring proper sequencing and context without tightly coupling the subagents.
135. Frage
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