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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Context Management & Reliability | 15% | - Idempotency, consistency and failure resilience - Token budget management and cost control - Context pruning and summarization strategies - Context window optimization and prioritization |
| Topic 2: Prompt Engineering & Structured Output | 20% | - JSON schema design and structured output enforcement - System prompt design and persona alignment - Explicit criteria definition and few-shot prompting - Validation, parsing and retry loop strategies |
| Topic 3: Agentic Architecture & Orchestration | 27% | - Task decomposition and dynamic subagent selection - Session state management and workflow enforcement - Error recovery, guardrails and safety patterns - Agentic loop design and stop_reason handling - Multi-agent patterns: coordinator-subagent and hub-and-spoke |
| Topic 4: Claude Code Configuration & Workflows | 20% | - Custom slash commands and plan mode vs direct execution - 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 |
| Topic 5: Tool Design & MCP Integration | 18% | - Tool distribution and permission controls - 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 |
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NEW QUESTION # 44
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 invoices and extracts line items, subtotals, tax amounts, and grand totals. During evaluation, you discover that in 18% of extractions, the sum of extracted line item amounts doesn't match the extracted grand total--sometimes due to OCR errors in the source document, sometimes due to extraction mistakes by the model. Downstream accounting systems reject records with mismatched totals.
What's the most effective approach to improve extraction reliability?
Answer: A
Explanation:
The pipeline must preserve source evidence while making inconsistencies explicit. Option D records the amount stated on the invoice separately from the total derived from extracted line items. A mismatch then becomes a machine-detectable validation condition rather than an invisible extraction defect.
This approach is superior because it does not silently overwrite source data or ask another model to guess which value is correct. Anthropic's evaluation guidance recommends automated, code- based grading whenever the criterion can be expressed deterministically. Arithmetic reconciliation is precisely such a criterion.
In production, the summation should preferably be calculated by application code using normalized decimal values, even though the option describes the model populating calculated_total. The essential design principle remains the same: preserve stated_total, compute an independent total, compare them, and route discrepancies for adjudication.
NEW QUESTION # 45
Your update_user_profile tool accepts a user_id (required) and an optional fields_to_update object. In testing, Claude frequently omits user_id or passes incorrectly structured data. What is most critical for helping Claude understand what parameter values to provide?
Answer: B
Explanation:
Enforcing required fields and type constraints through a JSON Schema gives the agent clear rules about what parameters must be provided and their expected structure, preventing omissions or incorrectly formatted data.
NEW QUESTION # 46
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.
You're implementing a caching layer for API responses to speed up the /products endpoint. You have a rough idea-Redis with a 5-minute TTL-but you're new to production caching and aren't sure what other considerations a robust implementation requires. What's the most effective way to start your iterative workflow?
Answer: D
Explanation:
Because the requirements are incomplete and production caching involves hidden design choices, an interview helps surface invalidation, consistency, failure handling, and other trade-offs before implementation. Anthropic recommends this approach for larger features when important requirements may not yet be identified.
NEW QUESTION # 47
Your automated reviewer uses a single prompt covering security issues, API design, and business-logic correctness. Your evaluation suite shows strong recall for API-design findings at
82% but poor recall for business-logic edge cases in quiz scoring at 34%. When you add few-shot examples of logic bugs to the prompt, logic recall improves to 41%, but API-design recall drops to
68%. How should you address this trade-off to improve detection across both categories?
Answer: D
Explanation:
The evaluation results demonstrate prompt interference: improving attention to business-logic examples reduces performance on API design. Option C isolates the competing objectives so each model call can apply focused terminology, examples, evidence requirements, and evaluation criteria. The resulting structured findings can then be merged, deduplicated, and ranked before being posted.
Anthropic's Building Effective AI Agents describes parallelization through sectioning, where independent aspects of a task are handled by separate model calls and subsequently aggregated. This pattern is appropriate when one prompt must evaluate several distinct considerations and a single call does not handle all of them reliably.
NEW QUESTION # 48
A team wants to reduce hallucinations without significantly changing application logic. Which improvement should be implemented FIRST?
Answer: D
Explanation:
Grounding Claude with trusted retrieved information is one of the most effective methods for reducing hallucinations. It provides current, authoritative context while requiring relatively small architectural changes compared with retraining or redesigning the application.
NEW QUESTION # 49
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