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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 - Session state management and workflow enforcement - Agentic loop design and stop_reason handling |
| Topic 2: Context Management & Reliability | 15% | - Idempotency, consistency and failure resilience - Context pruning and summarization strategies - Token budget management and cost control - Context window optimization and prioritization |
| Topic 3: Claude Code Configuration & Workflows | 20% | - CLAUDE.md hierarchy, precedence and @import rules - Hooks vs advisory instructions - CI/CD integration and non-interactive mode parameters - Custom slash commands and plan mode vs direct execution - Path-specific rules and .claude/rules/ configuration |
| Topic 4: Tool Design & MCP Integration | 18% | - Tool schema design and interface boundaries - MCP tool, resource and prompt implementation - Tool distribution and permission controls - Error handling and tool response formatting - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 |
| Topic 5: Prompt Engineering & Structured Output | 20% | - JSON schema design and structured output enforcement - Explicit criteria definition and few-shot prompting - Validation, parsing and retry loop strategies - System prompt design and persona alignment |
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NEW QUESTION # 160
Production monitoring shows that follow-up queries such as "summarize what we learned about market trends" consistently take more than 40 seconds. Investigation reveals that the coordinator spawns the synthesis subagent for every summarization request, passing more than 80,000 tokens of accumulated findings. The coordinator already has these findings in its context from orchestrating the research. What is the most effective way to improve response time for these follow-up summaries?
Answer: B
Explanation:
Option C avoids an unnecessary agent boundary. The coordinator already possesses the accumulated findings and can perform a straightforward summary without serializing, transferring, and reprocessing more than
80,000 tokens in another context window. Subagents should be reserved for work that requires isolated context, specialized instructions, separate tools, or an independent analytical process.
Anthropic's current prompting guidance advises using subagents for independent workstreams and parallel or context-isolated tasks, while handling simpler tasks directly. Anthropic also notes that excessive subagent use creates unnecessary cost and latency.
Option A generates multiple summaries speculatively, consuming resources even if they are never requested and creating cache-invalidation complexity whenever findings change. Option B may reduce repeated input- token cost, but it does not eliminate subagent startup, message processing, or the unnecessary orchestration round trip. Option D introduces an iterative request protocol that will likely increase latency further. Direct coordinator summarization uses information already available in active context and therefore provides the smallest architectural change, lowest token-transfer overhead, and fastest response while preserving subagent synthesis for genuinely complex comparative or cross-source analysis.
NEW QUESTION # 161
In addition to your CI pipeline, your organization has enabled Claude's managed Code Review through the Claude GitHub App on this repository, and reviews run automatically on every pull request. Reviews average
18 findings per pull request. Developer feedback reveals three categories of unwanted noise: (1) style and formatting issues already enforced by your CI linter, (2) findings on automatically generated template code under src/gen/*, and (3) rendering-helper patterns that are intentional project conventions but are flagged because they resemble common anti-patterns. Only approximately four findings per pull request are genuine logic bugs. What is the most effective way to reduce this noise while preserving the detection of real issues?
Answer: A
Explanation:
Option A uses the dedicated configuration mechanism for Anthropic's managed Code Review service. The official Code Review documentation states that a root-level REVIEW.md is injected into every review agent as the highest-priority instruction block. It can redefine severity, suppress categories already enforced by CI, skip generated paths, and require concrete evidence before particular findings are reported.
The proposed rules map directly to the observed noise. Skipping lint and formatting findings eliminates duplication with deterministic CI checks. Excluding src/gen/* prevents comments on machine-generated code. Requiring a specific source line demonstrating incorrect behavior raises the verification threshold for rendering-helper findings without suppressing genuine defects.
Options B and C configure self-hosted GitHub Actions workflows, but the scenario concerns managed Code Review running through the Claude GitHub App on Anthropic's infrastructure. Those workflow prompts do not govern the managed reviewer. Option D supplies useful general project context, but CLAUDE.md applies to all Claude Code tasks and has lower review-specific authority. The documentation states that REVIEW.md is the stronger control for changing what managed Code Review flags and how findings are reported. It therefore provides the most targeted solution with the least unnecessary infrastructure.
NEW QUESTION # 162
You are using Claude Code to accelerate software development. Your team uses it for code generation, refactoring, debugging, and documentation. You need to integrate it into your development workflow with custom slash commands, CLAUDE.md configurations, and understand when to use plan mode vs direct execution.
A critical bug is affecting production users. Error logs show exceptions in the OrderProcessing module with a clear stack trace pointing to a specific function. You haven't worked with this module before. What's the most effective approach?
Answer: C
Explanation:
Because the module is unfamiliar, plan mode allows safe, read-only investigation of the stack trace, relevant code, and dependencies before changing production code. Anthropic recommends planning when working with unfamiliar code.
NEW QUESTION # 163
You are building a multi-agent research system using the Claude Agent SDK. A coordinator agent delegates to specialized subagents: one searches the web, one analyzes documents, one synthesizes findings, and one generates reports. The system researches topics and produces comprehensive, cited reports.
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 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: C
Explanation:
Option C matches the distinction between reasoning about delegation and executing it. Defining subagents makes their descriptions available for selection, but the coordinator must still invoke the SDK's subagent- spawning tool. Current Claude Agent SDK documentation calls this the Agent tool; Task was its earlier name and remains relevant to older SDK configurations. Anthropic's Subagents in the SDK documentation instructs developers to include Agent in allowedTools so subagent invocations are approved automatically. Without that permission, an invocation can fall through to a permission callback or be denied under a non-interactive permission mode. Option A is unlikely because the configured subagent descriptions already tell Claude when each agent should be selected, although explicit prompting can improve invocation reliability. Option B misstates context isolation: context must be included in the spawning prompt, but that issue occurs after an invocation is attempted and does not explain the absence of all subagent executions. Option D would normally produce truncation evidence or incomplete output rather than consistent verbal promises with no tool call. The configuration should therefore permit Agent, explicitly request delegation where necessary, and log subagent invocation events.
NEW QUESTION # 164
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.
In addition to your CI pipeline, your organization has enabled Claude's managed Code Review through the Claude GitHub App on this repository, and reviews run automatically on every pull request. Reviews average 18 findings per pull request. Developer feedback reveals three categories of unwanted noise: (1) style and formatting issues already enforced by your linter in CI, (2) findings on automatically generated template code under src/gen/, and (3) rendering- helper patterns that are intentional project conventions but get flagged because they resemble common anti-patterns. Only approximately four findings per pull request are genuine logic bugs.
What is the most effective way to reduce this noise while preserving the detection of genuine issues?
Answer: A
Explanation:
Option A uses the dedicated control surface for managed Claude Code Review. Anthropic's Code Review documentation states that a root-level REVIEW.md is injected into every review agent as the highest-priority instruction block. It can define skip paths, suppress categories already enforced by CI, recalibrate severity, cap nit volume, and require source evidence before reporting particular findings. The documentation explicitly identifies generated code, linting, and verification requirements as appropriate uses.
NEW QUESTION # 165
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