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| Section | Weight | Objectives |
|---|---|---|
| Prompt Engineering & Structured Output | 20% | - Explicit criteria definition and few-shot prompting - System prompt design and persona alignment - Validation, parsing and retry loop strategies - JSON schema design and structured output enforcement |
| Context Management & Reliability | 15% | - Context pruning and summarization strategies - Idempotency, consistency and failure resilience - Token budget management and cost control - Context window optimization and prioritization |
| Agentic Architecture & Orchestration | 27% | - Multi-agent patterns: coordinator-subagent and hub-and-spoke - Task decomposition and dynamic subagent selection - Error recovery, guardrails and safety patterns - Agentic loop design and stop_reason handling - Session state management and workflow enforcement |
| Claude Code Configuration & Workflows | 20% | - Path-specific rules and .claude/rules/ configuration - CLAUDE.md hierarchy, precedence and @import rules - CI/CD integration and non-interactive mode parameters - Hooks vs advisory instructions - Custom slash commands and plan mode vs direct execution |
| Tool Design & MCP Integration | 18% | - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - MCP tool, resource and prompt implementation - Tool distribution and permission controls - Tool schema design and interface boundaries - Error handling and tool response formatting |
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NEW QUESTION # 103
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.
Your agent is handling a billing dispute. After calling get_customer and lookup_order , it identifies that the dispute involves a promotional pricing error requiring manager approval-beyond the agent's authorization level.
How should the workflow handle this mid-process escalation?
Answer: D
Explanation:
A mid-process escalation should transfer the decision-ready state accumulated by the agent. The human reviewer needs the verified customer identity, relevant order information, the promotional-pricing discrepancy, the reason approval is required, and any actions already attempted. Option B preserves this information in a concise, structured handoff while avoiding unnecessary repetition of the complete raw transcript.
Anthropic's tool-design guidance recommends returning high-signal information and stable identifiers containing only what Claude or the next workflow participant needs to determine the next action. Anthropic's context-engineering guidance similarly advocates structured notes that preserve critical state and dependencies without retaining every redundant tool result. A structured escalation payload applies both principles and reduces handling time for the manager. ( https://platform.claude.com/docs/en/agents-and-tools
/tool-use/define-tools )
Option A discards the investigation already completed. Option C violates the agent's authorization boundary and risks an impermissible financial action. Option D provides auditability, but a reference ID alone forces the human to reconstruct the case from an excessively broad transcript. Human control must remain meaningful when an agent encounters a decision outside its authority; the agent should pause and hand the decision back with sufficient supporting context. ( https://www.anthropic.com/research/trustworthy-agents ) Official references/topics: Structured agent handoffs, high-signal tool results, human-control boundaries, persistent structured state.
NEW QUESTION # 104
A company is building its first production Claude application. Which principle should guide the initial deployment?
Answer: C
Explanation:
Successful enterprise AI projects usually begin with a straightforward architecture that can be measured and refined over time. Iterative evaluation enables teams to improve prompts, retrieval, safety controls, and user experience using real-world feedback rather than unnecessary initial complexity.
NEW QUESTION # 105
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:
Option B removes the ambiguity from the tool interface itself. The agent no longer needs to choose between separate functions that represent closely related business operations. Instead, a single compensation tool exposes an explicit action parameter, while the existing order lookup tool can optionally return delivery- tracking information through a clearly defined flag.
Anthropic's tool-design guidance recommends consolidating related operations into fewer, more capable tools when separate functions create unnecessary semantic overlap. Claude selects tools primarily from their names, descriptions, schemas, and the current task. When multiple tools appear capable of satisfying the same request, selection accuracy declines because the agent must infer distinctions that should have been made explicit in the interface design.
Option A adds routing complexity but leaves overlapping financial operations available within the same sub- agent. Option C reduces the number of tools loaded initially, but it does not eliminate the semantic duplication between compensation operations or overlapping lookup functions. Option D may improve behavior through examples, but it compensates for a poorly designed tool surface rather than correcting the root cause.
The consolidated schemas should use enums for supported actions, clearly define when each action applies, and document the returned fields. This creates a smaller and more deterministic agent-computer interface.
Official references/topics: Tool consolidation, semantic tool boundaries, action parameters, MCP tool- selection accuracy.
NEW QUESTION # 106
You are building developer productivity tools using the Claude Agent SDK. The agent helps engineers explore unfamiliar codebases, understand legacy systems, generate boilerplate code, and automate repetitive tasks. It uses the built-in tools (Read, Write, Bash, Grep, Glob) and integrates with Model Context Protocol (MCP) servers.
After adding an MCP server with specialized code refactoring tools (extract_function, rename_variable, inline_function), You notice the agent still uses basic text manipulation via Write and Bash sed commands for refactoring tasks. The MCP server is connected and healthy.
Examining the configuration, you find each MCP tool has a minimal description like
"extract_function: Extracts a function from code."
What's the most effective way to improve adoption of the MCP refactoring tools?
Answer: A
Explanation:
The minimal descriptions do not distinguish the specialized refactoring tools from general- purpose Write or Bash. Anthropic recommends detailed descriptions that specify what a tool does, when to use it, when not to use it, and its inputs and outputs.
NEW QUESTION # 107
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.
A customer contacts the agent about a warranty claim on a power drill. Resolving this requires multiple sequential tool calls: get_customer to look up their account, lookup_order to find the purchase details, and then either process_refund or escalate_to_human depending on warranty eligibility. You're implementing the agentic loop that orchestrates these steps using the Claude API.
What is the primary mechanism your application uses to determine whether to continue the loop or stop?
Answer: B
Explanation:
The Messages API exposes stop_reason specifically so the application can determine why Claude stopped generating and what action is required next. A value of tool_use means Claude is requesting one or more tools and expects the application to execute them and return corresponding tool_result blocks. A value of end_turn means Claude has naturally completed the response. ( https://platform.claude.com/docs/en/build-with-claude
/handling-stop-reasons )
Anthropic's agent-loop tutorial implements this as a loop that continues executing tools and returning results while stop_reason remains tool_use . Once the value changes, the response is treated according to the new terminal or continuation condition. ( https://platform.claude.com/docs/en/agents-and-tools/tool-use/build-a- tool-using-agent ) Option A is unreliable because an assistant response can contain both text and tool-use blocks. Text alone does not prove that the workflow is complete. Option B requires the application to predict that no further tool call is needed, defeating Claude's ability to adapt from tool results. Option D is an important safety limit but is not the primary completion signal; an arbitrary maximum may terminate a valid workflow prematurely.
The harness should check stop_reason , process every requested tool call, append results to the conversation, and maintain a separate maximum-turn safeguard against runaway loops.
Official references/topics: Messages API stop reasons, tool-use loops, tool-result continuation, bounded orchestration.
NEW QUESTION # 108
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