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| Section | Weight | Objectives |
|---|---|---|
| Context Management & Reliability | 15% | - Context handling
|
| Agentic Architecture & Orchestration | 27% | - Agentic architecture patterns
|
| Prompt Engineering & Structured Output | 20% | - Prompt design
|
| Claude Code Configuration & Workflows | 20% | - Claude Code
|
| Tool Design & MCP Integration | 18% | - Tool integration
|
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NEW QUESTION # 147
Your order management system requires tools for three distinct operations: issuing refunds (requires amount and reason), canceling orders (requires reason), and requesting reshipments (requires shipping address). Each operation shares an order_id parameter but has different additional requirements. You notice during testing that with your current unified tool design, the agent frequently omits required parameters or includes irrelevant ones. What design change will most effectively improve parameter accuracy?
Answer: B
Explanation:
Separate tools with operation-specific schemas make the required parameters clear and reduce ambiguity during tool selection. This improves parameter accuracy because each tool exposes only the fields needed for that exact action, such as refund amount and reason, cancellation reason, or reshipment address.
NEW QUESTION # 148
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?
Answer: A
Explanation:
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. Option A risks creating an oversized negative catalogue that consumes context, becomes difficult to maintain, and cannot anticipate every future variation. Option C filters text after generation and may suppress genuine findings that happen to use the selected keywords. Option D is dangerously vague: telling a reviewer to be conservative can suppress real but uncertain defects and reduce recall. The prompt should provide paired acceptable/problematic examples, explain why each classification differs, and require concrete code evidence for every reported finding. Anthropic prompting best practices
NEW QUESTION # 149
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 frequently migrates React components to Vue. You've written a step-by-step workflow for Claude Code to follow during each migration, and you want every developer on the team to invoke it by typing /migrate-component. The workflow should stay in sync as the team iterates on it. Where should you place the skill file?
Answer: D
Explanation:
A project-scoped skill is available to everyone working in that repository and stays synchronized through version control. The skill directory name exposes it as /migrate-component.
NEW QUESTION # 150
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 extraction pipeline validates outputs against JSON schemas, but you need to implement human review given limited reviewer capacity (they can handle approximately 5% of total extraction volume).
What's the most effective basis for selecting which extractions to route for human review?
Answer: B
Explanation:
Limited review capacity should be concentrated on records with the highest probability of semantic error. Schema validation confirms that the response has the correct structure and data types; it does not establish that the extracted values are accurate. Ambiguous wording, contradictory passages, missing evidence, and model-reported uncertainty are direct indicators that an extraction requires human judgment.
Anthropic's reliability guidance recommends permitting Claude to express uncertainty, grounding factual outputs in source material, and validating critical information because hallucination- reduction methods do not eliminate errors completely. These principles support routing uncertain or evidentially conflicted records to reviewers rather than treating syntactically valid output as automatically trustworthy.
NEW QUESTION # 151
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 # 152
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