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| Section | Weight | Objectives |
|---|---|---|
| Context Management & Reliability | 15% | - Context pruning and summarization strategies - Idempotency, consistency and failure resilience - Token budget management and cost control - Context window optimization and prioritization |
| Prompt Engineering & Structured Output | 20% | - System prompt design and persona alignment - Explicit criteria definition and few-shot prompting - Validation, parsing and retry loop strategies - JSON schema design and structured output enforcement |
| Tool Design & MCP Integration | 18% | - Tool schema design and interface boundaries - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - MCP tool, resource and prompt implementation - Tool distribution and permission controls - Error handling and tool response formatting |
| Agentic Architecture & Orchestration | 27% | - Error recovery, guardrails and safety patterns - Task decomposition and dynamic subagent selection - Session state management and workflow enforcement - Multi-agent patterns: coordinator-subagent and hub-and-spoke - Agentic loop design and stop_reason handling |
| Claude Code Configuration & Workflows | 20% | - CLAUDE.md hierarchy, precedence and @import rules - Custom slash commands and plan mode vs direct execution - Path-specific rules and .claude/rules/ configuration - CI/CD integration and non-interactive mode parameters - Hooks vs advisory instructions |
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NEW QUESTION # 190
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.
You're implementing a complex graph traversal algorithm with specific performance requirements and edge cases to handle (disconnected nodes, cycles, weighted edges). You want to structure your workflow for efficient iterative refinement with Claude.
What approach will most effectively enable progressive improvement across multiple iterations?
Answer: B
Explanation:
Option C creates an objective verification loop. The tests encode expected traversal behavior for disconnected graphs, cycle handling, weighted edges, invalid inputs, and performance constraints. Claude can implement the algorithm, execute the suite, inspect concrete failures, and refine the implementation until the measurable conditions pass.
Anthropic emphasizes giving Claude a verification mechanism such as tests, builds, linters, or fixture comparisons. Without an executable pass-or-fail check, Claude can only determine that an implementation appears complete. With tests, it can perform work, evaluate the result, and iterate using evidence rather than subjective judgment. Anthropic also recommends reproducing defects with failing tests before applying corrections. ( https://code.claude.com/docs/en/best-practices ) Option A may produce a thoughtful initial design but does not guarantee progressive improvement after implementation. Option B risks inheriting assumptions or deficiencies from a reference that may not match the project's constraints. Option D depends on manual review and converts the developer into the primary verification system.
The test suite should include correctness fixtures, boundary cases, complexity-sensitive workloads, and regression tests added whenever a new failure is discovered. This makes every iteration cumulative: a correction must satisfy the new case without breaking previously validated behavior.
Official references/topics: Executable Verification; Test-Driven Iteration; Feedback Loops; Regression Testing.
NEW QUESTION # 191
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: B
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 # 192
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: A
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. Option A repeats the same suppressive instruction and is likely to reproduce the same omissions. Option C removes the explicit reporting structure and leaves filtering behavior undefined. Option D may improve analysis depth, but extended reasoning does not override a direct instruction to suppress uncertain findings. Separating detection from deterministic filtering preserves recall, structure, and operational control.
NEW QUESTION # 193
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: C
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 # 194
A legal department requires Claude to answer only when sufficient evidence exists in retrieved documents. What should Claude do if evidence is insufficient?
Answer: C
Explanation:
When reliable supporting evidence is unavailable, Claude should explicitly acknowledge uncertainty rather than speculate. Transparent handling of missing information builds trust and reduces the risk of presenting unsupported legal guidance.
NEW QUESTION # 195
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