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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Claude Code Configuration & Workflows | 20% | - Claude Code usage and configuration - Developer productivity workflows - Integrating Claude Code into development processes |
| Topic 2: Prompt Engineering & Structured Output | 20% | - Structured output generation and validation - Improving Claude response quality and consistency - Prompt design strategies |
| Topic 3: Agentic Architecture & Orchestration | 27% | - Agent coordination and orchestration patterns - Designing agentic systems and workflows - Selecting appropriate Claude architectures |
| Topic 4: Context Management & Reliability | 15% | - Managing context windows and information flow - Evaluation and reliability strategies - Production deployment considerations |
| Topic 5: Tool Design & MCP Integration | 18% | - Designing effective tools for Claude applications - Tool safety, reliability, and usability - Model Context Protocol (MCP) concepts and integration |
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NEW QUESTION # 86
The synthesis agent completes its initial pass but flags that three key research questions remain unanswered because the web-search and document-analysis agents did not find relevant information on those specific subtopics. The coordinator currently proceeds directly to report generation, producing reports with incomplete coverage. What change would most effectively improve research completeness?
Answer: D
Explanation:
Option A implements an iterative orchestrator-worker loop in which synthesis is treated as an evaluation checkpoint rather than an irreversible transition to report generation. When synthesis identifies missing evidence, the coordinator can formulate focused follow-up tasks, send them to the agents with the appropriate tools, and repeat synthesis after receiving the additional findings.
Anthropic's multi-agent research architecture follows this pattern: the lead agent synthesizes subagent findings and determines whether further research is required. If gaps remain, it creates additional subagents or refines the research strategy. This preserves specialization and centralized control.
Option B makes the incompleteness visible but does not improve research coverage. Option C may increase initial cost without guaranteeing that the specific gaps discovered during synthesis will be addressed. Option D weakens separation of concerns by giving the synthesis agent search capabilities that belong to the research specialists. Targeted redelegation is more efficient because the second research round is informed by concrete deficiencies rather than speculative breadth. It also allows the coordinator to track completeness explicitly before authorizing final report generation.
NEW QUESTION # 87
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.
Your team has connected a custom MCP server that provides DevOps workflow templates. The server exposes several MCP prompts (such as deploy_checklist and incident_response) in addition to tools. How do these MCP prompts become accessible within Claude Code?
Answer: D
Explanation:
Claude Code dynamically exposes MCP server prompts as commands using the format
/mcp__servername__promptname, such as /mcp__devops__deploy_checklist. Any prompt arguments are passed afterward, separated by spaces.
NEW QUESTION # 88
A customer sends: "This is frustrating. I've explained my issue twice and nothing is being resolved. I want to talk to a real person NOW." The agent has not yet called any tools to investigate the customer's account.
What should the agent do?
Answer: C
Explanation:
Option C respects the customer's explicit request for human assistance. The agent should not attempt to retain the interaction, gather additional account information, or require the customer to repeat the request. Because the escalation decision has already been made by the customer, further autonomous investigation would create unnecessary delay and disregard clear human direction.
Anthropic's trustworthy-agent framework emphasizes maintaining meaningful human control over agent autonomy. Anthropic's customer-support implementation guidance also recommends defining interaction branches and measuring escalation efficiency as part of the system's success criteria.
Option A deliberately delays escalation despite an unambiguous request. Option B performs unnecessary tool calls and may expose or retrieve account information that is not required before transferring the conversation.
Option D similarly introduces another conversational barrier. Since the current exchange is short and contains no tool-generated evidence, passing the available conversation history gives the human agent the immediate context-the customer's frustration, repeated unsuccessful attempts, and explicit transfer request-without pretending that account investigation has occurred.
NEW QUESTION # 89
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.
After your daily batch of 10,000 documents completes, 300 documents (3%) fail with context_length_exceeded errors. The results file identifies each failure by custom_id.
What is the most cost-effective approach to process these failures?
Answer: A
Explanation:
Option D fixes the actual failure while avoiding needless reprocessing. Anthropic's Message Batches API treats every request independently, records the result against its unique custom_id, and explicitly states that one failed request does not affect the others. The 9,700 successful documents therefore require no retry. The failed requests exceeded the available context; increasing max_tokens changes the permitted output budget, not the size of the input context, so option C does not correct the cause. Resubmitting all 10,000 requests would repeat paid work, and prompt caching would only reduce some repeated-input cost without repairing oversized requests. Chunking each failed document reduces the input presented to Claude so every replacement request fits within the model's context window . The application can then merge the extracted partial structures deterministically, retaining the original document identifier and chunk ordering. Anthropic recommends retry logic for failed batch requests and using custom_id to match results because batch results can arrive out of order. Consequently, selective retry plus chunking is both the technically correct recovery path and the lowest-cost option.
NEW QUESTION # 90
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 running for 3 weeks and human reviewers have corrected 847 extractions. Analysis reveals a recurring pattern: when recipes use informal measurements like "a handful" or "a splash," the model either invents specific amounts or leaves fields empty-accounting for 23% of all corrections.
How should you use this feedback to improve extraction accuracy?
Answer: D
Explanation:
The reviewer corrections have exposed a narrow and repeatable interpretation failure. The desired policy is clear: informal measurements are valid source values and must be preserved verbatim rather than normalized into invented quantities or treated as missing. This behavior can be communicated efficiently through targeted few-shot examples.
Anthropic recommends examples for demonstrating expected behavior and improving consistency. Examples can pair source phrases such as "a handful of spinach," "a splash of vinegar," and "a pinch of salt" with outputs that retain handful , splash , and pinch exactly. Additional counterexamples can show that Claude must not convert these phrases into grams, millilitres, or estimated serving quantities. ( https://docs.anthropic.
com/en/docs/about-claude/use-case-guides/ticket-routing )
Option A introduces a substantially heavier training workflow for a problem that can be addressed directly through the prompt. Option C creates a parallel extraction mechanism based on pattern matching; it will be brittle across linguistic variations and may populate a value without understanding its relationship to the correct ingredient. Option D adds useful classification metadata, but it does not instruct Claude to preserve the original measurement instead of inventing or omitting it.
The revised prompt should combine an explicit verbatim-extraction rule with several varied examples derived from the corrected cases, followed by regression evaluation against the identified failure set.
Official references/topics: Few-Shot Examples, Feedback-Driven Prompt Improvement, Verbatim Extraction, Regression Evaluation.
NEW QUESTION # 91
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