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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Claude Code Configuration & Workflows | 20% | - Custom slash commands and plan mode vs direct execution - CLAUDE.md hierarchy, precedence and @import rules - CI/CD integration and non-interactive mode parameters - Path-specific rules and .claude/rules/ configuration - Hooks vs advisory instructions |
| Topic 2: Tool Design & MCP Integration | 18% | - MCP tool, resource and prompt implementation - Tool distribution and permission controls - Error handling and tool response formatting - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - Tool schema design and interface boundaries |
| Topic 3: Context Management & Reliability | 15% | - Token budget management and cost control - Context pruning and summarization strategies - Context window optimization and prioritization - Idempotency, consistency and failure resilience |
| Topic 4: Prompt Engineering & Structured Output | 20% | - JSON schema design and structured output enforcement - System prompt design and persona alignment - Validation, parsing and retry loop strategies - Explicit criteria definition and few-shot prompting |
| Topic 5: Agentic Architecture & Orchestration | 27% | - Agentic loop design and stop_reason handling - Session state management and workflow enforcement - Task decomposition and dynamic subagent selection - Multi-agent patterns: coordinator-subagent and hub-and-spoke - Error recovery, guardrails and safety patterns |
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NEW QUESTION # 80
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, and Glob-and integrates with Model Context Protocol (MCP) servers.
An engineer who recently joined the team asks the agent to explain the authentication and authorization architecture before making security improvements. The codebase contains more than 800 files across multiple services.
What exploration strategy will most effectively build understanding while respecting context limits?
Answer: D
Explanation:
Option D builds an evidence-based architectural map without indiscriminately loading hundreds of files.
Authentication entry points may include route handlers, middleware registration, token-validation functions, session constructors, or identity-provider callbacks. After Grep identifies those anchors, targeted Read operations can establish their responsibilities, while imports, call sites, and configuration references reveal the downstream authorization flow. Anthropic's large-codebase guidance recommends scoping Claude to the portion of a repository touched by the task because unrelated instructions and file reads consume tokens and degrade performance. Its common workflows guidance similarly recommends beginning broadly and narrowing into specific components. Option A launches agents before the relevant service boundaries are known and may produce overlapping, inconsistent investigations. Option B is an uncontrolled lexical sweep that will include tests, documentation, unrelated tokens, and incidental terminology. Option C appropriately checks project guidance but incorrectly delegates technical file selection to a new engineer who may not know the architecture. Incremental dependency tracing maintains context efficiency while producing a verifiable end-to-end map grounded in actual code paths.
NEW QUESTION # 81
You are building a customer support resolution agent using the Claude Agent SDK. The agent handles high- ambiguity requests like returns, billing disputes, and account issues. It has access to your backend systems through custom Model Context Protocol (MCP) tools ( get_customer , lookup_order , process_refund , escalate_to_human ). Your target is 80%+ first-contact resolution while knowing when to escalate.
When the agent calls lookup_order and receives order details showing the item was purchased 45 days ago, how does the agentic loop determine whether to call process_refund or escalate_to_human next?
Answer: C
Explanation:
In the standard Claude tool-use loop, the application executes lookup_order and sends its output back as a tool_result . That result becomes part of the conversation state available to Claude. Claude then evaluates the purchase date, refund policy, customer request, authorization constraints, and available tools before selecting the next action.
Anthropic describes client-tool orchestration as a repeated loop: Claude emits a tool_use request, the application executes it, returns a tool_result , and Claude continues reasoning from the updated conversation.
Claude, rather than the tool implementation, selects when and how to invoke the next available tool unless the application has explicitly implemented a fixed workflow. ( https://platform.claude.com/docs/en/agents-and- tools/tool-use/how-tool-use-works ) Options B and C describe possible custom orchestration architectures, but neither is stated in the scenario.
Option D is inconsistent with adaptive agent behavior because later actions depend on information that did not exist before lookup_order completed. A rigid sequence would not respond appropriately to different purchase dates, eligibility states, or order conditions.
The tool result should return high-signal fields such as purchase date, return-window status, refund eligibility, existing refund status, and stable order identifiers so Claude can make the subsequent decision accurately.
Official references/topics: Tool-result continuation, adaptive agent loops, model-directed tool selection, sequential dependency handling.
NEW QUESTION # 82
Production monitoring shows that the research phase takes longer than expected. Analysis reveals that the coordinator invokes the web-search subagent, waits for its response, and then invokes the document-analysis subagent. These tasks are independent; neither requires the other's output. How should you modify the system to run these subagents concurrently?
Answer: D
Explanation:
Option A exposes both independent invocations in the same assistant turn, allowing the Agent SDK or application tool runner to execute them concurrently. The coordinator can then receive both results together and continue with synthesis only after the independent research branches have completed.
Anthropic's parallel tool-use documentation explains that a response may contain multiple tool- use blocks. Independent, read-only operations can be executed concurrently to reduce latency, after which all corresponding tool results should be returned together. The term "Agent" is used here because current Claude Agent SDK releases renamed the earlier "Task" tool.
NEW QUESTION # 83
Your pipeline runs:
PROMPT="You are a code reviewer."
PROMPT="$PROMPT Analyze the provided diff"
PROMPT="$PROMPT for bugs, security issues,"
PROMPT="$PROMPT and style violations."
claude -p \
--dangerously-skip-permissions \
--system-prompt "$PROMPT" < diff.txt
The reviews complete and return feedback, but Claude comments only on the piped diff--it never reads surrounding files in the checked-out repository to understand broader context, even when the diff modifies a function called by many other modules. Which change to the invocation will cause Claude to read related repository files while still applying your custom review instructions?
Answer: D
Explanation:
The --system-prompt flag replaces Claude Code's complete default system prompt. Although this does not technically remove the available tools, it discards the default coding-agent guidance that tells Claude how to gather repository context, navigate code, use tools, and verify findings. The replacement prompt says only to analyze the supplied diff, so the observed diff-only behaviour is consistent with the configured instructions.
Option B preserves the standard Claude Code identity and tool guidance while adding the custom review criteria. The Claude Code CLI reference states that --append-system-prompt appends instructions to the default prompt, whereas --system-prompt replaces it. Anthropic specifically recommends appending when Claude should remain a coding assistant that follows additional per-invocation rules.
NEW QUESTION # 84
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 review jobs take 18 seconds to initialize before Claude begins analyzing code.
Profiling reveals that the delay results from automatically discovering hooks, MCP servers, plugins, skills, and multiple nested CLAUDE.md files throughout the monorepo.
You need to reduce startup time while ensuring reviews still enforce the coding standards documented in the root-level CLAUDE.md file.
What is the most effective approach?
Answer: A
Explanation:
Option B removes the identified startup work while deliberately restoring the one source of project context the reviews require. Anthropic documents that --bare skips automatic discovery of hooks, skills, plugins, MCP servers, auto memory, and CLAUDE.md files. It is specifically intended for CI and scripted execution where fast, reproducible startup behavior is more important than loading every locally configured extension.
Because bare mode also skips the root CLAUDE.md, the pipeline must supply that content explicitly. - -append-system-prompt-file ./CLAUDE.md loads the standards while retaining Claude Code's default coding-agent behavior and tool guidance.
NEW QUESTION # 85
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