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| Section | Weight | Objectives |
|---|---|---|
| Context Management & Reliability | 15% | - Context window optimization and prioritization - Idempotency, consistency and failure resilience - Token budget management and cost control - Context pruning and summarization strategies |
| Tool Design & MCP Integration | 18% | - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - MCP tool, resource and prompt implementation - Tool distribution and permission controls - Tool schema design and interface boundaries - Error handling and tool response formatting |
| Claude Code Configuration & Workflows | 20% | - Custom slash commands and plan mode vs direct execution - CLAUDE.md hierarchy, precedence and @import rules - CI/CD integration and non-interactive mode parameters - Path-specific rules and .claude/rules/ configuration - Hooks vs advisory instructions |
| Prompt Engineering & Structured Output | 20% | - Explicit criteria definition and few-shot prompting - Validation, parsing and retry loop strategies - JSON schema design and structured output enforcement - System prompt design and persona alignment |
| Agentic Architecture & Orchestration | 27% | - Multi-agent patterns: coordinator-subagent and hub-and-spoke - Session state management and workflow enforcement - Agentic loop design and stop_reason handling - Error recovery, guardrails and safety patterns - Task decomposition and dynamic subagent selection |
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NEW QUESTION # 34
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: C
Explanation:
The checklist is a reusable, task-specific procedure that should load only when a pull-request review is being performed. A /review command provides an explicit invocation mechanism, prevents repeated copy-and- paste, and keeps the checklist out of unrelated coding, debugging, and documentation sessions.
In current Claude Code terminology, custom commands have been consolidated into skills. A .claude/skills
/review/SKILL.md file creates a /review command, while the legacy .claude/commands/review.md format remains supported. Anthropic recommends skills when users repeatedly supply the same checklist or multi- step procedure; unlike CLAUDE.md content, the skill body is loaded only when invoked or determined relevant. ( https://code.claude.com/docs/en/skills ) Option B introduces an isolated agent context when the requirement is primarily to inject a repeatable review procedure. A subagent could be useful for independent verification, but it is not necessary merely to avoid repasting the checklist. Option C bloats every session with instructions that apply only to code review. Option D controls editing permissions and planning behavior; it does not encode review criteria.
The review skill can also pre-authorize read-only tools and accept a pull-request identifier or branch as an argument.
Official references/topics: Claude Code Skills; Custom Slash Commands; On-Demand Instructions; Reusable Review Workflows.
NEW QUESTION # 35
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.
During a billing dispute resolution, your agent successfully retrieves customer info via get_customer and order details via lookup_order , but when attempting to call process_refund , the tool returns a timeout error.
The agent has enough information to explain the charges and verify refund eligibility, but cannot actually process the refund due to the backend failure.
What approach best balances first-contact resolution with appropriate error handling?
Answer: B
Explanation:
First-contact resolution does not require pretending that every backend operation succeeded. The agent can still resolve the informational portion of the interaction by explaining the charge and confirming eligibility using the successfully retrieved customer and order data. It must then distinguish that verified conclusion from the uncompleted refund transaction.
Anthropic's tool-error guidance states that Claude should receive the failure information so it can retry, request clarification, or explain the limitation. A tool timeout must therefore be surfaced honestly rather than converted into an unsupported success claim. ( https://platform.claude.com/docs/en/agents-and-tools/tool-use
/build-a-tool-using-agent?utm_source=chatgpt.com ) Anthropic also emphasizes transparent, simple agent designs and carefully constructed tool interfaces, which support explicit disclosure of tool failure and controlled escalation. ( https://www.anthropic.com/engineering/building-effective-agents ) Option A can create an unbounded or excessively long interaction; retries should be limited and conditioned on retryability. Option B falsely represents an incomplete financial operation as completed. Option D discards the useful work already performed and escalates before providing the customer with the available explanation.
Option C preserves trust, delivers the information already established, clearly states what remains incomplete, and gives the customer a practical next step through bounded retry or human escalation.
Official references/topics: Graceful tool failure, transparent customer communication, bounded retry, human escalation.
NEW QUESTION # 36
Production reviews reveal inconsistent handling of uncertainty in final reports. Sometimes conflicting subagent findings are synthesized into a single confident statement, losing important nuance, while other reports over-hedge with excessive qualifications and become unhelpful. The web-search agent returns,
"Industry analysts estimate a $50 billion market size, although methodologies vary." The document-analysis agent returns, "A peer-reviewed study estimates $35 billion, with a ±$7 billion 95% confidence interval." The coordinator either selects one estimate arbitrarily or produces a vague $35-$50 billion range. What systematic approach best addresses this?
Answer: B
Explanation:
Option A preserves disagreement as meaningful evidence. The report should identify the two estimates separately, explain that they arise from different methodologies and source types, retain the peer-reviewed study's stated confidence interval, and classify the market size as contested rather than manufacturing false agreement. It may then explain what additional evidence would resolve the discrepancy.
Anthropic's research prompting guidance recommends developing competing hypotheses, tracking confidence, verifying across sources, and retaining structured research notes. This supports calibrated synthesis rather than arbitrary selection or vague hedging.
Option B discards potentially important minority evidence and incorrectly treats corroboration count as equivalent to reliability. Option C creates numerical precision unsupported by the original sources; confidence scores from different agents are not automatically comparable, and averaging incompatible estimates can be meaningless. Option D introduces survivorship bias by suppressing uncertainty before synthesis. A systematic reporting structure distinguishes consensus, disagreement, evidence quality, methodological limitations, and unresolved questions. That produces an actionable briefing without overstating certainty or weakening every conclusion with generic qualifications.
NEW QUESTION # 37
You built an LLM-powered code-review tool that analyzes pull requests and returns structured findings. Each finding is a JSON object containing file_path, line_number, issue_category-such as security or style-and description. Developers can dismiss findings they consider unhelpful, and currently 35% of findings are dismissed. You want to analyze these dismissals to understand what the system is getting wrong and improve the prompts accordingly. What change to the output structure would best support this analysis?
Answer: B
Explanation:
Option B creates the granular error taxonomy needed to understand recurring false-positive patterns. The existing issue_category field is too broad: a high dismissal rate for style does not reveal whether developers object to single-letter variables, line-length warnings, naming conventions, or something else. Recording the detected construct allows dismissal rates to be grouped by trigger and connected to targeted prompt revisions or project-specific examples.
Anthropic's evaluation guidance recommends measurable, task-specific criteria and evaluation cases that reflect real production behavior and edge cases. Its structured-output documentation supports schema- constrained, parseable records suitable for this type of downstream analysis.
Option A can help rank findings, but a confidence score does not explain why developers reject them and may be poorly calibrated across issue types. Option C produces more text without adding a stable dimension for aggregation. Option D removes useful category information and makes systematic analysis harder. A normalized detected_pattern field enables dashboards, pattern-level dismissal metrics, representative-example sampling, and controlled evaluations of prompt changes while retaining the broader category for higher-level reporting.
NEW QUESTION # 38
You are building developer-productivity tools using the Claude Agent SDK. The agent helps engineers explore unfamiliar codebases, understand legacy systems, generate boilerplate code, and automate repetitive tasks. It uses the built-in tools--Read, Write, Bash, Grep, and Glob--and integrates with Model Context Protocol (MCP) servers.
You are building a security-scanning workflow.
When engineers need to locate every occurrence of a dangerous function such as eval() across a large codebase, which tool should the agent use for content searching?
Answer: A
Explanation:
Option B uses the tool designed to search file contents. Anthropic's Claude Code tools reference distinguishes Grep from Glob: Grep searches lines inside files, whereas Glob matches filenames and paths. Grep is built on ripgrep and accepts regular-expression patterns, so the opening parenthesis should be escaped as eval\( when the intention is to match the literal function call.
The search can return matching files, line numbers, and surrounding context without loading every file into the model's context window.
NEW QUESTION # 39
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