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| Section | Weight | Objectives |
|---|---|---|
| Prompt Engineering & Structured Output | 20% | - Improving Claude response quality and consistency - Structured output generation and validation - Prompt design strategies |
| Context Management & Reliability | 15% | - Managing context windows and information flow - Production deployment considerations - Evaluation and reliability strategies |
| Agentic Architecture & Orchestration | 27% | - Designing agentic systems and workflows - Selecting appropriate Claude architectures - Agent coordination and orchestration patterns |
| Claude Code Configuration & Workflows | 20% | - Developer productivity workflows - Integrating Claude Code into development processes - Claude Code usage and configuration |
| Tool Design & MCP Integration | 18% | - Model Context Protocol (MCP) concepts and integration - Designing effective tools for Claude applications - Tool safety, reliability, and usability |
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NEW QUESTION # 122
Your send_notification tool calls third-party messaging APIs. When these services time out during delivery, you cannot determine whether the message was actually sent. Currently, the tool returns is_error: true with a generic "Notification failed" message for all timeouts. Production monitoring reveals agents automatically retry these failures, frequently causing users to receive duplicate notifications. How should you modify the error response?
Answer: D
Explanation:
Adding a structured retry_safe flag distinguishes uncertain, potentially idempotent errors from permanent failures. This allows the agent to make informed decisions about automatic retries, preventing duplicate notifications while maintaining reliability for recoverable errors.
NEW QUESTION # 123
You are building a customer support resolution agent using the Claude Agent SDK. The agent handles high-ambiguity requests like returns, billing disputes, and account issues. It has access to your backend systems through custom Model Context Protocol (MCP) tools ( get_customer, lookup_order , process_refund , escalate_to_human ). Your target is 80%+ first-contact resolution while knowing when to escalate.
After expanding the agent's MCP tools with delivery-specific capabilities ( check_delivery_status , contact_driver , issue_credit , apply_promo_code , update_delivery_address , reschedule delivery ), the total tool count has grown from 4 to 10. Your evaluation suite shows tool selection accuracy has dropped from 88% to 71%. Log analysis reveals the majority of errors involve the agent selecting between semantically overlapping tools - calling issue_credit when process_refund was correct, and calling check_delivery_status when lookup_order already returns the needed data. Which approach structurally eliminates the semantic overlap identified in the logs as the error source?
Answer: D
Explanation:
Merging issue_credit and process_refund into one compensation tool, and folding delivery-status lookup into lookup_order, removes competing tools with overlapping purposes. This directly eliminates the ambiguity causing incorrect tool selection, rather than merely hiding tools, routing between agents, or adding examples.
NEW QUESTION # 124
A company wants Claude to summarize thousands of support tickets efficiently. Which design scales BEST?
Answer: D
Explanation:
Hierarchical summarization is an effective strategy for large datasets. Claude summarizes smaller batches first, then combines intermediate summaries into higher-level reports, improving scalability while maintaining important information.
NEW QUESTION # 125
A developer wants Claude to consistently answer as an experienced cybersecurity analyst regardless of the user question. Which prompt component should contain this instruction?
Answer: A
Explanation:
The system prompt establishes persistent behavioral instructions that apply throughout the conversation. Role definition, response style, and organizational policies belong there because Claude prioritizes system instructions above ordinary user requests whenever possible.
NEW QUESTION # 126
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 schema includes a skills: string[] field. Production monitoring reveals three consistency issues: (1) compound phrases like "Python and SQL" are sometimes kept as one entry, sometimes split; (2) implied but unstated skills occasionally appear in extractions; (3) similar documents produce wildly different array lengths (5-10 vs 40+ entries). Your prompt currently says "Extract all skills mentioned." What's the most effective improvement?
Answer: B
Explanation:
The failure is caused by an underspecified extraction policy rather than an inadequate JSON structure.
"Extract all skills mentioned" does not define whether coordinated phrases should be split, whether inferred capabilities qualify, or what level of specificity constitutes one skill. Few-shot examples can demonstrate all three decisions using concrete inputs and expected outputs.
Anthropic identifies examples as one of the most effective ways to improve output accuracy and consistency because they communicate behavior more precisely than abstract instructions alone. Representative examples can show that "Python and SQL" becomes two entries, that an unstated skill must not be inferred from a job title, and that broad descriptions should not be decomposed into dozens of speculative micro-skills. (
https://docs.anthropic.com/en/docs/about-claude/use-case-guides/ticket-routing ) Option A adds provenance and confidence metadata but does not define the correct extraction granularity.
Option C addresses explicitness and splitting, but the arbitrary 10-20 limit can truncate documents containing more than 20 legitimate skills or force padding when fewer than 10 appear. Option D is useful for canonicalization and deduplication, but post-processing cannot reliably remove plausible implied skills or determine how the original phrase should have been interpreted.
The strongest prompt should combine explicit rules with diverse few-shot examples covering normal cases, compound phrases, implied information, duplicates, and unusually long or short skill lists.
Official references/topics: Few-Shot Prompting, Output Consistency, Explicit Extraction Criteria, Representative Examples.
NEW QUESTION # 127
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