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| Section | Weight | Objectives |
|---|---|---|
| Tool Design & MCP Integration | 18% | - Designing effective tools for Claude applications - Model Context Protocol (MCP) concepts and integration - Tool safety, reliability, and usability |
| Context Management & Reliability | 15% | - Production deployment considerations - Managing context windows and information flow - Evaluation and reliability strategies |
| Prompt Engineering & Structured Output | 20% | - Prompt design strategies - Improving Claude response quality and consistency - Structured output generation and validation |
| Claude Code Configuration & Workflows | 20% | - Claude Code usage and configuration - Developer productivity workflows - Integrating Claude Code into development processes |
| Agentic Architecture & Orchestration | 27% | - Designing agentic systems and workflows - Selecting appropriate Claude architectures - Agent coordination and orchestration patterns |
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NEW QUESTION # 80
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: C
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 # 81
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: A
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 # 82
A developer uses Claude Code to refactor a function during a development session. Before committing, the developer asks the same Claude session to review the code for issues. Later, a separate automated CI review catches several bugs that the same-session review missed. What best explains this discrepancy?
Answer: A
Explanation:
The key difference is reviewer independence. In the original session, Claude retains the reasoning, assumptions, implementation decisions, and intermediate conclusions that led to the refactor. That context can anchor the subsequent review toward defending or confirming the chosen approach instead of evaluating the resulting code independently. The separate CI review begins with fresh context and can assess the implementation solely against the code and review criteria.
Anthropic's Claude Code best-practices documentation explicitly states that fresh context improves code review because Claude is not biased toward code it has just written. It recommends a writer/reviewer pattern using separate sessions or a review subagent with an isolated context.
Option B is possible in an uncontrolled comparison, but the scenario's defining distinction is that one review occurs in the implementation session and the other is independent. Option C is false because local Claude Code can read and search the repository when permissions and tools permit it; it is not inherently restricted to the current file. Option D would require evidence of context saturation, such as degraded instruction following or an almost-full context window. No such evidence is supplied. Prior-reasoning bias most directly explains the observed discrepancy.
NEW QUESTION # 83
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 tasked with adding real-time updates to the application. This could be implemented using WebSockets, Server-Sent Events, or polling, each with different complexity, browser support, and infrastructure requirements.
What's the most effective way to begin this task?
Answer: A
Explanation:
The implementation approach is unresolved and carries architectural consequences. WebSockets, Server-Sent Events, and polling differ in connection lifecycle, bidirectional communication, proxy compatibility, scaling requirements, deployment topology, reconnection behavior, and operational complexity. Claude should therefore inspect the existing application and infrastructure before modifying code.
Anthropic recommends separating exploration and planning from implementation when the approach is uncertain, the work affects multiple files, or the developer is unfamiliar with the relevant architecture. In plan mode, Claude can read files and analyze the system without making changes, produce a detailed proposal, and allow the developer or team to review the plan before execution. ( https://code.claude.com/docs/en/best- practices ) Option A commits to polling before determining whether its latency and load characteristics meet the requirement. Option B delegates a consequential architectural decision and immediate implementation to a single execution step without a review checkpoint. Option D prematurely selects the most infrastructure- sensitive option and assumes later refactoring will be inexpensive.
Option C creates the correct sequence: examine the frontend and backend architecture, document requirements, compare alternatives, identify infrastructure constraints, obtain approval, and only then implement the selected design with appropriate tests and observability.
Official references/topics: Plan mode, explore-plan-implement workflow, architectural trade-off analysis, review checkpoints.
NEW QUESTION # 84
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:
Option B removes the ambiguity from the tool interface itself. The agent no longer needs to choose between separate functions that represent closely related business operations. Instead, a single compensation tool exposes an explicit action parameter, while the existing order lookup tool can optionally return delivery- tracking information through a clearly defined flag.
Anthropic's tool-design guidance recommends consolidating related operations into fewer, more capable tools when separate functions create unnecessary semantic overlap. Claude selects tools primarily from their names, descriptions, schemas, and the current task. When multiple tools appear capable of satisfying the same request, selection accuracy declines because the agent must infer distinctions that should have been made explicit in the interface design.
Option A adds routing complexity but leaves overlapping financial operations available within the same sub- agent. Option C reduces the number of tools loaded initially, but it does not eliminate the semantic duplication between compensation operations or overlapping lookup functions. Option D may improve behavior through examples, but it compensates for a poorly designed tool surface rather than correcting the root cause.
The consolidated schemas should use enums for supported actions, clearly define when each action applies, and document the returned fields. This creates a smaller and more deterministic agent-computer interface.
Official references/topics: Tool consolidation, semantic tool boundaries, action parameters, MCP tool- selection accuracy.
NEW QUESTION # 85
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