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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prompt Engineering & Structured Output | 20% | - Validation, parsing and retry loop strategies - JSON schema design and structured output enforcement - System prompt design and persona alignment - Explicit criteria definition and few-shot prompting |
| Topic 2: 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 - Hooks vs advisory instructions - Path-specific rules and .claude/rules/ configuration |
| Topic 3: Context Management & Reliability | 15% | - Idempotency, consistency and failure resilience - Context pruning and summarization strategies - Context window optimization and prioritization - Token budget management and cost control |
| Topic 4: Tool Design & MCP Integration | 18% | - Error handling and tool response formatting - MCP tool, resource and prompt implementation - Tool distribution and permission controls - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - Tool schema design and interface boundaries |
| Topic 5: Agentic Architecture & Orchestration | 27% | - Task decomposition and dynamic subagent selection - Error recovery, guardrails and safety patterns - Agentic loop design and stop_reason handling - Multi-agent patterns: coordinator-subagent and hub-and-spoke - Session state management and workflow enforcement |
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NEW QUESTION # 71
Production logs show that when the agent handles complex billing disputes requiring 6+ tool calls, it sometimes exhausts its max_turns limit after gathering data but before completing resolution or escalating. The team's goal is to guarantee that every customer interaction ends with either a completed resolution or a human handoff, regardless of how the agent loop terminates. Which approach achieves this guarantee?
Answer: A
Explanation:
A system prompt cannot guarantee compliance, and turn-count hooks may escalate prematurely.
An orchestration-level fallback runs regardless of why the agent loop stopped, ensuring every interaction ends in either resolution or human escalation.
NEW QUESTION # 72
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.
You've asked Claude Code to build a PDF report generation feature. The initial implementation queries the database correctly, but the output has formatting issues: table columns are too narrow causing content truncation, dates display without proper formatting, and page break handling is incorrect. You've noticed these issues interact-changing column widths affects how dates render, and page breaks depend on content height.
What's the most effective approach for iterating toward a working solution?
Answer: B
Explanation:
The defects are coupled, so changing all three simultaneously would make it difficult to determine which modification caused an improvement or regression. Option C establishes a controlled sequence: correct the foundational column geometry, verify the resulting layout, format dates within the stabilized columns, and finally tune page breaks using the resulting content heights.
Anthropic recommends tight feedback loops and early course correction. It also advises supplying Claude with an executable or observable verification mechanism, such as a test, build result, generated fixture, or screenshot comparison. Claude can then make a focused change, inspect the output, and iterate until that specific condition is satisfied. ( https://code.claude.com/docs/en/best-practices ) Option A discards useful context from the functioning database implementation. Option B changes multiple interacting variables in one pass, making failures harder to isolate. Option D provides a useful visual target but does not replace precise technical constraints or incremental verification.
Each stage should have explicit acceptance criteria-for example, minimum column widths, expected date strings, and page-break fixtures using short and long content. Once a stage passes, its test becomes a regression guard for subsequent changes.
Official references/topics: Incremental Refinement; Tight Feedback Loops; Observable Verification; Regression Control.
NEW QUESTION # 73
An engineering team notices that Claude occasionally provides answers beyond the company's internal policy documents. They want responses to rely only on approved documentation whenever possible. Which solution is MOST appropriate?
Answer: C
Explanation:
Retrieval-Augmented Generation allows Claude to access authoritative enterprise knowledge during inference. By grounding responses in retrieved documents, the model is less likely to rely on general knowledge or generate unsupported information. This improves factual accuracy and policy compliance.
NEW QUESTION # 74
Production monitoring shows that follow-up queries such as "summarize what we learned about market trends" consistently take more than 40 seconds. Investigation reveals that the coordinator spawns the synthesis subagent for every summarization request, passing more than 80,000 tokens of accumulated findings. The coordinator already has these findings in its context from orchestrating the research. What is the most effective way to improve response time for these follow-up summaries?
Answer: B
Explanation:
Option C avoids an unnecessary agent boundary. The coordinator already possesses the accumulated findings and can perform a straightforward summary without serializing, transferring, and reprocessing more than
80,000 tokens in another context window. Subagents should be reserved for work that requires isolated context, specialized instructions, separate tools, or an independent analytical process.
Anthropic's current prompting guidance advises using subagents for independent workstreams and parallel or context-isolated tasks, while handling simpler tasks directly. Anthropic also notes that excessive subagent use creates unnecessary cost and latency.
Option A generates multiple summaries speculatively, consuming resources even if they are never requested and creating cache-invalidation complexity whenever findings change. Option B may reduce repeated input- token cost, but it does not eliminate subagent startup, message processing, or the unnecessary orchestration round trip. Option D introduces an iterative request protocol that will likely increase latency further. Direct coordinator summarization uses information already available in active context and therefore provides the smallest architectural change, lowest token-transfer overhead, and fastest response while preserving subagent synthesis for genuinely complex comparative or cross-source analysis.
NEW QUESTION # 75
Your infrastructure-as-code repository includes Terraform modules (/terraform/), Kubernetes manifests (/kubernetes/), and CI/CD pipeline scripts (/pipelines/). Each requires different conventions, but your single root CLAUDE.md has grown to 500+ lines. When developers work on Kubernetes files, Terraform-specific rules load into context unnecessarily, consuming tokens.
What is the best approach to reorganize so only relevant guidance loads when editing specific file types?
Answer: A
Explanation:
The paths field conditionally loads each rule only when Claude works with matching files, preventing unrelated Terraform, Kubernetes, or pipeline guidance from consuming context.
Subdirectory CLAUDE.md files are directory-based, while path-scoped rules provide precise file- pattern targeting.
NEW QUESTION # 76
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