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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Agentic Architecture & Orchestration | 27% | - Error recovery, guardrails and safety patterns - Multi-agent patterns: coordinator-subagent and hub-and-spoke - Session state management and workflow enforcement - Agentic loop design and stop_reason handling - Task decomposition and dynamic subagent selection |
| Topic 2: Prompt Engineering & Structured Output | 20% | - JSON schema design and structured output enforcement - Explicit criteria definition and few-shot prompting - Validation, parsing and retry loop strategies - System prompt design and persona alignment |
| Topic 3: 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 |
| Topic 4: Tool Design & MCP Integration | 18% | - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - Tool schema design and interface boundaries - Tool distribution and permission controls - Error handling and tool response formatting - MCP tool, resource and prompt implementation |
| Topic 5: Claude Code Configuration & Workflows | 20% | - Hooks vs advisory instructions - CI/CD integration and non-interactive mode parameters - Path-specific rules and .claude/rules/ configuration - CLAUDE.md hierarchy, precedence and @import rules - Custom slash commands and plan mode vs direct execution |
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NEW QUESTION # 88
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 contracts that frequently include amendments. When a contract contains both original terms and later amendments (e.g., original clause specifies "30- day payment terms" while Amendment 1 changes this to "45 days"), the model inconsistently extracts one value or the other with no indication of which applies.
What's the most effective approach to improve extraction accuracy for documents with amendments?
Answer: D
Explanation:
The document contains multiple factually valid values whose applicability depends on chronology and legal context. Collapsing those values into a single scalar field discards essential provenance. Option B corrects the data model by representing each term as a structured record containing the extracted value, source location, document or amendment identifier, and effective date.
Anthropic's Structured Outputs feature is designed for data-extraction use cases in which nested objects and arrays must conform to a defined JSON Schema. Anthropic also recommends grounding factual outputs in direct source material and making claims auditable through supporting evidence. A provenance-aware schema applies both principles: it retains the original clause and the amendment instead of forcing Claude to resolve a potentially complex legal precedence question during extraction.
NEW QUESTION # 89
During initial testing of the automated review pipeline, you notice that reviews of 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 after reaching either a fixed iteration count or a fixed dollar amount. Both limits must be enforced by Claude Code itself rather than by the surrounding job runner. Which configuration change directly enforces both per-invocation limits?
Answer: C
Explanation:
Option A is the only configuration that establishes both required limits inside Claude Code. The official CLI reference defines --max-turns as the maximum number of agentic turns permitted in print mode; Claude Code exits with an error when that limit is reached. It defines --max-budget-usd as the maximum dollar expenditure on API calls before execution stops, including applicable subagent expenditure.
These controls address different failure dimensions. The turn limit prevents an investigation from continuing through excessive read-search-analyze cycles, while the budget limit stops an invocation whose expensive turns consume the monetary allowance before reaching the turn ceiling. Supplying both therefore creates an effective per-run boundary.
Option B controls how permission requests are handled; it does not limit the number of already permitted tool calls or API expenditure. Option C applies an external wall-clock timeout and merely observes cost, violating the requirement that Claude Code itself enforce both limits. Option D lowers expected cost per turn but creates no hard ceiling: the agent can still perform many iterations and exceed the intended budget. A cheaper model also does not guarantee that the review will terminate within a predictable number of turns.
NEW QUESTION # 90
When the agent calls lookup_order and receives order details showing the item was purchased
45 days ago, how does the agentic loop determine whether to call process_refund or escalate_to_human next?
Answer: D
Explanation:
In an agentic loop, tool results are returned to the model as context. The model then reasons over the updated information, such as the purchase age, and decides the next appropriate action based on the available tools, policies, and task objective.
NEW QUESTION # 91
A developer includes multiple unrelated tasks inside one extremely long prompt. What is the MOST likely outcome?
Answer: B
Explanation:
Combining many unrelated objectives in a single prompt increases ambiguity and makes it harder for Claude to identify priorities. Separating tasks into focused prompts generally produces more reliable and maintainable outputs.
NEW QUESTION # 92
A developer wants Claude to explain every reasoning step internally before answering. Why might this request be inappropriate?
Answer: A
Explanation:
Applications should evaluate Claude based on observable outputs rather than expecting access to internal reasoning processes. Prompting should request useful explanations or evidence instead of relying on hidden reasoning mechanisms.
NEW QUESTION # 93
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