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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Tool Design & MCP Integration | 18% | - 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 - Error handling and tool response formatting |
| Topic 2: Context Management & Reliability | 15% | - Token budget management and cost control - Context window optimization and prioritization - Idempotency, consistency and failure resilience - Context pruning and summarization strategies |
| Topic 3: 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 |
| Topic 4: Prompt Engineering & Structured Output | 20% | - System prompt design and persona alignment - Explicit criteria definition and few-shot prompting - Validation, parsing and retry loop strategies - JSON schema design and structured output enforcement |
| Topic 5: Claude Code Configuration & Workflows | 20% | - Path-specific rules and .claude/rules/ configuration - Custom slash commands and plan mode vs direct execution - CI/CD integration and non-interactive mode parameters - CLAUDE.md hierarchy, precedence and @import rules - Hooks vs advisory instructions |
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NEW QUESTION # 165
A developer includes multiple unrelated tasks inside one extremely long prompt. What is the MOST likely outcome?
Answer: D
Explanation:
Combining many unrelated objectives in a single prompt increases ambiguity and makes it harder for Claude to identify priorities. Separating tasks into focused prompts generally produces more reliable and maintainable outputs.
NEW QUESTION # 166
You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JSON schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Your extraction system uses tool use with a JSON schema containing 12 fields and detailed descriptions, totaling approximately 2,500 tokens for the complete tool definition. Processing documents under 150,000 tokens yields 98% accuracy. For documents between 175,000 and
190,000 tokens, accuracy drops to 71%, with information from the final third consistently missed.
The model's context window is 200,000 tokens.
What is the most likely cause?
Answer: B
Explanation:
Option D identifies the dominant capacity problem. Anthropic states that everything sent in a request counts toward the context window: the system prompt, messages, documents, tool results, tool definitions, and the output Claude generates. A 175,000?90,000-token document is therefore not the complete input. Adding a 2,500-token tool schema, system instructions, wrappers, and the reserved output budget can push a nominally sub-200,000-token document close to or beyond the effective limit. Anthropic also warns that more context is not automatically better and that accuracy and recall can degrade as token count grows, a behavior described as context rot. That combination explains why short documents remain accurate while long documents lose information.
NEW QUESTION # 167
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.
Your agent needs to insert a new helper function into the middle of a 150-line utility module, between two existing functions. The Edit tool fails because its old_string parameter cannot find unique text to match-the file has repetitive docstrings, variable names, and structural patterns.
What is the most reliable way to complete this insertion?
Answer: C
Explanation:
Option D is the closest match to Claude Code's documented editing procedure. The Claude Code tools reference explains that Edit performs exact string replacement and requires old_string to appear exactly once.
When the text occurs multiple times, the prescribed response is to include sufficient surrounding context to identify one occurrence uniquely. For this insertion, the match should span a distinctive boundary between the preceding function and the following function. It does not literally need 30 lines; it needs the smallest exact block that is demonstrably unique, but option D is the only answer expressing that method.
Option A would modify every occurrence of a repeated pattern and could insert the helper function multiple times. Option B places the function at the end rather than at the required architectural location. Option C can technically work, but Write replaces the complete file and increases the change surface. Anthropic explicitly states that Write creates or overwrites full files, while partial modifications should use Edit. A targeted, uniquely anchored Edit preserves all unrelated content and produces a smaller, safer diff.
NEW QUESTION # 168
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 test generation produces unit tests for new code, but reviews show that 55% are low-value:
trivial assertions that only verify functions do not throw exceptions, tests duplicating existing coverage, or tests ignoring your team's fixture conventions.
How do you reduce the rate of low-value tests being generated in the first place?
Answer: D
Explanation:
Option D improves the generation conditions instead of filtering defective output afterward.
Anthropic recommends using CLAUDE.md for repository-wide standards and information that Claude should apply in every session. The file can define what constitutes a useful test, require assertions on observable behavior, identify approved fixture factories, prohibit duplication of existing scenarios, and provide contrasting examples of strong and trivial tests. These instructions are available before Claude decides what tests to create.
NEW QUESTION # 169
The web-search agent has gathered several relevant sources for a research topic. The document-analysis agent now needs to examine those sources. How does information typically flow between these two specialized subagents?
Answer: A
Explanation:
Option A matches the standard coordinator-managed subagent model. The coordinator invokes the web- search agent, receives its final result, selects the useful URLs or source records, and then supplies them explicitly when invoking the document-analysis agent. This keeps task ownership, provenance, and execution order visible to the coordinator.
The Claude Agent SDK subagent documentation states that a subagent begins with a fresh context window and does not inherit the parent's conversation history or previous tool results. The information passed through the Agent tool's prompt is the primary parent-to-subagent context channel. Consequently, the analysis prompt must include the source URLs, documents, retrieval notes, and any questions the analyzer must answer.
Option B requires nested delegation that is neither stated nor necessary and reduces centralized observability.
Options C and D describe valid custom architectures only if the application has deliberately implemented a queue or shared memory system; neither is automatic Agent SDK behavior. Coordinator-mediated handoff is therefore the expected approach. For reliability, the handoff should use structured source records rather than an informal prose statement, ensuring that the analyzer receives identifiers, provenance, relevant excerpts, and analysis objectives.
NEW QUESTION # 170
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