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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Claude Code Configuration & Workflows | 20% | - Path-specific rules and .claude/rules/ configuration - Custom slash commands and plan mode vs direct execution - Hooks vs advisory instructions - CLAUDE.md hierarchy, precedence and @import rules - CI/CD integration and non-interactive mode parameters |
| Topic 2: Agentic Architecture & Orchestration | 27% | - Session state management and workflow enforcement - Agentic loop design and stop_reason handling - Task decomposition and dynamic subagent selection - Error recovery, guardrails and safety patterns - Multi-agent patterns: coordinator-subagent and hub-and-spoke |
| Topic 3: Prompt Engineering & Structured Output | 20% | - Validation, parsing and retry loop strategies - Explicit criteria definition and few-shot prompting - System prompt design and persona alignment - JSON schema design and structured output enforcement |
| Topic 4: Context Management & Reliability | 15% | - Idempotency, consistency and failure resilience - Context pruning and summarization strategies - Token budget management and cost control - Context window optimization and prioritization |
| Topic 5: Tool Design & MCP Integration | 18% | - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - Error handling and tool response formatting - MCP tool, resource and prompt implementation - Tool distribution and permission controls - Tool schema design and interface boundaries |
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NEW QUESTION # 77
During initial testing of the automated review pipeline, you notice that reviews of large pull requests containing more than 50 changed files sometimes take over 20 minutes and cost $8-$12 per run because of extensive agentic loops--Claude reads files, runs analysis tools, and iterates many times. Your team needs each invocation to abort after reaching either a fixed iteration count or a fixed dollar amount. Both limits must be enforced by Claude Code itself rather than by the surrounding job runner. Which configuration change directly enforces both per-invocation limits?
Answer: C
Explanation:
Option A is the only configuration that establishes both required limits inside Claude Code. The official CLI reference defines --max-turns as the maximum number of agentic turns permitted in print mode; Claude Code exits with an error when that limit is reached. It defines --max-budget- usd as the maximum dollar expenditure on API calls before execution stops, including applicable subagent expenditure.
These controls address different failure dimensions. The turn limit prevents an investigation from continuing through excessive read-search-analyze cycles, while the budget limit stops an invocation whose expensive turns consume the monetary allowance before reaching the turn ceiling. Supplying both therefore creates an effective per-run boundary.
NEW QUESTION # 78
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 pipeline runs:
PROMPT= ' You are a code reviewer. Analyze the provided diff for bugs, security issues, and style violations.
'
claude -p \
--dangerously-skip-permissions \
--system-prompt " $PROMPT " \
< diff.txt
The reviews complete and return feedback, but Claude only comments on the piped diff text-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 inspect related repository files while still applying your custom review instructions?
Answer: B
Explanation:
Option D preserves Claude Code's standard coding-agent instructions while adding the specialized review criteria. Anthropic documents that --system-prompt replaces the entire default system prompt, including its tool guidance, safety instructions, and coding conventions. It does not technically disable tools, but removing that guidance can make the invocation behave like a narrowly scoped text processor. --append-system-prompt retains the default behavior and layers the review instructions on top.
The prompt should explicitly direct Claude to use Read, Glob, and Grep to inspect definitions, callers, tests, and related modules whenever the diff alone is insufficient. Option A is incorrect because --system-prompt is not incompatible with tools. Option B is also inaccurate: --allowedTools pre-approves tool execution; it does not make tools available when -p would otherwise disable them. In this command, --dangerously-skip- permissions already bypasses permission prompts. Option C is false because Claude Code officially supports piped standard input in non-interactive mode. The repaired invocation should therefore use --append-system- prompt and include an explicit repository-exploration requirement. Claude Code programmatic usage , CLI system-prompt reference
NEW QUESTION # 79
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.
A customer returns 4 hours after their initial session about the same billing dispute. The previous 32-turn session contains lookup_order results showing "Status: PENDING, Expected resolution: 24-48 hours." In testing, you observe that when resuming sessions with stale tool results, the agent often references the outdated data in responses (e.g., "I see your refund is still being processed") even after subsequent fresh tool calls return different information.
What approach most reliably handles returning customers?
Answer: D
Explanation:
Option D separates durable case history from volatile operational data. The new session receives a compact, structured summary describing the billing dispute, the customer's objective, actions previously taken, and the unresolved status. It does not inherit outdated backend observations as though they were still authoritative.
Fresh tool calls then retrieve the current refund or order state before the agent responds.
Agent SDK sessions preserve conversation history, including earlier tool calls and tool results. Resuming the complete transcript therefore reintroduces stale system data into the active context, even though the external backend may have changed substantially during the four-hour gap. Conversation persistence must not be confused with persistence of external-system truth.
Option A performs unnecessary calls to every previously used tool, including tools unrelated to the returning customer's current question. Option B relies on prompt compliance while retaining contradictory historical evidence in context. Option C manually removes tool results from an existing transcript and may damage the logical relationship between prior tool_use and tool_result blocks while still retaining a long, unstructured conversation.
A structured summary should preserve stable identifiers, previous actions, customer commitments, and unresolved issues. Time-sensitive fields such as refund status, delivery state, account balance, or expected resolution should always be refreshed through authoritative tools.
Official references/topics: Session persistence, stale tool-result management, context compaction, fresh-data retrieval.
NEW QUESTION # 80
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 frequently migrates React components to Vue. You've written a step-by-step workflow for Claude Code to follow during each migration, and you want every developer on the team to invoke it by typing
/migrate-component . The workflow should stay in sync as the team iterates on it.
Where should you place the skill file?
Answer: B
Explanation:
Project skills are stored at .claude/skills/ < skill-name > /SKILL.md . The directory name becomes the invocable command name, so .claude/skills/migrate-component/SKILL.md creates /migrate-component .
Anthropic distinguishes project skills from personal skills: project skills apply only to the repository, while skills under ~/.claude/skills/ apply to one user across all projects. ( https://code.claude.com/docs/en/skills ) Committing the project skill to version control ensures that every developer receives the same workflow and that changes are reviewed, versioned, and synchronized with the codebase. Anthropic's .claude directory guidance states that project configuration should be committed when it is intended to be shared with the team, whereas files under ~/.claude are personal. ( https://code.claude.com/docs/en/claude-directory ) Option A requires manual duplication across developer machines and allows versions to diverge. Option B loads the migration procedure into every conversation even when no migration is occurring; Anthropic recommends skills for reusable checklists and multi-step procedures because their bodies load only when used. Option C describes no valid mechanism for defining a skill's content in settings.json .
Option D provides the correct scope, command name, on-demand loading behavior, and team distribution model.
Official references/topics: Project skills, SKILL.md structure, command invocation, version-controlled team configuration.
NEW QUESTION # 81
When implementing your lookup_order MCP tool, the backend sometimes returns errors (e.g.,
"Order not found" or temporary database failures). What is the correct pattern for communicating these errors back to the agent?
Answer: A
Explanation:
Returning structured tool results with isError set to true allows the agent to receive explicit error signals while preserving the error message. This enables the agent to reason about the failure, choose appropriate recovery actions, and provide meaningful feedback to the user.
NEW QUESTION # 82
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