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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 |
| Topic 2: Agentic Architecture & Orchestration | 27% | - Multi-agent patterns: coordinator-subagent and hub-and-spoke - Error recovery, guardrails and safety patterns - Task decomposition and dynamic subagent selection - Agentic loop design and stop_reason handling - Session state management and workflow enforcement |
| Topic 3: Prompt Engineering & Structured Output | 20% | - JSON schema design and structured output enforcement - Validation, parsing and retry loop strategies - System prompt design and persona alignment - Explicit criteria definition and few-shot prompting |
| Topic 4: Tool Design & MCP Integration | 18% | - 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 - Tool schema design and interface boundaries |
| Topic 5: Claude Code Configuration & Workflows | 20% | - CLAUDE.md hierarchy, precedence and @import rules - CI/CD integration and non-interactive mode parameters - Hooks vs advisory instructions - Custom slash commands and plan mode vs direct execution - Path-specific rules and .claude/rules/ configuration |
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NEW QUESTION # 12
A legal department requires Claude to answer only when sufficient evidence exists in retrieved documents. What should Claude do if evidence is insufficient?
Answer: A
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 # 13
Your multi-agent research pipeline crashed after processing 12 of 28 documents. The web-search agent had identified relevant sources, the document analyzer had partially completed extraction, and the synthesizer had begun identifying patterns. You need to resume processing without repeating work or losing fidelity in the prior findings. What state-management approach best balances information fidelity with context efficiency when restoring agent state?
Answer: B
Explanation:
Option A combines lossless persistence with selective restoration. Each agent should checkpoint structured records containing completed document identifiers, extracted findings, source provenance, unresolved work, verification status, and schema version. A coordinator-level manifest can record which stages completed and where their artifacts are stored. On recovery, the coordinator skips completed documents and injects only the state required by each restarted agent.
Anthropic's effective context-engineering guidance recommends structured notes stored outside the active context window and reloaded when needed. This preserves critical dependencies and progress without replaying an entire execution history.
Option B fragments recovery ownership across agents and makes cross-agent consistency, versioning, and dependency validation difficult. Option C preserves extensive logs but wastes context on redundant tool calls, obsolete instructions, and intermediate messages. Option D is useful for relevance retrieval but semantic search can omit exact details and should not serve as the authoritative recovery record. Structured checkpoints and a validated manifest provide deterministic recovery, efficient context reconstruction, auditability, and precise provenance. Writes should be atomic so partially produced exports are never mistaken for completed work.
NEW QUESTION # 14
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.
Your team frequently migrates React components to Vue. You've written a step-by-step workflow for Claude Code to follow during each migration, and you want every developer on the team to invoke it by typing
/migrate-component . The workflow should stay in sync as the team iterates on it.
Where should you place the skill file?
Answer: C
Explanation:
Project skills are stored at .claude/skills/ < skill-name > /SKILL.md . The directory name becomes the invocable command name, so .claude/skills/migrate-component/SKILL.md creates /migrate-component .
Anthropic distinguishes project skills from personal skills: project skills apply only to the repository, while skills under ~/.claude/skills/ apply to one user across all projects. ( https://code.claude.com/docs/en/skills ) Committing the project skill to version control ensures that every developer receives the same workflow and that changes are reviewed, versioned, and synchronized with the codebase. Anthropic's .claude directory guidance states that project configuration should be committed when it is intended to be shared with the team, whereas files under ~/.claude are personal. ( https://code.claude.com/docs/en/claude-directory ) Option A requires manual duplication across developer machines and allows versions to diverge. Option B loads the migration procedure into every conversation even when no migration is occurring; Anthropic recommends skills for reusable checklists and multi-step procedures because their bodies load only when used. Option C describes no valid mechanism for defining a skill's content in settings.json .
Option D provides the correct scope, command name, on-demand loading behavior, and team distribution model.
Official references/topics: Project skills, SKILL.md structure, command invocation, version-controlled team configuration.
NEW QUESTION # 15
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: D
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 # 16
After deploying the automated review, you notice high precision but low recall-real bugs are slipping through undetected. Investigation reveals that your review prompt instructs Claude to "only report high- confidence issues you are certain about" and "err on the side of not commenting." Developers appreciate the low noise, but a race condition that caused a production outage was visible in a reviewed pull request and went unreported. You need to substantially improve bug detection while keeping false-positive rates manageable. What is the most effective approach?
Answer: B
Explanation:
The prompt's conservative reporting policy is directly causing the low recall. Claude may discover a legitimate race condition during analysis but suppress it because it cannot satisfy the instruction to report only issues about which it is certain. Option C separates two objectives that should not be conflated: broad defect discovery and strict acceptance filtering.
Anthropic's current code-review prompting guidance explicitly recommends reporting every issue, including uncertain or lower-severity findings, assigning confidence and severity metadata, and allowing a separate verification stage to filter them. This maximizes recall while retaining control over developer-facing noise.
Option A leaves the suppression policy intact, so additional examples cannot guarantee that discovered problems will be reported. Option B improves recall but relies on historical category-level suppression, which can discard genuine findings that happen to belong to noisy categories. Option D can improve analysis quality but does not remove the instruction responsible for withholding findings. A dedicated finding stage followed by an independently configurable verification or thresholding stage produces measurable recall and precision controls without forcing one model call to optimize competing objectives.
NEW QUESTION # 17
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