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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prompt Engineering & Structured Output | 20% | - Validation, parsing and retry loop strategies - System prompt design and persona alignment - Explicit criteria definition and few-shot prompting - JSON schema design and structured output enforcement |
| Topic 2: Tool Design & MCP Integration | 18% | - Tool distribution and permission controls - Tool schema design and interface boundaries - Error handling and tool response formatting - MCP tool, resource and prompt implementation - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 |
| Topic 3: Context Management & Reliability | 15% | - Context pruning and summarization strategies - Token budget management and cost control - Context window optimization and prioritization - Idempotency, consistency and failure resilience |
| Topic 4: Claude Code Configuration & Workflows | 20% | - Hooks vs advisory instructions - CI/CD integration and non-interactive mode parameters - CLAUDE.md hierarchy, precedence and @import rules - Path-specific rules and .claude/rules/ configuration - Custom slash commands and plan mode vs direct execution |
| Topic 5: Agentic Architecture & Orchestration | 27% | - Session state management and workflow enforcement - Task decomposition and dynamic subagent selection - Agentic loop design and stop_reason handling - Multi-agent patterns: coordinator-subagent and hub-and-spoke - Error recovery, guardrails and safety patterns |
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NEW QUESTION # 173
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: D
Explanation:
The primary risk is not implementation difficulty but incomplete requirements. Production caching introduces decisions involving invalidation, stale-data tolerance, cache keys, tenant boundaries, serialization, stampede prevention, failure behavior, observability, deployment topology, and consistency expectations. Implementing Redis with a five-minute TTL before resolving these questions can produce a technically functional but operationally unsafe design.
Anthropic recommends having Claude interview the user before beginning a larger feature when important requirements remain uncertain. The AskUserQuestion workflow is intended to surface technical implementation concerns, edge cases, trade-offs, and assumptions the user may not have considered.
Anthropic further recommends converting the resulting answers into a self-contained specification with explicit scope and an end-to-end verification step. ( https://code.claude.com/docs/en/best-practices ) Option B provides useful codebase context but postpones requirement discovery. Option C creates avoidable rework by allowing architecture to emerge from production failures. Option D documents uncertainty but delegates unresolved design choices during implementation, when they may already constrain the code.
After the interview produces a caching specification, Claude can enter plan mode to inspect the endpoint and map those requirements onto the existing architecture before implementation.
Official references/topics: Requirements Interviewing; AskUserQuestion; Specification Development; Edge- Case and Trade-Off Discovery.
NEW QUESTION # 174
The synthesis agent completes its initial pass but flags that three key research questions remain unanswered because the web search and document analysis agents didn't find relevant information on those specific subtopics. The coordinator currently proceeds directly to report generation, producing reports with incomplete coverage. What change would most effectively improve research completeness?
Answer: C
Explanation:
The coordinator should treat unanswered research questions as gaps in the workflow and route them back to the appropriate specialist agents with targeted follow-up tasks. This creates an iterative research loop that improves completeness before the final report is generated.
NEW QUESTION # 175
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.
1.5An engineer asks the agent to understand how the caching layer works before adding a new cache invalidation trigger. After initial Grep searches, the agent has identified that caching logic spans 15 files including decorators, middleware, and service classes (~6,000 lines total).
What's the most effective next step for building understanding while managing context constraints?
Answer: B
Explanation:
The correct objective is to construct an architectural map before consuming the full implementation.
Identifying the base cache abstraction, its interface, and the classes that implement or invoke it gives the agent a dependency-guided path through the code. It can then inspect only the invalidation implementations and integration points relevant to the proposed trigger.
This approach protects the context window. Anthropic states that every file read occupies context and that model performance can deteriorate as the window fills. Its Claude Code guidance warns against unbounded investigation that reads large numbers of files and recommends narrowing the exploration or delegating it. (
https://code.claude.com/docs/en/best-practices )
Option A is too lexical: searching only for invalidate or expire can miss event-driven invalidation, overridden methods, cache-key mutation, and generic interface calls. Option B loads approximately 6,000 lines without first establishing relevance. Option C assumes that filename patterns and file size correlate with architectural importance; the largest files may contain incidental code while a small interface defines the entire design.
Option D follows control and type relationships rather than arbitrary file order. After reading the base class, the agent can search for subclasses, imports, construction sites, middleware hooks, and calls to the invalidation contract, progressively expanding only where evidence requires it.
Official references/topics: Context-Efficient Exploration; Dependency-Guided Reading; Architectural Interfaces; Narrowly Scoped Investigation.
NEW QUESTION # 176
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: A
Explanation:
Prompt chaining is appropriate because the workflow consists of predictable, fixed subtasks that apply to every pull request. The system can run a focused style-compliance analysis, then a security analysis, then a documentation review, and finally synthesize the three results into a consistent report.
Anthropic defines prompt chaining as decomposing a task into a sequence of steps in which each call handles a smaller component. It is recommended when a task can be cleanly divided into fixed subtasks, trading additional latency for better focus and accuracy. Intermediate checks can also be inserted between stages to confirm that each report section meets its requirements. ( https://www.anthropic.com/engineering/building- effective-agents ) Option A concentrates all considerations into one call, making it easier for one aspect to receive inadequate attention. Option B is unnecessary because orchestrator-workers is intended for complex work where the required subtasks cannot be predicted in advance. Here, the three review dimensions are already known and remain constant. Option D solves a different problem: routing is appropriate when inputs belong to distinct categories that require different downstream processes.
Although the three analyses could potentially be parallelized for lower latency, the option that accurately represents the stated repeatable decomposition is prompt chaining with final synthesis.
Official references/topics: Prompt Chaining, Fixed Subtasks, Intermediate Validation, Workflow Selection.
NEW QUESTION # 177
Your update_user_profile tool accepts a user_id (required) and an optional fields_to_update object. In testing, Claude frequently omits user_id or passes incorrectly structured data. What is most critical for helping Claude understand what parameter values to provide?
Answer: B
Explanation:
Enforcing required fields and type constraints through a JSON Schema gives the agent clear rules about what parameters must be provided and their expected structure, preventing omissions or incorrectly formatted data.
NEW QUESTION # 178
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