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| Section | Weight | Objectives |
|---|---|---|
| Context Management & Reliability | 15% | - Idempotency, consistency and failure resilience - Context pruning and summarization strategies - Context window optimization and prioritization - Token budget management and cost control |
| Prompt Engineering & Structured Output | 20% | - JSON schema design and structured output enforcement - System prompt design and persona alignment - Validation, parsing and retry loop strategies - Explicit criteria definition and few-shot prompting |
| Tool Design & MCP Integration | 18% | - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - Tool distribution and permission controls - MCP tool, resource and prompt implementation - Tool schema design and interface boundaries - Error handling and tool response formatting |
| Agentic Architecture & Orchestration | 27% | - Error recovery, guardrails and safety patterns - Agentic loop design and stop_reason handling - Session state management and workflow enforcement - Multi-agent patterns: coordinator-subagent and hub-and-spoke - Task decomposition and dynamic subagent selection |
| Claude Code Configuration & Workflows | 20% | - CI/CD integration and non-interactive mode parameters - CLAUDE.md hierarchy, precedence and @import rules - Custom slash commands and plan mode vs direct execution - Hooks vs advisory instructions - Path-specific rules and .claude/rules/ configuration |
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NEW QUESTION # 113
You have configured the system so that all four subagents have access to the complete set of 18 tools. During testing, agents frequently call tools outside their specialization-the synthesis agent attempts web searches, and the report generator tries to analyze documents. What is the primary cause of this poor tool-selection behavior?
Answer: D
Explanation:
Option A identifies the direct configuration contradiction. Each system prompt defines a specialized responsibility, but the corresponding tool allowlist presents unrelated actions as available capabilities. The synthesis agent may therefore interpret web searching as an acceptable way to fill evidence gaps, while the report generator may perform document analysis instead of limiting itself to report production.
Anthropic's subagent documentation defines subagents as focused workers with their own prompts and specific tool access. Its tool-definition guidance also recommends reducing selection ambiguity by presenting clear, relevant tools. The appropriate correction is least-capability configuration: expose search tools to the search agent, document tools to the analyzer, synthesis utilities to the synthesizer, and formatting or output tools to the report generator.
Option B may affect token efficiency but does not explain the role-specific misuse pattern. Option C concerns delegation, whereas the improper calls occur after successful delegation. Option D is tempting, but Anthropic does not define four or five tools as a universal reliability threshold; 18 tools alone does not prove threshold failure. The decisive defect is misalignment between declared roles and permitted capabilities.
NEW QUESTION # 114
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.
After deployment, you find that 12% of extractions contain semantic errors that pass JSON Schema validation-for example, a duration such as "30 minutes" is incorrectly placed in an ingredient-quantity field.
Human reviewers have the capacity to check only 20% of extractions.
Which approach most effectively allocates reviewer attention?
Answer: D
Explanation:
Option A uses the limited review capacity as a risk-ranking mechanism rather than spending it uniformly.
JSON Schema validation can prove that an output has the expected types and structure, but it cannot detect a semantically misplaced value such as a duration stored as an ingredient quantity. Field-level confidence can identify the exact parts of a record that warrant inspection, provided those scores are calibrated against labeled examples rather than trusted at face value. The labeled validation set must mirror the production distribution and include edge cases; Anthropic's evaluation guidance explicitly recommends task-specific evaluations, representative distributions, measurable criteria, and automated grading where possible. Calibration then converts raw confidence into an empirically tested estimate of error risk, allowing reviewers to inspect the lowest-confidence records or fields first. Option C, random sampling, is useful for unbiased quality measurement and discovering new failure modes, but it does not maximize error detection within a fixed 20 percent review budget. Options B and D target narrow proxies-layout anomalies and missing required values-while the observed errors pass structural validation and may occur in ordinary-looking documents.
The operational design should combine calibrated risk-based review with a smaller random audit sample to detect drift and overconfidence.
NEW QUESTION # 115
The automated review consistently flags patterns your team uses intentionally-force-unwrapping optionals in test files, using large coordinator classes that follow your established architecture, and importing internally maintained modules marked as deprecated in the public SDK. Developers are dismissing approximately 30% of all findings as project-specific false positives. Which approach prevents the model from generating these findings in the first place by supplying the project's conventions as persistent context during every review?
Answer: D
Explanation:
Option D supplies the missing repository-specific knowledge before Claude evaluates the code. The project's CLAUDE.md should state that force-unwrapping is permitted within test paths, explain the coordinator-class architecture, and identify the internally maintained deprecated module as an approved dependency. These instructions should be concise and scoped carefully so exceptions do not become global permissions.
Anthropic's CLAUDE.md documentation defines project CLAUDE.md files as persistent context for coding standards, architecture, workflows, and team conventions. They are loaded at the beginning of applicable sessions, preventing the organization from repeatedly supplying the same corrections. Anthropic also recommends recording information that Claude should know whenever it works in the repository.
Option A filters generated findings after tokens have already been consumed and can hide genuine issues containing similar terminology. Option B removes valuable surrounding context and would likely increase false positives and missed cross-file defects. Option C distributes project knowledge across individual source lines, creates maintenance overhead, and requires custom preprocessing. Centralized, narrowly scoped CLAUDE.md instructions address the root cause and improve the initial review judgment instead of repairing its output afterward.
NEW QUESTION # 116
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.
After the web-search and document-analysis subagents complete their tasks, the coordinator needs to spawn the synthesis subagent to synthesize the findings.
What is the correct approach for providing the synthesis subagent with the information it needs?
Answer: C
Explanation:
Option B follows the Claude Agent SDK's subagent context model. A normal subagent begins with a fresh context window and does not inherit the parent agent's conversation history or prior tool results. Anthropic's Subagents in the SDK documentation states that the information passed from parent to subagent is the spawning tool's prompt string; required file paths, decisions, errors, or findings must therefore be included in that prompt. In this scenario, the coordinator should supply both agents' relevant findings, source metadata, and explicit synthesis instructions. "Complete findings" means the full required result artifacts, not every intermediate search trace. Option C incorrectly assumes automatic context inheritance. Option A introduces callbacks that are neither necessary nor the standard handoff mechanism. Option D can be a valid custom architecture when a shared store has deliberately been implemented, but the question does not establish such infrastructure, and identifiers alone do not give the subagent information. The prompt should use clear sections or structured objects to distinguish web findings, document findings, sources, unresolved conflicts, and expected output. This preserves context isolation while providing everything the synthesis task actually requires.
NEW QUESTION # 117
During initial testing of the automated review pipeline, you notice that reviews of 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 after reaching either a fixed iteration count or a fixed dollar amount. Both limits must be enforced by Claude Code itself rather than by the surrounding job runner. Which configuration change directly enforces both per-invocation limits?
Answer: B
Explanation:
Option A is the only configuration that establishes both required limits inside Claude Code. The official CLI reference defines --max-turns as the maximum number of agentic turns permitted in print mode; Claude Code exits with an error when that limit is reached. It defines --max-budget-usd as the maximum dollar expenditure on API calls before execution stops, including applicable subagent expenditure.
These controls address different failure dimensions. The turn limit prevents an investigation from continuing through excessive read-search-analyze cycles, while the budget limit stops an invocation whose expensive turns consume the monetary allowance before reaching the turn ceiling. Supplying both therefore creates an effective per-run boundary.
Option B controls how permission requests are handled; it does not limit the number of already permitted tool calls or API expenditure. Option C applies an external wall-clock timeout and merely observes cost, violating the requirement that Claude Code itself enforce both limits. Option D lowers expected cost per turn but creates no hard ceiling: the agent can still perform many iterations and exceed the intended budget. A cheaper model also does not guarantee that the review will terminate within a predictable number of turns.
NEW QUESTION # 118
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