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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Agentic Architecture & Orchestration | 27% | - Agentic loop design and stop_reason handling - Task decomposition and dynamic subagent selection - Session state management and workflow enforcement - Multi-agent patterns: coordinator-subagent and hub-and-spoke - Error recovery, guardrails and safety patterns |
| Topic 2: Context Management & Reliability | 15% | - Idempotency, consistency and failure resilience - Context window optimization and prioritization - Context pruning and summarization strategies - Token budget management and cost control |
| Topic 3: Tool Design & MCP Integration | 18% | - MCP tool, resource and prompt implementation - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - Tool schema design and interface boundaries - Tool distribution and permission controls - Error handling and tool response formatting |
| Topic 4: Claude Code Configuration & Workflows | 20% | - Hooks vs advisory instructions - CI/CD integration and non-interactive mode parameters - Path-specific rules and .claude/rules/ configuration - Custom slash commands and plan mode vs direct execution - CLAUDE.md hierarchy, precedence and @import rules |
| Topic 5: Prompt Engineering & Structured Output | 20% | - Explicit criteria definition and few-shot prompting - JSON schema design and structured output enforcement - System prompt design and persona alignment - Validation, parsing and retry loop strategies |
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NEW QUESTION # 37
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.
After deploying automated code review, developers report that approximately 35% of findings are false positives following consistent patterns: style suggestions that contradict team conventions, security warnings for patterns that are safe in the deployment environment, and performance suggestions that would degrade this particular use case.
You want to reduce false positives while enabling the model to generalize its judgment to novel code patterns it has not seen before.
Which approach is most effective?
Answer: D
Explanation:
Option B demonstrates the decision boundary Claude must learn. Carefully selected examples can show structurally similar code producing different outcomes based on project context-for example, an approved authentication wrapper versus an unsafe direct call, or a deliberate performance trade-off versus an accidental quadratic operation. These contrasts help Claude apply the underlying judgment to new code rather than merely memorizing prohibited phrases.
Anthropic identifies relevant, diverse, and clearly structured examples as one of the most reliable methods for improving output accuracy and consistency. It recommends several examples that mirror the real use case and cover important edge conditions. Option A risks creating an oversized negative catalogue that consumes context, becomes difficult to maintain, and cannot anticipate every future variation. Option C filters text after generation and may suppress genuine findings that happen to use the selected keywords. Option D is dangerously vague: telling a reviewer to be conservative can suppress real but uncertain defects and reduce recall. The prompt should provide paired acceptable/problematic examples, explain why each classification differs, and require concrete code evidence for every reported finding. Anthropic prompting best practices
NEW QUESTION # 38
Your multi-agent research pipeline crashed after processing12 of 28 documents. The web search agent had identified relevant sources, the document analyzer had partially completed extraction, and the synthesizer had begun pattern identification. You need to resume processing without repeating work or losing fidelity of prior findings. What state management approach best balances information fidelity with context efficiency when restoring agent state?
Answer: D
Explanation:
Persisting structured exports and a coordinator manifest preserves completed work in a reliable, resumable format. On recovery, the coordinator can reload only the relevant state for each agent, avoiding repeated processing while maintaining fidelity of prior findings without flooding prompts with full conversation logs.
NEW QUESTION # 39
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.
During initial testing of the automated review pipeline, you notice that reviews on large pull requests containing more than 50 changed files sometimes take over 20 minutes and cost $8-$12 per run because of extensive agentic loops. Claude reads files, runs analysis tools, and iterates many times. Your team needs each invocation to abort once it reaches both a fixed iteration count and a fixed dollar amount, enforced by Claude Code itself rather than by the surrounding job runner.
Which configuration change directly enforces both per-invocation caps?
Answer: B
Explanation:
Option C applies the two native stopping controls required by the question. Anthropic's Claude Code CLI reference defines --max-turns as the maximum number of agentic turns permitted in print mode and --max- budget-usd as the maximum API expenditure for that invocation. When the turn limit is reached, the run exits with an error. Spending by subagents counts toward the budget cap, and current Claude Code versions stop remaining background subagents when the limit is reached. These flags therefore control the complete invocation rather than merely one model request.
Option A lowers expected cost per iteration but establishes no hard ceiling on either iterations or total expenditure. Option B limits wall-clock execution at the CI-runner level and only observes spending retrospectively; it does not enforce a Claude Code budget. Option D controls permission behavior, not iteration count, execution time, or token spending. The pipeline should use both flags, capture the resulting nonzero exit or structured error status, and report whether a review ended normally or was terminated by a configured operational limit.
NEW QUESTION # 40
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: B
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 # 41
When implementing your lookup_order MCP tool, the backend sometimes returns errors-for example,
"Order not found" or temporary database failures. What is the correct pattern for communicating these errors back to the agent?
Answer: D
Explanation:
Option A follows the MCP error contract. An error originating during execution of a valid tool call-such as an API failure, input-validation problem, or business-logic failure-should be returned inside the tool result with isError: true. The content should explain what happened and, where possible, provide actionable recovery guidance.
The official MCP tools specification distinguishes tool-execution errors from protocol-level errors and specifies that execution failures are reported through tool results using isError: true. Anthropic's tool-using agent tutorial provides the equivalent Claude API pattern using is_error: true, allowing Claude to retry with corrected input, request clarification, or explain the limitation.
Option B falsely represents a failed operation as successful, weakening reliable failure detection. Option C deprives the agent of the information required to determine an appropriate recovery action. Option D may be converted into a tool error by some SDK runners, but uncaught exceptions are not the MCP-level response contract. The strongest implementation returns a concise, sanitized, actionable error result and separately records detailed diagnostics in server logs.
NEW QUESTION # 42
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