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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Context Management & Reliability | 15% | - Context handling
|
| Topic 2: Prompt Engineering & Structured Output | 20% | - Prompt design
|
| Topic 3: Tool Design & MCP Integration | 18% | - Tool integration
|
| Topic 4: Claude Code Configuration & Workflows | 20% | - Claude Code
|
| Topic 5: Agentic Architecture & Orchestration | 27% | - Agentic architecture patterns
|
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NEW QUESTION # 164
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 asks your agent to identify untested code paths in a legacy payment processing module spanning 45 files. After reading the first 8 source files, the agent's responses are becoming noticeably less accurate - it's forgetting previously discussed code patterns and hasn't yet located all test files or traced critical payment flows. What's the most effective approach to complete this investigation?
Answer: B
Explanation:
Subagents isolate detailed exploration in separate context windows and return concise findings to the main agent. This prevents further context degradation while preserving high-level coordination across test discovery and payment-flow tracing.
NEW QUESTION # 165
Users report that final reports sometimes lack depth on specific subtopics. Investigation shows that the document-analysis agent frequently identifies evidence gaps--for example, noting that
"the retrieved sources discuss API authentication but lack details about token-refresh patterns." Under the current strict pipeline, this insight is not actionable because searching has already finished. What is the most effective architectural change?
Answer: C
Explanation:
Option C converts the linear pipeline into a controlled iterative research loop. The document- analysis agent is best positioned to identify precisely what the retrieved evidence fails to answer.
It should return structured gaps containing the missing question, evidence already examined, preferred source characteristics, and the coverage criterion that remains unsatisfied. The coordinator can then issue focused searches and resubmit the new material for analysis.
Anthropic's multi-agent research architecture uses a lead agent to decompose work, coordinate research agents, assess returned information, and continue investigation when further evidence is required. The guidance also stresses clear objectives, output formats, source requirements, and task boundaries to prevent gaps and duplicated work.
NEW QUESTION # 166
Your automated review calls the Claude API for each pull request, using tool_use with a report_findings tool that returns a JSON array of finding objects. Each object contains file_path, line_number, severity, category, and description. During testing on a large pull request touching more than 30 files, the response reaches the max_tokens limit and is truncated in the middle of the JSON, causing your pipeline's parser to fail. What is the most effective way to handle this?
Answer: D
Explanation:
Option A reduces the maximum output required from any single response while preserving the structured schema and complete severity range. The pipeline can partition files into coherent groups, execute bounded reviews, validate each returned array, and merge and deduplicate findings using stable fields such as file path, line number, category, and description.
Anthropic's stop-reason documentation confirms that max_tokens means generation reached the configured output limit and the response must be treated as incomplete. Structured output constraints can guarantee schema-valid generation when completion succeeds, but they cannot create unlimited output capacity. A large findings array can still exceed the available token budget.
Option B may postpone the failure but provides no durable guarantee for still-larger pull requests, and aggressively shortening descriptions may eliminate necessary evidence. Option C abandons machine- validated structure without reducing the amount of generated content. Option D deliberately suppresses medium- or low-severity findings and repeats an oversized request rather than addressing its scope.
Partitioning establishes predictable output bounds, supports targeted retries, retains every required finding category, and prevents a single truncated response from invalidating the complete review.
NEW QUESTION # 167
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.
Your agent has spent 25 minutes exploring a game engine's rendering subsystem-reading shader code, buffer management, and frame synchronization logic. An engineer now asks it to understand how the physics engine integrates with rendering for collision debug overlays. You notice recent responses reference "typical rendering patterns" rather than the specific VulkanPipeline and FrameGraph classes it discovered earlier.
What's the most effective approach?
Answer: D
Explanation:
The reference to generic rendering patterns indicates that the current context has become saturated or that important earlier details are no longer receiving sufficient attention. Anthropic documents that performance can degrade as a Claude Code context window fills with conversation history, file contents, and command output; symptoms include forgetting earlier instructions and making less precise responses. ( https://code.
claude.com/docs/en/best-practices )
Option C preserves the high-value rendering discoveries while isolating the physics investigation. The summary should identify the relevant rendering classes, synchronization boundaries, debug-overlay interfaces, and unresolved integration questions. A physics-focused subagent can then inspect collision and simulation code in its own context without adding dozens of additional files to the already crowded main conversation.
This explicit summary matters because an ordinary named subagent starts with a fresh, isolated context and does not automatically receive the parent's full conversation history or previously read files. Claude constructs a delegation message containing the task context, so the essential rendering findings must be supplied deliberately. ( https://docs.anthropic.com/en/docs/claude-code/sub-agents ) Option A omits the integration context needed to guide the physics search. Option B destroys useful prior analysis. Option D keeps expanding an already degraded context and treats the symptom rather than controlling context growth.
Official references/topics: Context-Window Management; Subagent Isolation; Delegation Summaries; Codebase Exploration.
NEW QUESTION # 168
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: D
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 # 169
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