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| Section | Weight | Objectives |
|---|---|---|
| Prompt Engineering & Structured Output | 20% | - System prompt design and persona alignment - JSON schema design and structured output enforcement - Explicit criteria definition and few-shot prompting - Validation, parsing and retry loop strategies |
| Agentic Architecture & Orchestration | 27% | - Agentic loop design and stop_reason handling - Task decomposition and dynamic subagent selection - Multi-agent patterns: coordinator-subagent and hub-and-spoke - Error recovery, guardrails and safety patterns - Session state management and workflow enforcement |
| Tool Design & MCP Integration | 18% | - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - Tool schema design and interface boundaries - Error handling and tool response formatting - Tool distribution and permission controls - MCP tool, resource and prompt implementation |
| Claude Code Configuration & Workflows | 20% | - Custom slash commands and plan mode vs direct execution - CI/CD integration and non-interactive mode parameters - Hooks vs advisory instructions - Path-specific rules and .claude/rules/ configuration - CLAUDE.md hierarchy, precedence and @import rules |
| Context Management & Reliability | 15% | - Context pruning and summarization strategies - Token budget management and cost control - Context window optimization and prioritization - Idempotency, consistency and failure resilience |
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NEW QUESTION # 111
You are building a multi-agent research system using the Claude Agent SDK. A coordinator agent delegates to specialized subagents: one searches the web, one analyzes documents, one synthesizes findings, and one generates reports. The system researches topics and produces comprehensive, cited reports.
The coordinator provides detailed step-by-step instructions to the web-search subagent, specifying exact search queries, source priorities, and date filters. Production monitoring reveals three issues: (1) the subagent reports "insufficient results" rather than trying alternative approaches when the pre-specified searches fail, (2) research quality drops for emerging topics that do not match expected patterns, and (3) the subagent rarely surfaces valuable tangential sources.
What is the most effective way to improve subagent adaptability?
Answer: D
Explanation:
Option A defines what successful research must achieve without hard-coding a brittle search procedure. The subagent receives measurable objectives-breadth, diversity, recency, and relevance-but retains authority to reformulate queries, follow promising leads, and adjust source selection when the topic does not match familiar patterns. Anthropic's account of its multi-agent research system states that subagents need an objective, expected output format, guidance about tools and sources, and clear task boundaries. This is more informative than option B's vague instruction, yet more adaptive than prescribing every query. Option C improves only one known failure path and still constrains the agent to a predefined procedure that may be unsuitable for emerging subjects. Option D adds orchestration complexity and a potentially incorrect classification step without solving over-prescription within either category. The coordinator should describe mandatory constraints separately from strategic guidance: authoritative-source requirements and time boundaries can remain fixed, while query formulation and exploration paths remain agent-controlled.
Evaluation should then measure source coverage, evidence quality, duplication, and discovery of relevant secondary leads rather than whether the subagent followed a particular series of searches.
NEW QUESTION # 112
Your automated review calls the Claude API for each pull request, using tool_use with a report_findings tool that returns a JSON array of finding objects. Each object contains file_path, line_number, severity, category, and description. During testing on a large pull request touching more than 30 files, the response reaches the max_tokens limit and is truncated in the middle of the JSON, causing your pipeline's parser to fail. What is the most effective way to handle this?
Answer: B
Explanation:
Option A reduces the maximum output required from any single response while preserving the structured schema and complete severity range. The pipeline can partition files into coherent groups, execute bounded reviews, validate each returned array, and merge and deduplicate findings using stable fields such as file path, line number, category, and description.
Anthropic's stop-reason documentation confirms that max_tokens means generation reached the configured output limit and the response must be treated as incomplete. Structured output constraints can guarantee schema-valid generation when completion succeeds, but they cannot create unlimited output capacity. A large findings array can still exceed the available token budget.
Option B may postpone the failure but provides no durable guarantee for still-larger pull requests, and aggressively shortening descriptions may eliminate necessary evidence. Option C abandons machine- validated structure without reducing the amount of generated content. Option D deliberately suppresses medium- or low-severity findings and repeats an oversized request rather than addressing its scope.
Partitioning establishes predictable output bounds, supports targeted retries, retains every required finding category, and prevents a single truncated response from invalidating the complete review.
NEW QUESTION # 113
Anthropic's tool use documentation states: "Write instructive error messages. Instead of generic errors like 'failed', include what went wrong and what Claude should try next." A billing dispute agent uses lookup_order, which catches all exceptions and returns a tool_result with is_error:
true and the message "Tool execution failed". Monitoring shows two failure modes: the agent retries the identical call until hitting the turn limit, or it immediately calls escalate_to_human without trying alternative tools. Which change follows the documented recommendation and gives Claude the information it needs to select the correct recovery action for each error type?
Answer: B
Explanation:
Specific error messages tell Claude both what failed and the appropriate next action. A missing order should trigger an alternative lookup path, while a transient timeout may justify retrying. This directly follows Anthropic's recommendation for instructive tool errors.
NEW QUESTION # 114
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, Glob) and integrates with Model Context Protocol (MCP) servers.
Your code review assistant needs to analyze pull requests and provide feedback on three aspects: code style compliance, potential security issues, and documentation completeness.
Each aspect requires reading files, running analysis tools, and generating a report section. The review process follows the same three-step workflow for every PR. Which task decomposition pattern is most appropriate for this workflow?
Answer: D
Explanation:
The review always follows the same predefined stages-style, security, and documentation-so each can be analyzed separately and then combined into a final report. Orchestrator-workers is better when subtasks must be determined dynamically.
NEW QUESTION # 115
After the web-search agent finds 25 sources containing 120,000 tokens of raw content, the document-analysis agent extracts 15,000 tokens of key insights, and the synthesis agent produces a coherent 3,000-token narrative draft, the coordinator must pass context to the report-generation agent for the final output with proper source citations. What context-passing strategy provides the best balance of completeness and efficiency?
Answer: C
Explanation:
Option B gives the report generator the two forms of information it actually needs: a compact narrative containing the synthesized conclusions and precise evidence records supporting those conclusions. The source index should map claim identifiers to URLs, document locations, excerpts, and other provenance fields so citations can be generated and verified without reloading 120,000 tokens of raw material.
Anthropic's context-engineering guidance recommends distilling extensive subagent activity into high-signal summaries while preserving critical context and dependencies. Its research architecture similarly treats subagents as intelligent filters that compress large evidence collections before returning relevant findings to the coordinator.
Option A lacks sufficient location-level evidence for reliable citation verification. Option C postpones attribution until after drafting, when claim wording may no longer map cleanly to the original evidence.
Option D maximizes theoretical completeness but introduces extensive irrelevant material, higher token costs, slower processing, and greater context pollution. The synthesis draft plus a structured claim-to-source index maintains evidentiary completeness where it matters while eliminating intermediate searches, duplicate passages, and other low-value context.
NEW QUESTION # 116
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