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| Section | Weight | Objectives |
|---|---|---|
| Prompt Engineering & Structured Output | 20% | - JSON schema design and structured output enforcement - Explicit criteria definition and few-shot prompting - System prompt design and persona alignment - Validation, parsing and retry loop strategies |
| Agentic Architecture & Orchestration | 27% | - Session state management and workflow enforcement - Multi-agent patterns: coordinator-subagent and hub-and-spoke - Agentic loop design and stop_reason handling - Error recovery, guardrails and safety patterns - Task decomposition and dynamic subagent selection |
| Claude Code Configuration & Workflows | 20% | - Path-specific rules and .claude/rules/ configuration - CLAUDE.md hierarchy, precedence and @import rules - Hooks vs advisory instructions - Custom slash commands and plan mode vs direct execution - CI/CD integration and non-interactive mode parameters |
| 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 |
| Context Management & Reliability | 15% | - Idempotency, consistency and failure resilience - Token budget management and cost control - Context pruning and summarization strategies - Context window optimization and prioritization |
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74. Frage
After deploying the automated review, you notice high precision but low recall--real bugs are slipping through undetected. Investigation reveals that your review prompt instructs Claude to
"only report high-confidence issues you are certain about" and "err on the side of not commenting." Developers appreciate the low noise, but a race condition that caused a production outage was visible in a reviewed pull request and went unreported. You need to substantially improve bug detection while keeping false-positive rates manageable. What is the most effective approach?
Antwort: D
Begründung:
The prompt's conservative reporting policy is directly causing the low recall. Claude may discover a legitimate race condition during analysis but suppress it because it cannot satisfy the instruction to report only issues about which it is certain. Option C separates two objectives that should not be conflated: broad defect discovery and strict acceptance filtering.
Anthropic's current code-review prompting guidance explicitly recommends reporting every issue, including uncertain or lower-severity findings, assigning confidence and severity metadata, and allowing a separate verification stage to filter them. This maximizes recall while retaining control over developer-facing noise.
75. Frage
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.
A customer contacts the agent about a warranty claim on a power drill. Resolving this requires multiple sequential tool calls: get_customer to look up their account, lookup_order to find the purchase details, and then either process_refund or escalate_to_human depending on warranty eligibility. You're implementing the agentic loop that orchestrates these steps using the Claude API.
What is the primary mechanism your application uses to determine whether to continue the loop or stop?
Antwort: C
76. 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 runs:
PROMPT='You are a code reviewer. Analyze the provided diff for bugs,
security issues, and style violations.'
claude -p \
--dangerously-skip-permissions \
--system-prompt "$PROMPT" \
< diff.txt
The reviews complete and return feedback, but Claude only comments on the piped diff text--it never reads surrounding files in the checked-out repository to understand broader context, even when the diff modifies a function called by many other modules.
Which change to the invocation will cause Claude to inspect related repository files while still applying your custom review instructions?
Antwort: D
Begründung:
Option D preserves Claude Code's standard coding-agent instructions while adding the specialized review criteria. Anthropic documents that --system-prompt replaces the entire default system prompt, including its tool guidance, safety instructions, and coding conventions. It does not technically disable tools, but removing that guidance can make the invocation behave like a narrowly scoped text processor. --append-system-prompt retains the default behavior and layers the review instructions on top.
The prompt should explicitly direct Claude to use Read, Glob, and Grep to inspect definitions, callers, tests, and related modules whenever the diff alone is insufficient.
77. 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.
78. 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 document-analysis agent has a single analyze_document tool that takes a document and a free-text instruction parameter. During evaluation, requests such as "extract the key financial metrics" often return narrative summaries, while "summarize the methodology" sometimes returns raw data tables. The synthesis agent reports that 35% of analysis results require new requests with clarified instructions.
What is the most effective way to improve reliability?
Antwort: C
Begründung:
Option B removes ambiguity at the tool-interface level. Extraction, summarization, and claim verification are different operations with different success criteria and output structures. Separate tools allow each operation to have a precise name, purpose, parameter schema, response contract, and error behavior. Anthropic's guidance on writing effective tools for agents recommends a small set of thoughtful tools targeted at specific, high-impact workflows rather than generic wrappers that force the model to infer operational meaning from loosely structured instructions. Tool descriptions and examples, option A, could improve behavior but leave the overloaded free-text contract intact. An analysis_type enum, option C, identifies the requested mode but still requires one tool to return substantially different result structures, increasing validation and downstream branching. Coordinator pre-classification, option D, adds another probabilistic decision without correcting the interface used by the analysis agent. Purpose-specific tools let the synthesis layer know exactly what output it will receive. Their schemas should require relevant fields-for example, data-point names and source locations for extraction, or claim, verdict, and evidence for verification-and should be tested against the observed failure cases.
79. Frage
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