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| Section | Weight | Objectives |
|---|---|---|
| Tool Design & MCP Integration | 18% | - Tool safety, reliability, and usability - Designing effective tools for Claude applications - Model Context Protocol (MCP) concepts and integration |
| Agentic Architecture & Orchestration | 27% | - Agent coordination and orchestration patterns - Selecting appropriate Claude architectures - Designing agentic systems and workflows |
| Prompt Engineering & Structured Output | 20% | - Prompt design strategies - Structured output generation and validation - Improving Claude response quality and consistency |
| Context Management & Reliability | 15% | - Managing context windows and information flow - Evaluation and reliability strategies - Production deployment considerations |
| Claude Code Configuration & Workflows | 20% | - Developer productivity workflows - Claude Code usage and configuration - Integrating Claude Code into development processes |
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NEW QUESTION # 174
Your multi-agent research pipeline crashed after processing12 of 28 documents. The web search agent had identified relevant sources, the document analyzer had partially completed extraction, and the synthesizer had begun pattern identification. You need to resume processing without repeating work or losing fidelity of prior findings. What state management approach best balances information fidelity with context efficiency when restoring agent state?
Answer: C
Explanation:
Persisting structured exports and a coordinator manifest preserves completed work in a reliable, resumable format. On recovery, the coordinator can reload only the relevant state for each agent, avoiding repeated processing while maintaining fidelity of prior findings without flooding prompts with full conversation logs.
NEW QUESTION # 175
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 invoice extraction uses tool use with strict JSON schemas. JSON syntax errors never occur, but 12% of extractions fail semantic validation-for example, line-item amounts do not sum to the extracted total, or vendor IDs do not match valid formats. These failures currently route to manual review.
What is the most effective approach to reduce manual-review volume while maintaining accuracy?
Answer: A
Explanation:
Option B creates a targeted correction loop using information the first extraction did not have: the validator's precise failure report. Anthropic's prompting guidance describes prompt chaining as appropriate when an application must inspect intermediate output or enforce a specific pipeline, with self-correction following the pattern generate, review against criteria, and refine. Here, deterministic validators identify arithmetic mismatches and invalid vendor-ID formats. Returning the source document, original extraction, and explicit errors lets Claude revise only the defective fields while preserving valid data. The corrected record must then be validated again before acceptance, with a bounded retry count and human-review fallback. Option A is unsafe because automatically recalculating a total may overwrite the amount actually printed on the invoice; a mismatch can originate in an OCR or line-item extraction error. Option C repeats the same extraction without explaining what failed, wasting calls and relying on chance. Option D may prevent some format violations, but JSON Schema cannot express every cross-field arithmetic relationship or external vendor-registry rule. A validator-guided second pass therefore reduces manual review without silently changing source facts, while keeping deterministic code-not the model-as the final acceptance authority.
NEW QUESTION # 176
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 wants Claude to follow a detailed code review checklist (8 items covering API changes, test coverage, documentation, security, etc.) when reviewing pull requests. The team also uses Claude extensively for other tasks: writing new features, debugging production issues, and generating documentation. Currently, developers paste the checklist at the start of each review session. Which approach best addresses this workflow need?
Answer: A
Explanation:
A reusable command is ideal for a checklist or procedure that developers invoke only during code reviews. It avoids repeatedly pasting the checklist and prevents review-specific instructions from consuming context during unrelated tasks.
NEW QUESTION # 177
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 automated reviewer uses a single prompt covering security issues, API design, and business-logic correctness. Your evaluation suite shows strong recall for API-design findings at 82% but poor recall for business-logic edge cases in quiz scoring at 34%. When you add few-shot examples of logic bugs to the prompt, logic recall improves to 41%, but API-design recall drops to 68%.
How should you address this trade-off to improve detection across both categories?
Answer: B
Explanation:
Option A separates competing objectives so each review call can devote its attention and examples to a coherent concern. Anthropic's Building Effective AI Agents describes sectioning as a parallelization pattern in which independent aspects are handled by separate model calls and then aggregated. It specifically notes that models generally perform better on complex tasks when each consideration receives focused attention.
The evaluation results demonstrate prompt interference: optimizing business-logic detection reduces API- design recall.
A security and API-design reviewer can use examples, terminology, and evidence criteria appropriate to interfaces and vulnerabilities. A business-logic reviewer can focus on state transitions, arithmetic boundaries, invariants, and domain-specific edge cases. Their structured findings can then be deduplicated and ranked before posting. Option B may improve only the weak category and can overfit to known quiz-scoring failures.
Option C assumes that model capacity alone removes prompt interference without evaluation evidence.
Option D adds substantial unrelated context and does not isolate the competing review objectives. The split prompts should be evaluated independently and together so consolidation does not reintroduce omissions or duplicate findings.
NEW QUESTION # 178
After investigating a billing dispute over 25+ turns, you've identified that duplicate charges occurred due to a payment gateway timeout triggering retry logic. The required refund ($847) exceeds your $500 authorization limit You need to call escalate_to_human, and the human agent won't have access to your conversation transcript. What context should you pass to enable effective resolution?
Answer: B
Explanation:
The human agent needs concise, complete handoff context because they cannot see the prior conversation. A structured summary with the customer ID, root cause, refund amount, and recommended action preserves the essential diagnostic and resolution details without overwhelming them with unnecessary transcript history.
NEW QUESTION # 179
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