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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Tool Design & MCP Integration | 18% | - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - MCP tool, resource and prompt implementation - Error handling and tool response formatting - Tool schema design and interface boundaries - Tool distribution and permission controls |
| Topic 2: Prompt Engineering & Structured Output | 20% | - Explicit criteria definition and few-shot prompting - Validation, parsing and retry loop strategies - System prompt design and persona alignment - JSON schema design and structured output enforcement |
| Topic 3: Context Management & Reliability | 15% | - Token budget management and cost control - Idempotency, consistency and failure resilience - Context pruning and summarization strategies - Context window optimization and prioritization |
| Topic 4: Agentic Architecture & Orchestration | 27% | - Agentic loop design and stop_reason handling - Task decomposition and dynamic subagent selection - Multi-agent patterns: coordinator-subagent and hub-and-spoke - Session state management and workflow enforcement - Error recovery, guardrails and safety patterns |
| Topic 5: Claude Code Configuration & Workflows | 20% | - Hooks vs advisory instructions - Path-specific rules and .claude/rules/ configuration - 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 # 147
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 reviewer uses a single prompt covering security issues, API design, and business-logic correctness. Your evaluation suite shows strong recall for API-design findings at
82% but poor recall for business-logic edge cases in quiz scoring at 34%. When you add few-shot examples of logic bugs to the prompt, logic recall improves to 41%, but API-design recall drops to
68%.
How should you address this trade-off to improve detection across both categories?
Answer: B
Explanation:
Option A separates competing objectives so each review call can devote its attention and examples to a coherent concern. Anthropic's Building Effective AI Agents describes sectioning as a parallelization pattern in which independent aspects are handled by separate model calls and then aggregated. It specifically notes that models generally perform better on complex tasks when each consideration receives focused attention. The evaluation results demonstrate prompt interference: optimizing business-logic detection reduces API-design recall.
A security and API-design reviewer can use examples, terminology, and evidence criteria appropriate to interfaces and vulnerabilities. A business-logic reviewer can focus on state transitions, arithmetic boundaries, invariants, and domain-specific edge cases. Their structured findings can then be deduplicated and ranked before posting.
NEW QUESTION # 148
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.
During testing, you observe that in extended exploration sessions (30+ minutes), the agent starts giving inconsistent answers about code structure it discussed earlier. Engineers report having to repeat context about modules they've already explored. What's the most effective approach to address this?
Answer: D
Explanation:
Long exploration sessions fill the context window and degrade recall. A persistent scratchpad externalizes important architectural findings so the agent can re-read them instead of relying solely on increasingly crowded conversation context.
NEW QUESTION # 149
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: A
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 # 150
Your automated reviewer uses a single prompt covering security issues, API design, and business-logic correctness. Your evaluation suite shows strong recall for API-design findings at
82% but poor recall for business-logic edge cases in quiz scoring at 34%. When you add few-shot examples of logic bugs to the prompt, logic recall improves to 41%, but API-design recall drops to
68%. How should you address this trade-off to improve detection across both categories?
Answer: B
Explanation:
The evaluation results demonstrate prompt interference: improving attention to business-logic examples reduces performance on API design. Option C isolates the competing objectives so each model call can apply focused terminology, examples, evidence requirements, and evaluation criteria. The resulting structured findings can then be merged, deduplicated, and ranked before being posted.
Anthropic's Building Effective AI Agents describes parallelization through sectioning, where independent aspects of a task are handled by separate model calls and subsequently aggregated. This pattern is appropriate when one prompt must evaluate several distinct considerations and a single call does not handle all of them reliably.
NEW QUESTION # 151
Your MCP server implements a check_availability tool that queries an external calendar API.
During testing, you encounter three error conditions: (1) the tool is called with a malformed request missing the required user_email parameter, (2) the calendar API returns a 404 because the specified user doesn't exist in the calendar system, and (3) the calendar API returns a 503 because the service is temporarily unavailable. How should each error be reported according to MCP's error handling design?
Answer: B
Explanation:
Errors caused by malformed requests (missing required parameters) violate the RPC protocol and should be reported as JSON-RPC errors. Errors returned by the external service (user not found, temporary unavailability) are valid tool execution results and should be reported as tool outputs with isError set to true, allowing the agent to handle them gracefully.
NEW QUESTION # 152
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