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| Section | Weight | Objectives |
|---|---|---|
| Claude Code Configuration & Workflows | 20% | - CI/CD integration and non-interactive mode parameters - CLAUDE.md hierarchy, precedence and @import rules - Path-specific rules and .claude/rules/ configuration - Hooks vs advisory instructions - Custom slash commands and plan mode vs direct execution |
| Prompt Engineering & Structured Output | 20% | - System prompt design and persona alignment - JSON schema design and structured output enforcement - Validation, parsing and retry loop strategies - Explicit criteria definition and few-shot prompting |
| Agentic Architecture & Orchestration | 27% | - Session state management and workflow enforcement - 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 |
| Context Management & Reliability | 15% | - Context pruning and summarization strategies - Context window optimization and prioritization - Idempotency, consistency and failure resilience - Token budget management and cost control |
| Tool Design & MCP Integration | 18% | - Error handling and tool response formatting - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - Tool schema design and interface boundaries - Tool distribution and permission controls - MCP tool, resource and prompt implementation |
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NEW QUESTION # 116
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 operating with 100% human review for 3 months. Analysis shows that extractions with model confidence #90% have 97% accuracy overall. To reduce reviewer workload, you plan to automate high- confidence extractions.
Before deploying, what validation step is most critical?
Answer: D
Explanation:
An aggregate accuracy value can conceal severe performance disparities. A system may achieve 97% overall accuracy while performing poorly on a low-volume document type, a critical financial field, or a specific edge case. Automating outputs solely from the aggregate figure could therefore expose downstream systems to concentrated, high-impact errors.
Anthropic's evaluation guidance states that evaluations should be task-specific, reflect the real-world task distribution, and explicitly include edge cases. It also emphasizes multidimensional success criteria rather than reliance on a single global metric. ( https://docs.anthropic.com/en/docs/build-with-claude/develop-tests ) Option A applies those principles by stratifying performance according to document type and field. This reveals whether confidence is calibrated consistently and whether the proposed automation threshold remains safe for every operationally significant segment.
Option B is useful only after segment-level performance has been understood. Selecting a global threshold cannot correct a subgroup where confidence is systematically overstated. Option C is necessary governance work, but it treats the overall 97% result as though errors were uniformly distributed. Option D places unvalidated outputs into downstream systems and depends on passive error reporting, which may fail to detect silent corruption.
The correct deployment gate is therefore segmented validation, followed by threshold selection, downstream acceptance criteria, and a controlled pilot.
Official references/topics: Define Success Criteria; Task-Specific Evaluations; Edge-Case Coverage; Reliability Segmentation.
NEW QUESTION # 117
A prompt asks Claude to produce Markdown tables. Which addition MOST increases consistency?
Answer: A
Explanation:
Explicit structural requirements reduce ambiguity. Providing exact column names, ordering, and formatting expectations helps Claude consistently generate Markdown tables that downstream users or systems can reliably consume.
NEW QUESTION # 118
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: D
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 # 119
The coordinator agent has AgentDefinitions configured for all four specialized subagents, each with appropriate descriptions, prompts, and tool restrictions. During testing, you notice the coordinator correctly reasons about when to delegate - it generates messages like "I'll ask the web search agent to find sources on this topic" - but no subagent execution ever occurs. The coordinator then proceeds as if the delegation happened and continues with incomplete information. Logs show no errors. What is the most likely cause?
Answer: D
NEW QUESTION # 120
Your pipeline runs:
PROMPT="You are a code reviewer."
PROMPT="$PROMPT Analyze the provided diff"
PROMPT="$PROMPT for bugs, security issues,"
PROMPT="$PROMPT and style violations."
claude -p \
--dangerously-skip-permissions \
--system-prompt "$PROMPT" < diff.txt
The reviews complete and return feedback, but Claude comments only on the piped diff--it never reads surrounding files in the checked-out repository to understand broader context, even when the diff modifies a function called by many other modules. Which change to the invocation will cause Claude to read related repository files while still applying your custom review instructions?
Answer: C
Explanation:
The --system-prompt flag replaces Claude Code's complete default system prompt. Although this does not technically remove the available tools, it discards the default coding-agent guidance that tells Claude how to gather repository context, navigate code, use tools, and verify findings. The replacement prompt says only to analyze the supplied diff, so the observed diff-only behaviour is consistent with the configured instructions.
Option B preserves the standard Claude Code identity and tool guidance while adding the custom review criteria. The Claude Code CLI reference states that --append-system-prompt appends instructions to the default prompt, whereas --system-prompt replaces it. Anthropic specifically recommends appending when Claude should remain a coding assistant that follows additional per-invocation rules.
NEW QUESTION # 121
......
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