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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Tool Design & MCP Integration | 18% | - Error handling and tool response formatting - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - Tool schema design and interface boundaries - Tool distribution and permission controls - MCP tool, resource and prompt implementation |
| Topic 2: Claude Code Configuration & Workflows | 20% | - Path-specific rules and .claude/rules/ configuration - CI/CD integration and non-interactive mode parameters - Custom slash commands and plan mode vs direct execution - Hooks vs advisory instructions - CLAUDE.md hierarchy, precedence and @import rules |
| 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: Agentic Architecture & Orchestration | 27% | - Agentic loop design and stop_reason handling - 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 |
| Topic 5: Context Management & Reliability | 15% | - Context window optimization and prioritization - Context pruning and summarization strategies - Token budget management and cost control - Idempotency, consistency and failure resilience |
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NEW QUESTION # 128
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 need to add a date validation check ensuring event dates are in the future. This requires adding a conditional statement to one existing function in a single file. What is the most appropriate approach?
Answer: C
Explanation:
This is a small, well-defined modification in one function and one file. Plan mode is better suited to complex, high-impact, or multi-file changes that require review before editing.
NEW QUESTION # 129
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: D
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 # 130
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. ( https://docs.anthropic.com/en/docs/agents-and- tools/mcp-connector ) 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.
Option B incorrectly describes sequential capability discovery. Server identity and tool definitions are established through the MCP configuration rather than discovered by querying every server for each operation. Option C is incorrect because connecting several servers does not inherently restrict the agent to a single selected server. Option D imposes a one-server-per-turn limitation that MCP does not require.
Access can still be controlled through allowlists, denylists, permissions, and per-tool configuration. Those controls restrict which tools are enabled; they do not change the fundamental ability to make tools from multiple configured servers available together.
Official references/topics: MCP Server Configuration, Multi-Server Tool Access, Tool Discovery, MCP Tool Permissions.
NEW QUESTION # 131
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.
An engineer used the agent yesterday to analyze a legacy authentication module, identifying two distinct refactoring approaches: extracting a microservice versus refactoring in-place. Today, they want to explore both approaches in depth - having the agent propose specific code changes for each - before deciding which to implement. What's the most effective way to structure this exploration?
Answer: D
Explanation:
Forking preserves the accumulated authentication-module context while giving each refactoring approach an independent session, preventing one exploration from influencing the other.
Anthropic's Agent SDK supports creating a new session ID when resuming with session forking enabled.
NEW QUESTION # 132
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.
Monitoring shows 12% of extractions fail Pydantic validation with specific errors like "expected float for quantity, got '2 to 3'". Retrying these requests without modification produces identical failures.
What's the most effective approach to recover from these validation failures?
Answer: C
Explanation:
An unchanged retry repeats the same task specification and therefore commonly reproduces the same invalid interpretation. The validator has generated precise corrective information- quantity requires a float, but the model returned the range string 2 to 3 . Supplying that error in a follow-up turn converts a generic retry into an iterative repair operation.
Anthropic identifies iterative refinement as a method for detecting and correcting inconsistencies by feeding an earlier output back into a subsequent request with targeted instructions. ( https://docs.anthropic.com/en/docs
/test-and-evaluate/strengthen-guardrails/reduce-hallucinations ) Option A applies that pattern directly. Claude receives the invalid output, the exact Pydantic error, and an instruction to return a schema-compliant correction. The application should cap retries, retain the original source, and escalate cases that cannot be represented without information loss.
Option B does not guarantee correct formatting; lower temperature may make the same wrong output more repeatable. Option C can be valuable for systematic OCR or source-format problems, but it is unnecessarily broad when the immediate failure is already described by the validator. Option D increases cost and complexity without first using the actionable feedback available from the existing validation layer.
For supported models, native Structured Outputs should also be considered because they guarantee schema- conformant JSON and can prevent many Pydantic shape failures before they occur. ( https://platform.claude.
com/docs/en/build-with-claude/structured-outputs )
Official references/topics: Iterative Refinement; Validation-Error Feedback; Bounded Retry Loops; Structured Outputs.
NEW QUESTION # 133
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