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| Section | Weight | Objectives |
|---|---|---|
| Prompt Engineering & Structured Output | 20% | - Validation, parsing and retry loop strategies - JSON schema design and structured output enforcement - Explicit criteria definition and few-shot prompting - System prompt design and persona alignment |
| Context Management & Reliability | 15% | - Token budget management and cost control - Context pruning and summarization strategies - Context window optimization and prioritization - Idempotency, consistency and failure resilience |
| Agentic Architecture & Orchestration | 27% | - Agentic loop design and stop_reason handling - Multi-agent patterns: coordinator-subagent and hub-and-spoke - Task decomposition and dynamic subagent selection - Session state management and workflow enforcement - Error recovery, guardrails and safety patterns |
| Tool Design & MCP Integration | 18% | - Tool schema design and interface boundaries - Tool distribution and permission controls - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - Error handling and tool response formatting - MCP tool, resource and prompt implementation |
| Claude Code Configuration & Workflows | 20% | - Custom slash commands and plan mode vs direct execution - Hooks vs advisory instructions - CI/CD integration and non-interactive mode parameters - CLAUDE.md hierarchy, precedence and @import rules - Path-specific rules and .claude/rules/ configuration |
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NEW QUESTION # 29
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: A
Explanation:
A structured handoff containing the customer details, order information, and the specific issue ensures the human agent has all relevant context to act immediately. This approach avoids delays or repeated clarification and preserves continuity when authority limits prevent the agent from completing the task.
NEW QUESTION # 30
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 automated code review is missing genuine bugs in pull requests. Investigation reveals that your review prompt includes the instruction: "Only flag critical issues that would definitely cause production failures. Ignore minor concerns and anything you are uncertain about." Developers confirm that some missed bugs are genuine logic errors that the model investigated but chose not to report. The team requires the review output to remain structured, with each finding tagged with metadata, and actionable.
Which prompt change both removes the cause of the suppressed findings and preserves structured, tagged output for downstream filtering?
Answer: B
Explanation:
Option B removes the prompt-level suppression responsible for the false negatives while preserving machine-readable metadata. Anthropic's current code-review prompting guidance warns that instructions such as "only report high-severity issues" or "be conservative" may be followed literally:
Claude can identify genuine defects during analysis but omit them from its output. Anthropic recommends requesting all findings and applying filtering separately.
Confidence and severity fields allow downstream code to apply adjustable thresholds without forcing the model to discard evidence during generation. A schema can require fields such as file, line, description, severity, confidence, evidence, and recommended action; Anthropic's Structured Outputs documentation supports enforcing such a response contract.
NEW QUESTION # 31
An organization wants predictable outputs for automated invoice classification. Which API parameter should generally be LOWER?
Answer: B
Explanation:
Lower temperature reduces randomness during token selection, producing more consistent and repeatable outputs. This is especially valuable in structured enterprise workflows where deterministic behavior is preferred over creative variation.
NEW QUESTION # 32
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: C
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 # 33
The web-search agent has gathered several relevant sources for a research topic. The document-analysis agent now needs to examine those sources. How does information typically flow between these two specialized subagents?
Answer: B
Explanation:
Option A matches the standard coordinator-managed subagent model. The coordinator invokes the web- search agent, receives its final result, selects the useful URLs or source records, and then supplies them explicitly when invoking the document-analysis agent. This keeps task ownership, provenance, and execution order visible to the coordinator.
The Claude Agent SDK subagent documentation states that a subagent begins with a fresh context window and does not inherit the parent's conversation history or previous tool results. The information passed through the Agent tool's prompt is the primary parent-to-subagent context channel. Consequently, the analysis prompt must include the source URLs, documents, retrieval notes, and any questions the analyzer must answer.
Option B requires nested delegation that is neither stated nor necessary and reduces centralized observability.
Options C and D describe valid custom architectures only if the application has deliberately implemented a queue or shared memory system; neither is automatic Agent SDK behavior. Coordinator-mediated handoff is therefore the expected approach. For reliability, the handoff should use structured source records rather than an informal prose statement, ensuring that the analyzer receives identifiers, provenance, relevant excerpts, and analysis objectives.
NEW QUESTION # 34
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