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| Section | Weight | Objectives |
|---|---|---|
| Context Management & Reliability | 15% | - Token budget management and cost control - Context window optimization and prioritization - Context pruning and summarization strategies - Idempotency, consistency and failure resilience |
| Tool Design & MCP Integration | 18% | - Tool distribution and permission controls - Error handling and tool response formatting - Tool schema design and interface boundaries - MCP tool, resource and prompt implementation - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 |
| Claude Code Configuration & Workflows | 20% | - CLAUDE.md hierarchy, precedence and @import rules - Path-specific rules and .claude/rules/ configuration - Hooks vs advisory instructions - Custom slash commands and plan mode vs direct execution - CI/CD integration and non-interactive mode parameters |
| Prompt Engineering & Structured Output | 20% | - Validation, parsing and retry loop strategies - JSON schema design and structured output enforcement - Explicit criteria definition and few-shot prompting - System prompt design and persona alignment |
| 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 - Error recovery, guardrails and safety patterns - Agentic loop design and stop_reason handling |
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NEW QUESTION # 65
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're implementing a caching layer for API responses to speed up the /products endpoint. You have a rough idea-Redis with a 5-minute TTL-but you're new to production caching and aren't sure what other considerations a robust implementation requires. What's the most effective way to start your iterative workflow?
Answer: A
Explanation:
Because the requirements are incomplete and production caching involves hidden design choices, an interview helps surface invalidation, consistency, failure handling, and other trade-offs before implementation. Anthropic recommends this approach for larger features when important requirements may not yet be identified.
NEW QUESTION # 66
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 codebase exploration tool stores session IDs to allow engineers to continue investigations across work sessions. An engineer spent an hour yesterday analyzing a legacy authentication module, building context about its architecture and dependencies. They want to continue today. The session ID is valid, but version control shows 3 of the 12 files the agent previously read were modified overnight by a teammate's merge.
What approach best balances efficiency and accuracy?
Answer: D
Explanation:
Resuming preserves the prior architectural analysis, dependency mapping, and conversation history, avoiding the cost of repeating an hour of valid work. Anthropic states that resuming by session ID restores the agent's full prior context, including files read, analysis performed, and decisions made. ( https://code.claude.com/docs
/en/agent-sdk/sessions )
However, the session represents historical knowledge rather than a repository snapshot. Because three files changed externally, the agent must be told which files are affected and directed to re-read them before relying on earlier conclusions. Anthropic clarifies that sessions persist the conversation, not the filesystem. (
https://code.claude.com/docs/en/agent-sdk/sessions ) Targeted re-analysis updates the stale portion of the agent's model while retaining the nine unchanged files' established context.
Option A discards valuable work even though most of the analyzed code remains unchanged. Option C is accurate but inefficient because it reprocesses all twelve files regardless of whether they changed. Option D is unsafe: the agent may continue reasoning from obsolete function signatures, dependencies, or control flows.
The continuation prompt should identify the changed files, summarize the merge's purpose where known, and request a focused comparison against the earlier understanding. Any architectural conclusions affected by those changes should then be revised explicitly.
Official references/topics: Session Resume; Filesystem Versus Conversation State; Targeted Re-Analysis; Repository Change Awareness.
NEW QUESTION # 67
Your automated reviewer uses a single prompt covering security issues, API design, and business-logic correctness. Your evaluation suite shows strong recall for API-design findings at 82% but poor recall for business-logic edge cases in quiz scoring at 34%. When you add few-shot examples of logic bugs to the prompt, logic recall improves to 41%, but API-design recall drops to 68%. How should you address this trade- off to improve detection across both categories?
Answer: A
Explanation:
The evaluation results demonstrate prompt interference: improving attention to business-logic examples reduces performance on API design. Option C isolates the competing objectives so each model call can apply focused terminology, examples, evidence requirements, and evaluation criteria. The resulting structured findings can then be merged, deduplicated, and ranked before being posted.
Anthropic's Building Effective AI Agents describes parallelization through sectioning, where independent aspects of a task are handled by separate model calls and subsequently aggregated. This pattern is appropriate when one prompt must evaluate several distinct considerations and a single call does not handle all of them reliably.
Option A increases context volume without resolving competition between the review objectives; irrelevant repository content may further dilute attention. Option B may improve known quiz-scoring cases but can overfit the reviewer to an incomplete checklist and does not restore API-design recall. Option D assumes that additional model capability will eliminate prompt interference without supporting evaluation evidence.
Separate specialized review passes directly address the measured failure. Each pass should be evaluated independently, followed by an end-to-end evaluation confirming that consolidation preserves recall and does not introduce duplicated findings.
NEW QUESTION # 68
Users report that final reports sometimes lack depth on specific subtopics. Investigation shows that the document analysis agent frequently identifies gaps - for instance, noting "the retrieved sources discuss API authentication but lack details on token refresh patterns" - but under the current strict pipeline, this insight isn't actionable since search has already completed. What's the most effective architectural change?
Answer: B
Explanation:
Making the coordinator gap-aware allows it to dynamically re-delegate targeted searches based on analysis feedback. This iterative loop ensures subtopics with insufficient coverage are addressed before synthesis, improving the depth and completeness of final reports.
NEW QUESTION # 69
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.
You are building a security-scanning workflow.
When engineers need to locate every occurrence of a dangerous function such as eval() across a large codebase, which tool should the agent use for content searching?
Answer: A
Explanation:
Option B uses the tool designed to search file contents. Anthropic's Claude Code tools reference distinguishes Grep from Glob: Grep searches lines inside files, whereas Glob matches filenames and paths. Grep is built on ripgrep and accepts regular-expression patterns, so the opening parenthesis should be escaped as eval\( when the intention is to match the literal function call.
The search can return matching files, line numbers, and surrounding context without loading every file into the model's context window.
NEW QUESTION # 70
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