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| Section | Weight | Objectives |
|---|---|---|
| Prompt Engineering & Structured Output | 20% | - System prompt design and persona alignment - Explicit criteria definition and few-shot prompting - JSON schema design and structured output enforcement - Validation, parsing and retry loop strategies |
| Context Management & Reliability | 15% | - Idempotency, consistency and failure resilience - Context pruning and summarization strategies - Context window optimization and prioritization - Token budget management and cost control |
| Agentic Architecture & Orchestration | 27% | - Multi-agent patterns: coordinator-subagent and hub-and-spoke - Error recovery, guardrails and safety patterns - Session state management and workflow enforcement - Agentic loop design and stop_reason handling - Task decomposition and dynamic subagent selection |
| Claude Code Configuration & Workflows | 20% | - Hooks vs advisory instructions - CLAUDE.md hierarchy, precedence and @import rules - CI/CD integration and non-interactive mode parameters - Custom slash commands and plan mode vs direct execution - Path-specific rules and .claude/rules/ configuration |
| Tool Design & MCP Integration | 18% | - Error handling and tool response formatting - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - MCP tool, resource and prompt implementation - Tool schema design and interface boundaries - Tool distribution and permission controls |
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NEW QUESTION # 135
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 pre-specified searches fail, (2) research quality drops for emerging topics that don't match expected patterns, and (3) the subagent rarely surfaces valuable tangential sources.
What's the most effective way to improve subagent adaptability?
Answer: A
Explanation:
Delegating goals and quality criteria gives the subagent room to adapt its strategy while still defining what good research looks like. This improves handling of failed searches, emerging topics, and useful tangential sources because the subagent can vary queries, sources, and filters to satisfy the research objective rather than rigidly following preset steps.
NEW QUESTION # 136
You are building a structured data-extraction system using Claude. The system extracts information from unstructured documents, validates output against JSON schemas, and integrates the results with downstream systems.
Monitoring reveals that specifications sometimes appear inconsistently within source documents.
For example, a summary section might state "Battery: 4000 mAh," while the detailed specifications table states "Battery: 4200 mAh." Your current schema contains a single battery_capacity field.
This inconsistency occurs in approximately 15% of documents, and historical analysis confirms that the detailed specifications table is accurate 90% of the time.
What is the most effective approach?
Answer: C
Explanation:
Option C converts an empirically validated source hierarchy into an explicit extraction rule. The downstream contract requires one battery_capacity value, and historical analysis establishes that the detailed specifications table is substantially more reliable than the summary section. Claude should therefore be instructed to inspect all occurrences, detect conflicts, and select the detailed- table value when the two locations disagree.
Anthropic's prompting guidance emphasizes clear, direct instructions, relevant context, and explicit decision rules when order or completeness matters. Providing the reason for the precedence rule also helps the model generalize it to comparable specification conflicts.
NEW QUESTION # 137
You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JavaScript Object Notation (JSON) schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Your system has been operating with 100% human review for 3 months. Analysis shows that extractions with model confidence 90% have 97% accuracy overall. To reduce reviewer workload, you plan to automate high-confidence extractions.
Before deploying, what validation step is most critical?
Answer: C
Explanation:
An aggregate accuracy value can conceal severe performance disparities. A system may achieve
97% overall accuracy while performing poorly on a low-volume document type, a critical financial field, or a specific edge case. Automating outputs solely from the aggregate figure could therefore expose downstream systems to concentrated, high-impact errors.
Anthropic's evaluation guidance states that evaluations should be task-specific, reflect the real- world task distribution, and explicitly include edge cases. It also emphasizes multidimensional success criteria rather than reliance on a single global metric. Option A applies those principles by stratifying performance according to document type and field. This reveals whether confidence is calibrated consistently and whether the proposed automation threshold remains safe for every operationally significant segment.
NEW QUESTION # 138
Users report that final reports sometimes lack depth on specific subtopics. Investigation shows that the document analysis agent frequently identifies gaps - for instance, noting "the retrieved sources discuss API authentication but lack details on token refresh patterns" - but under the current strict pipeline, this insight isn't actionable since search has already completed. What's the most effective architectural change?
Answer: A
Explanation:
Making the coordinator gap-aware allows it to dynamically re-delegate targeted searches based on analysis feedback. This iterative loop ensures subtopics with insufficient coverage are addressed before synthesis, improving the depth and completeness of final reports.
NEW QUESTION # 139
Your pipeline reviews approximately 200 database-migration scripts daily using the Message Batches API. Each request includes a shared 8,000-token system prompt containing migration- review guidelines and schema documentation, followed by an individual migration script. You added cache_control breakpoints to the shared system prompt in every request, but monitoring shows cache-hit rates of only 32%, with misses concentrated among requests processed later in the batch window. Which change addresses the root cause without adding sequential-processing latency?
Answer: D
Explanation:
Option D directly addresses cache entries expiring before later batch requests are processed.
Anthropic's batch-processing documentation specifically notes that Message Batches can take longer than five minutes and recommends the one-hour prompt-cache duration for batches containing shared context. The prompt-caching documentation confirms that the default TTL is five minutes and that "ttl": "1h" creates an extended entry.
NEW QUESTION # 140
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