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| Section | Weight | Objectives |
|---|---|---|
| Tool Design & MCP Integration | 18% | - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - MCP tool, resource and prompt implementation - Tool schema design and interface boundaries - Error handling and tool response formatting - Tool distribution and permission controls |
| Prompt Engineering & Structured Output | 20% | - JSON schema design and structured output enforcement - System prompt design and persona alignment - Explicit criteria definition and few-shot prompting - Validation, parsing and retry loop strategies |
| Claude Code Configuration & Workflows | 20% | - CI/CD integration and non-interactive mode parameters - Path-specific rules and .claude/rules/ configuration - Hooks vs advisory instructions - Custom slash commands and plan mode vs direct execution - CLAUDE.md hierarchy, precedence and @import rules |
| 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 |
| Agentic Architecture & Orchestration | 27% | - Multi-agent patterns: coordinator-subagent and hub-and-spoke - Session state management and workflow enforcement - Task decomposition and dynamic subagent selection - Agentic loop design and stop_reason handling - Error recovery, guardrails and safety patterns |
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NEW QUESTION # 50
Your agent uses three tools: get_property_details(property_id) returns data including street address, get_price_history(property_id) returns historical pricing, and get_neighborhood_info(address) returns area statistics. You observe that get_neighborhood_info always requires get_property_details first just to extract the address, even when users specify the property by ID. This creates unnecessary latency and failure coupling - if the first call fails, the neighborhood request also fails. What tool design change best addresses this?
Answer: A
Explanation:
Allowing the neighborhood tool to accept the same identifier the user provides removes the need for the agent to chain through another tool just to extract an intermediate value. Resolving the address internally reduces latency, avoids unnecessary dependency on a prior tool call, and makes the workflow more reliable.
NEW QUESTION # 51
Which statement BEST describes Retrieval-Augmented Generation?
Answer: A
Explanation:
RAG retrieves relevant documents at inference time and includes them within the prompt. The model itself is unchanged, allowing organizations to incorporate current enterprise knowledge without retraining foundation models.
NEW QUESTION # 52
Your automated review CI jobs take 18 seconds to initialize before Claude begins analyzing code. Profiling reveals that the delay comes from automatically discovering hooks, MCP servers, plugins, skills, and multiple nested CLAUDE.md files throughout your monorepo. You need to reduce startup time while ensuring that reviews still enforce your team's coding standards, which are documented in the root-level CLAUDE.md file.
What is the most effective approach?
Answer: B
Explanation:
Claude Code's --bare option is specifically designed for faster scripted execution. It skips automatic discovery of CLAUDE.md files, hooks, skills, plugins, MCP servers, and auto-memory while retaining essential built-in capabilities such as Bash, file reading, and file editing. Because --bare also prevents automatic loading of the root CLAUDE.md, the required standards must be added explicitly.
Option C accomplishes both objectives. --append-system-prompt-file ./CLAUDE.md loads the project standards into the current invocation while preserving Claude Code's default coding-agent system prompt and tool-use guidance. According to the official Claude Code CLI reference , append flags add file contents to the default system prompt, whereas replacement flags discard that default guidance.
Option A could work functionally but duplicates the standards in every command and creates configuration drift. Option B replaces the complete default prompt, removing useful coding, safety, and tool-use instructions. Option D improves prompt-cache reuse across different machines, but it does not disable hooks, plugins, skills, MCP servers, or CLAUDE.md discovery and therefore does not directly address the measured initialization delay.
NEW QUESTION # 53
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 code review assistant needs to analyze pull requests and provide feedback on three aspects: code style compliance, potential security issues, and documentation completeness.
Each aspect requires reading files, running analysis tools, and generating a report section. The review process follows the same three-step workflow for every PR. Which task decomposition pattern is most appropriate for this workflow?
Answer: D
Explanation:
The review always follows the same predefined stages-style, security, and documentation-so each can be analyzed separately and then combined into a final report. Orchestrator-workers is better when subtasks must be determined dynamically.
NEW QUESTION # 54
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.
Production reviews reveal inconsistent handling of uncertainty in final reports. Sometimes conflicting subagent findings are synthesized into a single confident statement, losing important nuance, while other reports use excessive qualifications and become unhelpful. The web-search agent returns, "Industry analysts estimate a $50 billion market size, although methodologies vary." The document- analysis agent returns, "A peer-reviewed study estimates $35 billion, with a ?7 billion 95% confidence interval." The coordinator either selects one estimate arbitrarily or produces a vague $35?50 billion range.
What systematic approach best addresses this?
Answer: B
Explanation:
Option D preserves the evidence instead of manufacturing certainty. The two estimates are not directly interchangeable: one is an industry estimate with unspecified methodology, while the other is a peer-reviewed estimate with an explicit confidence interval. Converting both into model- generated confidence scores and calculating a weighted average would create a new figure that neither source reported and that may have no statistical validity. Anthropic's hallucination- reduction guidance recommends making claims auditable through quotations, citations, and supporting evidence rather than presenting unsupported synthesis as fact. Its Citations documentation similarly emphasizes retaining the exact source passages supporting individual claims. Filtering uncertain findings, option B, would remove decision-relevant information.
Requiring two-source corroboration, option C, could also discard credible evidence concerning emerging or specialized subjects. The synthesis agent should report the estimates separately, explain their methodological differences, identify which findings are strongly supported or disputed, and state what evidence would resolve the disagreement. This produces calibrated, useful reporting without arbitrary selection, excessive hedging, or false precision.
NEW QUESTION # 55
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The Anthropic world has become so competitive and challenging. To say updated and meet the challenges of the market you have to learn new in-demand skills and upgrade your knowledge. With the Anthropic CCAR-F Certification Exam everyone can do this job nicely and quickly. The Claude Certified Architect - Foundations (CCAR-F) certification exam offers a great opportunity to validate the skills and knowledge.
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