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| Section | Weight | Objectives |
|---|---|---|
| Prompt Engineering & Structured Output | 20% | - Explicit criteria definition and few-shot prompting - System prompt design and persona alignment - Validation, parsing and retry loop strategies - JSON schema design and structured output enforcement |
| Context Management & Reliability | 15% | - Context window optimization and prioritization - Context pruning and summarization strategies - Idempotency, consistency and failure resilience - Token budget management and cost control |
| Tool Design & MCP Integration | 18% | - MCP tool, resource and prompt implementation - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - Error handling and tool response formatting - Tool schema design and interface boundaries - Tool distribution and permission controls |
| Agentic Architecture & Orchestration | 27% | - Session state management and workflow enforcement - Multi-agent patterns: coordinator-subagent and hub-and-spoke - Task decomposition and dynamic subagent selection - Agentic loop design and stop_reason handling - Error recovery, guardrails and safety patterns |
| Claude Code Configuration & Workflows | 20% | - Path-specific rules and .claude/rules/ configuration - Hooks vs advisory instructions - Custom slash commands and plan mode vs direct execution - CLAUDE.md hierarchy, precedence and @import rules - CI/CD integration and non-interactive mode parameters |
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NEW QUESTION # 180
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 security audit requires updating your authentication library from v2 to v3. The migration guide documents breaking changes: authenticate() now returns a Promise instead of accepting a callback, the User type has restructured fields, and three deprecated methods were removed.
Grep shows the library is imported in 45 files across several modules.
What's the most effective approach?
Answer: A
Explanation:
This migration is a high-impact, cross-module change with several independent breaking changes. Before editing, Claude must determine how the callback-based API is currently used, where the restructured User fields propagate, whether deprecated methods are wrapped or re- exported, and which downstream modules depend on the affected behavior. Plan mode is therefore the correct starting point because it allows Claude to inspect the repository, identify affected code paths, and produce an implementation strategy without modifying source files.
Anthropic recommends separating exploration and planning from implementation when the approach is uncertain, the change affects multiple files, or the developer is unfamiliar with the impacted code. Direct execution is better reserved for small, clearly scoped changes that can be described as a simple diff.
NEW QUESTION # 181
You are building developer productivity tools using the Claude Agent SDK. The agent helps engineers explore unfamiliar codebases, understand legacy systems, generate boilerplate code, and automate repetitive tasks. It uses the built-in tools (Read, Write, Bash, Grep, Glob) and integrates with Model Context Protocol (MCP) servers.
You've configured your Claude agent with three MCP servers: one for git operations, one for Jira ticket management, and one for documentation search.
When a user asks the agent to "create a branch for JIRA-123 and add documentation links to the ticket," how does the agent access tools across these servers?
Answer: D
Explanation:
MCP allows a Claude application to connect to multiple servers and expose their enabled tools within the same agentic interaction. Anthropic's MCP connector documentation explicitly supports connecting to multiple MCP servers in one request. Once connected, Claude can invoke a server's tool when the user's request corresponds to the capability described by that tool.
In this scenario, the git server can provide the branch-creation operation, the documentation server can locate the relevant links, and the Jira server can update ticket JIRA-123. The agent can coordinate these capabilities without requiring a separate conversational turn that restricts it to only one server. Tool descriptions and schemas tell Claude what each operation does and what inputs it requires.
NEW QUESTION # 182
A customer raises three separate issues during one session: a refund inquiry (turns 1-15), a subscription question (turns 16-30), and a payment method update (turns 31-45). At turn 48, the customer asks "What happened with my refund?" The conversation is approaching context limits.
What strategy best maintains the agent's ability to address all issues throughout the session?
Answer: D
Explanation:
Persisting structured issue data separately allows the agent to reference critical details (like order IDs, amounts, and statuses) without relying on the full conversation history. This preserves the ability to address past issues even as token limits constrain the active conversational context.
NEW QUESTION # 183
Compliance requires that refunds exceeding $500 must automatically escalate to a human agent
- this rule cannot be left to model discretion. Despite clear system prompt instructions, production logs show the agent occasionally processes high-value refunds directly (3% failure rate). How should you achieve guaranteed compliance?
Answer: A
Explanation:
Enforcing the policy outside of the model via a PreToolUse hook guarantees compliance. By intercepting high-value refund attempts and triggering human escalation, the system ensures the rule is applied consistently regardless of prompt instructions or agent behavior.
NEW QUESTION # 184
You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JavaScript Object Notation (JSON) schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Your system extracts event metadata (date, location, organizer, attendee_count) from news articles using a JSON schema with all nullable fields. During evaluation, you observe the model frequently generates plausible but incorrect values for fields not mentioned in the article-for example, outputting "500" for attendee_count when the source contains no attendance information.
What's the most effective way to reduce these false extractions?
Answer: A
Explanation:
The schema already supports the correct representation of missing evidence: null . The remaining defect is behavioral. Claude must be explicitly instructed that absence of information is a valid outcome and that values may be populated only when directly supported by the supplied article.
Anthropic's hallucination-reduction guidance recommends explicitly permitting Claude to express uncertainty, grounding outputs in the source, and retracting claims that lack supporting evidence. It also recommends restricting the model to the provided documents rather than allowing unsupported external knowledge. ( https://docs.anthropic.com/en/docs/test-and-evaluate/strengthen-guardrails/reduce-hallucinations ) Option C translates those controls directly into the extraction contract: when no source evidence exists, the model returns null .
Option A may improve baseline performance but does not remove the architectural ambiguity that encourages completion of missing fields. Option B is counterproductive because non-nullable required fields force the model to provide values even when the article contains none. Structured validation would confirm that the response is syntactically valid while accepting fabricated content. Option D adds cost, latency, and another probabilistic model decision; a second model can repeat or endorse the original hallucination.
A robust implementation should supplement the instruction with source spans or quotations for populated fields and automated checks where feasible. Nevertheless, among the options, explicit null behavior is the direct and most effective correction.
Official references/topics: Reduce Hallucinations; External-Knowledge Restriction; Nullable JSON Schema Fields; Grounded Extraction.
NEW QUESTION # 185
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