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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prompt Engineering & Structured Output | 20% | - Explicit criteria definition and few-shot prompting - System prompt design and persona alignment - JSON schema design and structured output enforcement - Validation, parsing and retry loop strategies |
| Topic 2: Context Management & Reliability | 15% | - Idempotency, consistency and failure resilience - Context window optimization and prioritization - Context pruning and summarization strategies - Token budget management and cost control |
| Topic 3: Tool Design & MCP Integration | 18% | - Tool distribution and permission controls - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - Tool schema design and interface boundaries - MCP tool, resource and prompt implementation - Error handling and tool response formatting |
| Topic 4: 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 5: Agentic Architecture & Orchestration | 27% | - Agentic loop design and stop_reason handling - Error recovery, guardrails and safety patterns - Task decomposition and dynamic subagent selection - Multi-agent patterns: coordinator-subagent and hub-and-spoke - Session state management and workflow enforcement |
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NEW QUESTION # 51
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.
After adding an MCP server with specialized code refactoring tools (extract_function, rename_variable, inline_function), You notice the agent still uses basic text manipulation via Write and Bash sed commands for refactoring tasks. The MCP server is connected and healthy.
Examining the configuration, you find each MCP tool has a minimal description like
"extract_function: Extracts a function from code."
What's the most effective way to improve adoption of the MCP refactoring tools?
Answer: B
Explanation:
The minimal descriptions do not distinguish the specialized refactoring tools from general- purpose Write or Bash. Anthropic recommends detailed descriptions that specify what a tool does, when to use it, when not to use it, and its inputs and outputs.
NEW QUESTION # 52
A developer wants Claude to consistently answer as an experienced cybersecurity analyst regardless of the user question. Which prompt component should contain this instruction?
Answer: C
Explanation:
The system prompt establishes persistent behavioral instructions that apply throughout the conversation. Role definition, response style, and organizational policies belong there because Claude prioritizes system instructions above ordinary user requests whenever possible.
NEW QUESTION # 53
You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JavaScript Object Notation (JSON) schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Your system has been operating with 100% human review for 3 months. Analysis shows that extractions with model confidence #90% have 97% accuracy overall. To reduce reviewer workload, you plan to automate high- confidence extractions.
Before deploying, what validation step is most critical?
Answer: A
Explanation:
An aggregate accuracy value can conceal severe performance disparities. A system may achieve 97% overall accuracy while performing poorly on a low-volume document type, a critical financial field, or a specific edge case. Automating outputs solely from the aggregate figure could therefore expose downstream systems to concentrated, high-impact errors.
Anthropic's evaluation guidance states that evaluations should be task-specific, reflect the real-world task distribution, and explicitly include edge cases. It also emphasizes multidimensional success criteria rather than reliance on a single global metric. ( https://docs.anthropic.com/en/docs/build-with-claude/develop-tests ) Option A applies those principles by stratifying performance according to document type and field. This reveals whether confidence is calibrated consistently and whether the proposed automation threshold remains safe for every operationally significant segment.
Option B is useful only after segment-level performance has been understood. Selecting a global threshold cannot correct a subgroup where confidence is systematically overstated. Option C is necessary governance work, but it treats the overall 97% result as though errors were uniformly distributed. Option D places unvalidated outputs into downstream systems and depends on passive error reporting, which may fail to detect silent corruption.
The correct deployment gate is therefore segmented validation, followed by threshold selection, downstream acceptance criteria, and a controlled pilot.
Official references/topics: Define Success Criteria; Task-Specific Evaluations; Edge-Case Coverage; Reliability Segmentation.
NEW QUESTION # 54
Which statement BEST describes Retrieval-Augmented Generation?
Answer: B
NEW QUESTION # 55
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: D
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.
NEW QUESTION # 56
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