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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prompt Engineering & Structured Output | 20% | - Improving Claude response quality and consistency - Structured output generation and validation - Prompt design strategies |
| Topic 2: Tool Design & MCP Integration | 18% | - Tool safety, reliability, and usability - Designing effective tools for Claude applications - Model Context Protocol (MCP) concepts and integration |
| Topic 3: Context Management & Reliability | 15% | - Evaluation and reliability strategies - Managing context windows and information flow - Production deployment considerations |
| Topic 4: Claude Code Configuration & Workflows | 20% | - Claude Code usage and configuration - Developer productivity workflows - Integrating Claude Code into development processes |
| Topic 5: Agentic Architecture & Orchestration | 27% | - Selecting appropriate Claude architectures - Agent coordination and orchestration patterns - Designing agentic systems and workflows |
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170. Frage
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.
Your team's CLAUDE.md includes a rule: "Use 4-space indentation and always run Prettier formatting." Despite this, code reviews reveal that roughly 30% of files Claude Code generates use inconsistent formatting-sometimes 2-space indentation, sometimes missing trailing commas. Adding emphasis ("IMPORTANT: You MUST use Prettier formatting") reduces violations to about 15%, but doesn't eliminate them.
What is the most effective way to ensure all generated code is consistently formatted?
Antwort: D
Begründung:
Formatting is deterministic and should be enforced by deterministic tooling rather than stronger natural- language instructions. A PostToolUse hook executes after a successful tool operation. Matching Edit|Write restricts the hook to file modifications, allowing the edited file path to be passed directly to Prettier.
Anthropic provides this exact pattern in its official hooks guidance: configure a PostToolUse event with an Edit|Write matcher and run Prettier against the modified file. This ensures formatting occurs automatically after every relevant edit instead of depending on Claude remembering to execute a formatter. ( https://code.
claude.com/docs/en/hooks-guide )
Option A still relies on instruction-following and unnecessarily loads procedural material. Option B asks another probabilistic model evaluation to determine whether formatting is correct, even though Prettier can enforce the result directly. A Stop hook also runs later than necessary. Option C improves contextual relevance but does not guarantee compliance; path-scoped rules remain instructions rather than enforcement controls.
The hook should be stored in project settings so the workflow is shared by the team. The formatter's exit status should also be monitored so configuration or syntax failures are visible instead of silently ignored.
Official references/topics: Claude Code Hooks; PostToolUse; Edit|Write Matchers; Deterministic Formatting Enforcement.
171. 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 CI pipeline performs security-focused code reviews on approximately 50 pull requests daily, currently costing $150 per day using the synchronous API. Reviews are non-blocking-developers merge after tests pass and address findings in follow-up commits. You are evaluating the Message Batches API for its 50% cost reduction.
What factor most determines whether batch processing is appropriate for this use case?
Antwort: D
Begründung:
Option C identifies the fundamental trade-off introduced by batch processing: lower cost in exchange for asynchronous completion and potentially substantial latency. Anthropic states that most Message Batches complete within one hour, but results may become available only when all requests finish or after 24 hours, whichever occurs first. Therefore, the workflow must remain useful even if security findings arrive considerably later than they would through synchronous requests.
The reviews are explicitly non-blocking, so batch processing can be suitable if developers can still act on delayed findings through follow-up commits. Option B describes an implementation requirement because batch results may be returned out of submission order, but Anthropic provides a deterministic solution through each request's unique custom_id. Option A incorrectly focuses on near-instant feedback, which batch processing does not provide. Option D is not decisive because batches support independent Messages API requests containing system prompts, tool use, and multi-turn conversation content. The primary decision is therefore whether the maximum practical feedback delay is acceptable to the development workflow.
Anthropic Message Batches documentation
172. Frage
You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JSON schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
After deployment, you find that 12% of extractions contain semantic errors that pass JSON Schema validation-for example, a duration such as "30 minutes" is incorrectly placed in an ingredient-quantity field.
Human reviewers have the capacity to check only 20% of extractions.
Which approach most effectively allocates reviewer attention?
Antwort: C
Begründung:
Option A uses the limited review capacity as a risk-ranking mechanism rather than spending it uniformly.
JSON Schema validation can prove that an output has the expected types and structure, but it cannot detect a semantically misplaced value such as a duration stored as an ingredient quantity. Field-level confidence can identify the exact parts of a record that warrant inspection, provided those scores are calibrated against labeled examples rather than trusted at face value. The labeled validation set must mirror the production distribution and include edge cases; Anthropic's evaluation guidance explicitly recommends task-specific evaluations, representative distributions, measurable criteria, and automated grading where possible. Calibration then converts raw confidence into an empirically tested estimate of error risk, allowing reviewers to inspect the lowest-confidence records or fields first. Option C, random sampling, is useful for unbiased quality measurement and discovering new failure modes, but it does not maximize error detection within a fixed 20 percent review budget. Options B and D target narrow proxies-layout anomalies and missing required values-while the observed errors pass structural validation and may occur in ordinary-looking documents.
The operational design should combine calibrated risk-based review with a smaller random audit sample to detect drift and overconfidence.
173. Frage
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.
Your system must extract event details from calendar invitations and output JSON that strictly conforms to a schema with fields for title, date, time, location, and attendees. Downstream systems reject any malformed or non-conformant JSON.
What approach provides the most reliable schema compliance?
Antwort: C
Begründung:
A tool definition converts the desired extraction structure into an explicit machine-readable contract. Claude returns the event information inside a tool_use block, with the tool arguments corresponding to the properties defined by the tool's input_schema . Anthropic specifies that custom tool parameters are described using JSON Schema, allowing the application to extract the structured arguments directly rather than attempting to recover JSON from ordinary prose. For current implementations, adding strict: true to the tool definition provides guaranteed conformance of tool-call inputs to the declared schema. ( https://docs.anthropic.com/en
/docs/agents-and-tools/tool-use/implement-tool-use )
Options A, B, and D remain prompt-based formatting techniques. They may improve the probability of valid JSON, but none creates the same schema-enforced interface. Prefilling an opening brace constrains the beginning of the response without guaranteeing valid field names, required properties, or data types. Retry logic detects failures only after generation and adds latency. Detailed formatting instructions can still produce malformed or structurally incorrect output.
Anthropic now also provides Structured Outputs for direct, schema-validated JSON responses. Within the options presented, however, a schema-defined tool is the only approach that establishes an explicit structured- output boundary rather than relying primarily on text-generation compliance. ( https://docs.anthropic.com/en
/docs/test-and-evaluate/strengthen-guardrails/increase-consistency )
Official references/topics: Tool Definitions, JSON Schema Input Contracts, Strict Tool Use, Structured Outputs.
174. Frage
The synthesis agent completes its initial pass but flags that three key research questions remain unanswered because the web-search and document-analysis agents did not find relevant information on those specific subtopics. The coordinator currently proceeds directly to report generation, producing reports with incomplete coverage. What change would most effectively improve research completeness?
Antwort: D
Begründung:
Option A implements an iterative orchestrator-worker loop in which synthesis is treated as an evaluation checkpoint rather than an irreversible transition to report generation. When synthesis identifies missing evidence, the coordinator can formulate focused follow-up tasks, send them to the agents with the appropriate tools, and repeat synthesis after receiving the additional findings.
Anthropic's multi-agent research architecture follows this pattern: the lead agent synthesizes subagent findings and determines whether further research is required. If gaps remain, it creates additional subagents or refines the research strategy. This preserves specialization and centralized control.
Option B makes the incompleteness visible but does not improve research coverage. Option C may increase initial cost without guaranteeing that the specific gaps discovered during synthesis will be addressed. Option D weakens separation of concerns by giving the synthesis agent search capabilities that belong to the research specialists. Targeted redelegation is more efficient because the second research round is informed by concrete deficiencies rather than speculative breadth. It also allows the coordinator to track completeness explicitly before authorizing final report generation.
175. Frage
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