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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Agentic Architecture & Orchestration | 27% | - Agentic architecture patterns
|
| Topic 2: Claude Code Configuration & Workflows | 20% | - Claude Code
|
| Topic 3: Prompt Engineering & Structured Output | 20% | - Prompt design
|
| Topic 4: Context Management & Reliability | 15% | - Context handling
|
| Topic 5: Tool Design & MCP Integration | 18% | - Tool integration
|
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NEW QUESTION # 132
Your pipeline reviews approximately 200 database-migration scripts daily using the Message Batches API. Each request includes a shared 8,000-token system prompt containing migration- review guidelines and schema documentation, followed by an individual migration script. You added cache_control breakpoints to the shared system prompt in every request, but monitoring shows cache-hit rates of only 32%, with misses concentrated among requests processed later in the batch window. Which change addresses the root cause without adding sequential-processing latency?
Answer: C
Explanation:
Option D directly addresses cache entries expiring before later batch requests are processed.
Anthropic's batch-processing documentation specifically notes that Message Batches can take longer than five minutes and recommends the one-hour prompt-cache duration for batches containing shared context. The prompt-caching documentation confirms that the default TTL is five minutes and that "ttl": "1h" creates an extended entry.
NEW QUESTION # 133
You built an LLM-powered code-review tool that analyzes pull requests and returns structured findings. Each finding is a JSON object containing file_path, line_number, issue_category-such as security or style-and description. Developers can dismiss findings they consider unhelpful, and currently 35% of findings are dismissed. You want to analyze these dismissals to understand what the system is getting wrong and improve the prompts accordingly. What change to the output structure would best support this analysis?
Answer: A
Explanation:
Option B creates the granular error taxonomy needed to understand recurring false-positive patterns. The existing issue_category field is too broad: a high dismissal rate for style does not reveal whether developers object to single-letter variables, line-length warnings, naming conventions, or something else. Recording the detected construct allows dismissal rates to be grouped by trigger and connected to targeted prompt revisions or project-specific examples.
Anthropic's evaluation guidance recommends measurable, task-specific criteria and evaluation cases that reflect real production behavior and edge cases. Its structured-output documentation supports schema- constrained, parseable records suitable for this type of downstream analysis.
Option A can help rank findings, but a confidence score does not explain why developers reject them and may be poorly calibrated across issue types. Option C produces more text without adding a stable dimension for aggregation. Option D removes useful category information and makes systematic analysis harder. A normalized detected_pattern field enables dashboards, pattern-level dismissal metrics, representative-example sampling, and controlled evaluations of prompt changes while retaining the broader category for higher-level reporting.
NEW QUESTION # 134
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.
In production, you observe that simple fact-checking queries, such as "In what year was the Paris Climate Agreement signed?", traverse all four subagents sequentially, consuming more than 40 seconds and significant tokens per query. Complex comparative research benefits from the complete pipeline. Your query distribution is diverse and continues to evolve as users discover new applications.
What is the most effective approach to optimize for varying query complexity?
Answer: D
Explanation:
Option D allows orchestration effort to scale with the actual request. A simple factual query may require only the web-search agent and a direct coordinator response, whereas a comparative investigation may require web research, document analysis, synthesis, and report generation.
Anthropic's Building Effective AI Agents describes the orchestrator-workers pattern as a central model dynamically identifying subtasks, delegating them, and combining the results. It is specifically appropriate when the required subtasks cannot be predicted reliably in advance.
Anthropic's multi- agent research architecture likewise emphasizes varying the number of agents and tool calls according to task complexity.
NEW QUESTION # 135
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 has connected a custom MCP server that provides DevOps workflow templates. The server exposes several MCP prompts (such as deploy_checklist and incident_response ) in addition to tools.
How do these MCP prompts become accessible within Claude Code?
Answer: B
Explanation:
MCP prompts are exposed as user-invoked commands rather than autonomous tools or permanently loaded system instructions. Claude Code dynamically discovers prompts from connected MCP servers and displays them in the command list using the naming convention /mcp__servername__promptname .
Arguments are supplied as space-separated values after the command. When executed, the MCP server resolves the prompt and its returned content is injected into the active conversation. Anthropic's official documentation provides examples such as /mcp__github__list_prs and /mcp__jira__create_issue "Bug in login flow" high . ( https://code.claude.com/docs/en/mcp ) Option A would consume context continuously and incorrectly treat optional workflow templates as mandatory system instructions. Option B confuses MCP prompts with MCP tools: tools are model-callable operations, while prompts are reusable prompt templates invoked as commands. Option C describes MCP resources, which can be referenced and attached but are a distinct MCP capability.
For the stated server, the team could invoke commands such as /mcp__devops__deploy_checklist or
/mcp__devops__incident_response service-name . The exact server segment is derived from the configured server name, with normalization applied where necessary.
Official references/topics: MCP Prompts; Dynamic Prompt Discovery; MCP Slash-Command Naming; Prompt Arguments.
NEW QUESTION # 136
Your get_portfolio_value tool returns the total value of a user's investment portfolio. You're deciding between returning a structured JSON object with explicit fields versus returning the information as a formatted text string. What is the primary advantage of using structured output with defined fields?
Answer: B
Explanation:
Structured output with defined fields allows the agent to directly access specific values without needing to interpret or parse natural language text, reducing errors in downstream processing and improving reliability for subsequent operations.
NEW QUESTION # 137
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