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| Section | Weight | Objectives |
|---|---|---|
| Prompt Engineering & Structured Output | 20% | - Prompt design strategies - Structured output generation and validation - Improving Claude response quality and consistency |
| Tool Design & MCP Integration | 18% | - Model Context Protocol (MCP) concepts and integration - Tool safety, reliability, and usability - Designing effective tools for Claude applications |
| Claude Code Configuration & Workflows | 20% | - Developer productivity workflows - Claude Code usage and configuration - Integrating Claude Code into development processes |
| Agentic Architecture & Orchestration | 27% | - Agent coordination and orchestration patterns - Selecting appropriate Claude architectures - Designing agentic systems and workflows |
| Context Management & Reliability | 15% | - Evaluation and reliability strategies - Production deployment considerations - Managing context windows and information flow |
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NEW QUESTION # 107
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.
Your extraction system parses e-commerce product descriptions to extract specifications such as dimensions, weight, and materials into JSON. Despite having a well-defined schema, the model inconsistently extracts the materials field-sometimes returning "cotton blend," other times "Cotton/Polyester mix," and occasionally omitting the field when material information is clearly present in the source.
What is the most effective way to improve extraction consistency?
Answer: B
Explanation:
Option D addresses the observed inconsistency at the model-behavior level. A schema can require that materials be a string, but it cannot teach Claude which lexical form the application considers canonical or demonstrate when a source phrase should populate the field. Anthropic identifies examples as one of the most reliable ways to steer output format, structure, and consistency, recommending several relevant and diverse examples that mirror the real task in its prompting best practices . Complete input-output pairs show both recognition and normalization: for example, a description containing "60% cotton, 40% polyester" can consistently map to the chosen "cotton/polyester blend" representation. They can also include difficult cases where material information is embedded indirectly in prose. Temperature zero reduces sampling variability but does not repair an underspecified transformation rule. A more capable model likewise lacks the missing formatting convention. Making the field required is dangerous because documents may legitimately omit materials; it can force unsupported values and increase hallucinations. Few-shot examples therefore supply the missing decision boundary while preserving truthful absence handling. The examples should be evaluated on held-out descriptions and supplemented by deterministic post-processing if downstream systems require an exact controlled vocabulary.
NEW QUESTION # 108
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.
Your agent is handling a billing dispute. After calling get_customer and lookup_order , it identifies that the dispute involves a promotional pricing error requiring manager approval-beyond the agent's authorization level.
How should the workflow handle this mid-process escalation?
Answer: B
Explanation:
A mid-process escalation should transfer the decision-ready state accumulated by the agent. The human reviewer needs the verified customer identity, relevant order information, the promotional-pricing discrepancy, the reason approval is required, and any actions already attempted. Option B preserves this information in a concise, structured handoff while avoiding unnecessary repetition of the complete raw transcript.
Anthropic's tool-design guidance recommends returning high-signal information and stable identifiers containing only what Claude or the next workflow participant needs to determine the next action. Anthropic's context-engineering guidance similarly advocates structured notes that preserve critical state and dependencies without retaining every redundant tool result. A structured escalation payload applies both principles and reduces handling time for the manager. ( https://platform.claude.com/docs/en/agents-and-tools
/tool-use/define-tools )
Option A discards the investigation already completed. Option C violates the agent's authorization boundary and risks an impermissible financial action. Option D provides auditability, but a reference ID alone forces the human to reconstruct the case from an excessively broad transcript. Human control must remain meaningful when an agent encounters a decision outside its authority; the agent should pause and hand the decision back with sufficient supporting context. ( https://www.anthropic.com/research/trustworthy-agents ) Official references/topics: Structured agent handoffs, high-signal tool results, human-control boundaries, persistent structured state.
NEW QUESTION # 109
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.
After expanding the agent's MCP tools with delivery-specific capabilities ( check_delivery_status , contact_driver , issue_credit , apply_promo_code , update_delivery_address , reschedule delivery ), the total tool count has grown from 4 to 10. Your evaluation suite shows tool selection accuracy has dropped from 88% to 71%. Log analysis reveals the majority of errors involve the agent selecting between semantically overlapping tools - calling issue_credit when process_refund was correct, and calling check_delivery_status when lookup_order already returns the needed data. Which approach structurally eliminates the semantic overlap identified in the logs as the error source?
Answer: A
Explanation:
Merging issue_credit and process_refund into one compensation tool, and folding delivery-status lookup into lookup_order, removes competing tools with overlapping purposes. This directly eliminates the ambiguity causing incorrect tool selection, rather than merely hiding tools, routing between agents, or adding examples.
NEW QUESTION # 110
The automated review consistently flags patterns your team uses intentionally-force-unwrapping optionals in test files, using large coordinator classes that follow your established architecture, and importing internally maintained modules marked as deprecated in the public SDK. Developers are dismissing approximately 30% of all findings as project-specific false positives. Which approach prevents the model from generating these findings in the first place by supplying the project's conventions as persistent context during every review?
Answer: A
Explanation:
Option D supplies the missing repository-specific knowledge before Claude evaluates the code. The project's CLAUDE.md should state that force-unwrapping is permitted within test paths, explain the coordinator-class architecture, and identify the internally maintained deprecated module as an approved dependency. These instructions should be concise and scoped carefully so exceptions do not become global permissions.
Anthropic's CLAUDE.md documentation defines project CLAUDE.md files as persistent context for coding standards, architecture, workflows, and team conventions. They are loaded at the beginning of applicable sessions, preventing the organization from repeatedly supplying the same corrections. Anthropic also recommends recording information that Claude should know whenever it works in the repository.
Option A filters generated findings after tokens have already been consumed and can hide genuine issues containing similar terminology. Option B removes valuable surrounding context and would likely increase false positives and missed cross-file defects. Option C distributes project knowledge across individual source lines, creates maintenance overhead, and requires custom preprocessing. Centralized, narrowly scoped CLAUDE.md instructions address the root cause and improve the initial review judgment instead of repairing its output afterward.
NEW QUESTION # 111
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: C
Explanation:
Project skills are stored at .claude/skills/ < skill-name > /SKILL.md . The directory name becomes the invocable command name, so .claude/skills/migrate-component/SKILL.md creates /migrate-component .
Anthropic distinguishes project skills from personal skills: project skills apply only to the repository, while skills under ~/.claude/skills/ apply to one user across all projects. ( https://code.claude.com/docs/en/skills ) Committing the project skill to version control ensures that every developer receives the same workflow and that changes are reviewed, versioned, and synchronized with the codebase. Anthropic's .claude directory guidance states that project configuration should be committed when it is intended to be shared with the team, whereas files under ~/.claude are personal. ( https://code.claude.com/docs/en/claude-directory ) Option A requires manual duplication across developer machines and allows versions to diverge. Option B loads the migration procedure into every conversation even when no migration is occurring; Anthropic recommends skills for reusable checklists and multi-step procedures because their bodies load only when used. Option C describes no valid mechanism for defining a skill's content in settings.json .
Option D provides the correct scope, command name, on-demand loading behavior, and team distribution model.
Official references/topics: Project skills, SKILL.md structure, command invocation, version-controlled team configuration.
NEW QUESTION # 112
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