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| Section | Weight | Objectives |
|---|---|---|
| Tool Design & MCP Integration | 18% | - Tool integration
|
| Prompt Engineering & Structured Output | 20% | - Prompt design
|
| Context Management & Reliability | 15% | - Context handling
|
| Claude Code Configuration & Workflows | 20% | - Claude Code
|
| Agentic Architecture & Orchestration | 27% | - Agentic architecture patterns
|
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NEW QUESTION # 121
Your automated review generates many findings per pull request, but developer feedback shows that roughly half are dismissed as "not worth addressing." Analysis reveals that dismissed findings are often technically accurate but involve minor style preferences or patterns that are acceptable in your codebase. Before adding infrastructure complexity, what prompt-design change could most effectively reduce dismissals while maintaining the detection of genuine issues?
Answer: D
Explanation:
The problem is an undefined relevance threshold. Claude is finding technically valid observations, but the prompt does not clearly distinguish actionable defects from acceptable conventions and low-value style preferences. Option A establishes explicit positive and negative reporting criteria, allowing the model to apply the team's actual definition of a useful finding during generation.
Anthropic's prompt-engineering guidance emphasizes clear, specific instructions and well-defined success criteria. The managed Code Review documentation similarly recommends defining skipped categories, generated paths, severity rules, and evidence requirements. Reporting "bugs affecting correctness or security" while excluding "formatting handled by CI and documented local conventions" is substantially more precise than asking the model to be generally conservative.
Option B adds cost, latency, another probabilistic decision, and a new evaluation surface before improving the original prompt. Option C confuses confidence with importance: Claude may be highly confident about a trivial style observation. Option D is dangerously broad because it can suppress legitimate but uncertain bugs, reducing recall. Explicit relevance criteria address the demonstrated failure directly while preserving the model's ability to investigate and report genuine correctness or security problems.
NEW QUESTION # 122
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 has been running for 3 weeks and human reviewers have corrected 847 extractions.
Analysis reveals a recurring pattern: when recipes use informal measurements like "a handful" or
"a splash," the model either invents specific amounts or leaves fields empty--accounting for 23% of all corrections.
How should you use this feedback to improve extraction accuracy?
Answer: C
Explanation:
The reviewer corrections have exposed a narrow and repeatable interpretation failure. The desired policy is clear: informal measurements are valid source values and must be preserved verbatim rather than normalized into invented quantities or treated as missing. This behavior can be communicated efficiently through targeted few-shot examples.
Anthropic recommends examples for demonstrating expected behavior and improving consistency. Examples can pair source phrases such as "a handful of spinach," "a splash of vinegar," and "a pinch of salt" with outputs that retain handful, splash, and pinch exactly.
Additional counterexamples can show that Claude must not convert these phrases into grams, millilitres, or estimated serving quantities.
NEW QUESTION # 123
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?
Answer: D
Explanation:
Hooks provide deterministic enforcement. Running Prettier automatically after every file edit or write guarantees consistent formatting, whereas instructions, rules, skills, or prompt-based checks still depend on model compliance.
NEW QUESTION # 124
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 caching layer for API responses to speed up the /products endpoint. You have a rough idea-Redis with a 5-minute TTL-but you're new to production caching and aren't sure what other considerations a robust implementation requires. What's the most effective way to start your iterative workflow?
Answer: B
Explanation:
Because the requirements are incomplete and production caching involves hidden design choices, an interview helps surface invalidation, consistency, failure handling, and other trade-offs before implementation. Anthropic recommends this approach for larger features when important requirements may not yet be identified.
NEW QUESTION # 125
Your MCP server includes archive_file(file_id) and delete_file(file_id) tools. Production logs show the agent calls delete_file when users ask to "remove old backups," but company policy requires archiving backup files. Both tools currently have minimal descriptions: "Archives a file" and
"Deletes a file." Which change most directly improves tool selection for this scenario?
Answer: A
Explanation:
Clear tool descriptions directly guide the agent's choice between similar tools. Adding use-case guidance that backup files should not be deleted and should be archived helps the agent map
"remove old backups" to the correct archive operation before execution.
NEW QUESTION # 126
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