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| Section | Weight | Objectives |
|---|---|---|
| Agentic Architecture & Orchestration | 27% | - Multi-agent patterns: coordinator-subagent and hub-and-spoke - Session state management and workflow enforcement - Error recovery, guardrails and safety patterns - Agentic loop design and stop_reason handling - Task decomposition and dynamic subagent selection |
| Prompt Engineering & Structured Output | 20% | - JSON schema design and structured output enforcement - Validation, parsing and retry loop strategies - System prompt design and persona alignment - Explicit criteria definition and few-shot prompting |
| Context Management & Reliability | 15% | - Context window optimization and prioritization - Idempotency, consistency and failure resilience - Token budget management and cost control - Context pruning and summarization strategies |
| Tool Design & MCP Integration | 18% | - MCP tool, resource and prompt implementation - Tool distribution and permission controls - Error handling and tool response formatting - Tool schema design and interface boundaries - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 |
| Claude Code Configuration & Workflows | 20% | - Path-specific rules and .claude/rules/ configuration - Hooks vs advisory instructions - CLAUDE.md hierarchy, precedence and @import rules - Custom slash commands and plan mode vs direct execution - CI/CD integration and non-interactive mode parameters |
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NEW QUESTION # 73
The coordinator agent has AgentDefinition objects configured for all four specialized subagents, each with appropriate descriptions, prompts, and tool restrictions. During testing, you notice that the coordinator correctly reasons about when to delegate-it generates messages such as, "I'll ask the web-search agent to find sources on this topic"-but no subagent execution 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
Explanation:
Option B identifies the missing executable capability. Defining specialized agents makes their configurations available, but the coordinator must still be permitted to call the tool that invokes them. Without that tool, Claude can describe an intended delegation in ordinary text but cannot create a subagent execution.
The current Claude Agent SDK documentation requires " Agent " in allowedTools to auto-approve subagent invocations. The tool was renamed from " Task " to " Agent " in Claude Code 2.1.63, so the terminology in the original candidate question required correction. Older integrations may still expose " Task " in initialization or permission records.
Option A is unlikely because properly written AgentDefinition.description values already tell Claude when each agent should be used. Option C misinterprets context isolation: the parent supplies the subagent's assignment through the Agent tool's prompt, and no additional automatic forwarding setting is required.
Option D would normally produce truncation evidence or a max_tokens stop reason rather than silent absence of every invocation. The coordinator needs both agent definitions and permission to use the invocation tool.
NEW QUESTION # 74
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.
Production logs show that when the agent handles complex billing disputes requiring 6+ tool calls, it sometimes exhausts its max_turns limit after gathering data but before completing resolution or escalating.
The team's goal is to guarantee that every customer interaction ends with either a completed resolution or a human handoff, regardless of how the agent loop terminates.
Which approach achieves this guarantee?
Answer: C
Explanation:
Option C is the only approach that guarantees the required terminal condition independently of the model's behavior. When the agent loop stops, the orchestration layer evaluates the recorded outcome. If neither a successful resolution nor a confirmed human escalation occurred, deterministic application code invokes escalate_to_human using the information accumulated before termination.
The max_turns limit is a host-controlled safety boundary. Once that boundary is reached, the model may no longer have an opportunity to follow a prompt instruction or make another tool call. Therefore, the required fallback cannot depend entirely on Claude deciding to escalate before its remaining turns are exhausted.
Option A escalates based on an arbitrary 80% threshold and may prematurely terminate cases that could have been resolved successfully within the remaining budget. Option B provides additional turn capacity but does not guarantee that the second agent will complete its work or escalate before its own limit is reached. Option D remains probabilistic and cannot operate after an unexpected termination has already occurred.
The orchestration fallback should be idempotent, ensuring that repeated completion checks cannot create duplicate escalation records. It should also include verified customer information, relevant order data, completed actions, failure reasons, and the unresolved request in the handoff payload.
Official references/topics: Agent-loop termination, deterministic fallback orchestration, terminal-state validation, human handoff guarantees.
NEW QUESTION # 75
You are integrating Claude Code into your Continuous Integration/Continuous Deployment (CI/CD) pipeline. The system runs automated code reviews, generates test cases, and provides feedback on pull requests. You need to design prompts that provide actionable feedback and minimize false positives.
Your pipeline runs:
PROMPT='You are a code reviewer. Analyze the provided diff for bugs,
security issues, and style violations.'
claude -p \
--dangerously-skip-permissions \
--system-prompt "$PROMPT" \
< diff.txt
The reviews complete and return feedback, but Claude only comments on the piped diff text--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 inspect related repository files while still applying your custom review instructions?
Answer: C
Explanation:
Option D preserves Claude Code's standard coding-agent instructions while adding the specialized review criteria. Anthropic documents that --system-prompt replaces the entire default system prompt, including its tool guidance, safety instructions, and coding conventions. It does not technically disable tools, but removing that guidance can make the invocation behave like a narrowly scoped text processor. --append-system-prompt retains the default behavior and layers the review instructions on top.
The prompt should explicitly direct Claude to use Read, Glob, and Grep to inspect definitions, callers, tests, and related modules whenever the diff alone is insufficient.
NEW QUESTION # 76
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.
Your invoice extraction uses tool use with strict JSON schemas. JSON syntax errors never occur, but 12% of extractions fail semantic validation-for example, line-item amounts do not sum to the extracted total, or vendor IDs do not match valid formats. These failures currently route to manual review.
What is the most effective approach to reduce manual-review volume while maintaining accuracy?
Answer: C
Explanation:
Option B creates a targeted correction loop using information the first extraction did not have: the validator's precise failure report. Anthropic's prompting guidance describes prompt chaining as appropriate when an application must inspect intermediate output or enforce a specific pipeline, with self-correction following the pattern generate, review against criteria, and refine. Here, deterministic validators identify arithmetic mismatches and invalid vendor-ID formats. Returning the source document, original extraction, and explicit errors lets Claude revise only the defective fields while preserving valid data. The corrected record must then be validated again before acceptance, with a bounded retry count and human-review fallback. Option A is unsafe because automatically recalculating a total may overwrite the amount actually printed on the invoice; a mismatch can originate in an OCR or line-item extraction error. Option C repeats the same extraction without explaining what failed, wasting calls and relying on chance. Option D may prevent some format violations, but JSON Schema cannot express every cross-field arithmetic relationship or external vendor-registry rule. A validator-guided second pass therefore reduces manual review without silently changing source facts, while keeping deterministic code-not the model-as the final acceptance authority.
NEW QUESTION # 77
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 their account. What should the agent do?
Answer: C
Explanation:
The customer has made an explicit and urgent request to speak with a human after repeated unresolved attempts. The agent should respect that request immediately and pass the available conversation history so the human agent can continue without forcing the customer to repeat themselves.
NEW QUESTION # 78
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