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| Section | Weight | Objectives |
|---|---|---|
| Context Management & Reliability | 15% | - Token budget management and cost control - Idempotency, consistency and failure resilience - Context window optimization and prioritization - Context pruning and summarization strategies |
| Claude Code Configuration & Workflows | 20% | - Path-specific rules and .claude/rules/ configuration - Hooks vs advisory instructions - CI/CD integration and non-interactive mode parameters - Custom slash commands and plan mode vs direct execution - CLAUDE.md hierarchy, precedence and @import rules |
| 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% | - Error recovery, guardrails and safety patterns - Agentic loop design and stop_reason handling - Task decomposition and dynamic subagent selection - Multi-agent patterns: coordinator-subagent and hub-and-spoke - Session state management and workflow enforcement |
| Tool Design & MCP Integration | 18% | - Tool distribution and permission controls - MCP tool, resource and prompt implementation - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - Tool schema design and interface boundaries - Error handling and tool response formatting |
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NEW QUESTION # 84
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 extraction pipeline processes restaurant menus and must output structured JSON with fields for item names, descriptions, prices, and dietary tags. Some menus use inconsistent formatting-- prices as "$12" vs "12.00", dietary info as icons vs text.
What's the most reliable approach?
Answer: B
Explanation:
The most reliable architecture separates semantic interpretation from deterministic normalization.
Claude is well suited to identifying that "$12" and "12.00" represent prices, or that a leaf icon represents a dietary classification. However, canonical conversion--removing currency symbols, converting values to decimal types, mapping icons to controlled labels, and enforcing locale- specific rules--is more predictably performed in application code.
Structured Outputs guarantee that Claude returns valid JSON matching the supplied schema, but that guarantee concerns structural conformance. It does not by itself guarantee that every semantically equivalent source representation will be normalized identically. Anthropic's evaluation guidance identifies code-based checks as the fastest, most reliable, and most scalable mechanism for rule- based validation.
Option D therefore minimizes model responsibility: Claude extracts the evidence as represented, and deterministic post-processing converts it into the canonical downstream format. This also makes normalization rules independently testable, version-controlled, and auditable.
NEW QUESTION # 85
Your conversational assistant frequently generates multiple clarifying questions when users make ambiguous requests. When a user asks "Can you help me with the report?", the assistant responds: "I'd be happy to help! Could you tell me: 1) Which report? 2) What kind of help - drafting, reviewing, or formatting? 3) What's your deadline?" User analytics show a 40% conversation abandonment rate after these multi-question responses.
What's the most effective way to reduce friction while appropriately handling ambiguity?
Answer: D
Explanation:
Making reasonable assumptions based on context and clearly stating them allows the assistant to respond immediately while giving users the opportunity to correct or refine the interpretation, reducing abandonment caused by long multi-question prompts and maintaining engagement.
NEW QUESTION # 86
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.
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: D
Explanation:
Option A gives the report generator the finished narrative context plus the evidence required to verify and cite its claims. The 3,000-token synthesis draft supplies organization and conclusions, while the source index preserves URLs, supporting excerpts, and claim-to-source mappings without transferring 120,000 tokens of raw search material. Anthropic's effective context-engineering guidance recommends curating the smallest high-signal context and having subagents return condensed, distilled results rather than flooding later stages with their complete working history. Anthropic's citation guidance likewise depends on retaining specific supporting passages and source locations. Option B is complete but inefficient and introduces context pollution that can reduce attention to relevant evidence. Option C separates citation insertion from the reasoning that produced the claims, making incorrect or unsupported matches more likely. Option D retains source names but discards the passages needed to demonstrate that each source actually supports the associated claim. The structured index should use stable source IDs and include only the excerpts necessary for the report's factual claims, with validation that every cited claim maps to at least one evidence entry.
NEW QUESTION # 87
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.
During initial testing of the automated review pipeline, you notice that reviews on large pull requests containing more than 50 changed files sometimes take over 20 minutes and cost $8-$12 per run because of extensive agentic loops. Claude reads files, runs analysis tools, and iterates many times. Your team needs each invocation to abort once it reaches both a fixed iteration count and a fixed dollar amount, enforced by Claude Code itself rather than by the surrounding job runner.
Which configuration change directly enforces both per-invocation caps?
Answer: B
Explanation:
Option C applies the two native stopping controls required by the question. Anthropic's Claude Code CLI reference defines --max-turns as the maximum number of agentic turns permitted in print mode and --max- budget-usd as the maximum API expenditure for that invocation. When the turn limit is reached, the run exits with an error. Spending by subagents counts toward the budget cap, and current Claude Code versions stop remaining background subagents when the limit is reached. These flags therefore control the complete invocation rather than merely one model request.
Option A lowers expected cost per iteration but establishes no hard ceiling on either iterations or total expenditure. Option B limits wall-clock execution at the CI-runner level and only observes spending retrospectively; it does not enforce a Claude Code budget. Option D controls permission behavior, not iteration count, execution time, or token spending. The pipeline should use both flags, capture the resulting nonzero exit or structured error status, and report whether a review ended normally or was terminated by a configured operational limit.
NEW QUESTION # 88
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: B
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.
NEW QUESTION # 89
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