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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prompt Engineering & Structured Output | 20% | - Explicit criteria definition and few-shot prompting - JSON schema design and structured output enforcement - Validation, parsing and retry loop strategies - System prompt design and persona alignment |
| 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: 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 |
| Topic 4: Agentic Architecture & Orchestration | 27% | - Agentic loop design and stop_reason handling - Error recovery, guardrails and safety patterns - Task decomposition and dynamic subagent selection - Session state management and workflow enforcement - Multi-agent patterns: coordinator-subagent and hub-and-spoke |
| Topic 5: Context Management & Reliability | 15% | - Context pruning and summarization strategies - Token budget management and cost control - Idempotency, consistency and failure resilience - Context window optimization and prioritization |
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NEW QUESTION # 56
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.
Your team is configuring MCP servers in Claude Code. You want to add a shared venue lookup server that all team members should have access to, and you personally want to add an experimental music playlist server that only you are testing.
Which configuration approach correctly applies MCP server scopes?
Answer: A
Explanation:
The venue server must be available to every project contributor, so it belongs at project scope. Claude Code stores project-scoped MCP configurations in the repository-root .mcp.json file, which can be committed to version control and shared with the team.
The experimental playlist server should remain private. Local-scoped MCP servers are stored under the current project's entry in ~/.claude.json , while user-scoped servers are also stored in ~/.claude.json but load across all projects. Because this server is described as a personal experiment, either private scope is conceptually appropriate; among the listed choices, option B correctly separates shared project configuration from private personal configuration. ( https://code.claude.com/docs/en/mcp ) Option A prevents the team from receiving the venue configuration. Option C reverses the intended visibility.
Option D exposes the experimental server configuration to other contributors and may commit machine- specific commands or credentials.
The preferred commands would specify --scope project for the shared venue server and --scope local for an experiment limited to the current repository. Project-scoped servers also require user approval before Claude Code activates configurations received through .mcp.json .
Official references/topics: MCP Installation Scopes; Project Scope; Local Scope; .mcp.json ; ~/.claude.json .
NEW QUESTION # 57
A developer uses Claude Code to refactor a function during a development session. Before committing, the developer asks the same Claude session to review the code for issues. Later, a separate automated CI review catches several bugs that the same-session review missed. What best explains this discrepancy?
Answer: B
Explanation:
The key difference is reviewer independence. In the original session, Claude retains the reasoning, assumptions, implementation decisions, and intermediate conclusions that led to the refactor. That context can anchor the subsequent review toward defending or confirming the chosen approach instead of evaluating the resulting code independently. The separate CI review begins with fresh context and can assess the implementation solely against the code and review criteria.
Anthropic's Claude Code best-practices documentation explicitly states that fresh context improves code review because Claude is not biased toward code it has just written. It recommends a writer/reviewer pattern using separate sessions or a review subagent with an isolated context.
NEW QUESTION # 58
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.
After your daily batch of 10,000 documents completes, 300 documents (3%) fail with context_length_exceeded errors. The results file identifies each failure by custom_id.
What is the most cost-effective approach to process these failures?
Answer: A
Explanation:
Option D fixes the actual failure while avoiding needless reprocessing. Anthropic's Message Batches API treats every request independently, records the result against its unique custom_id, and explicitly states that one failed request does not affect the others. The 9,700 successful documents therefore require no retry. The failed requests exceeded the available context; increasing max_tokens changes the permitted output budget, not the size of the input context, so option C does not correct the cause. Resubmitting all 10,000 requests would repeat paid work, and prompt caching would only reduce some repeated-input cost without repairing oversized requests. Chunking each failed document reduces the input presented to Claude so every replacement request fits within the model's context window . The application can then merge the extracted partial structures deterministically, retaining the original document identifier and chunk ordering. Anthropic recommends retry logic for failed batch requests and using custom_id to match results because batch results can arrive out of order. Consequently, selective retry plus chunking is both the technically correct recovery path and the lowest-cost option.
NEW QUESTION # 59
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: C
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.
Option A may remove some weak tests, but it doubles model work and still permits the first stage to generate low-quality material. Option B treats line coverage as a quality measure even though a trivial assertion can execute new lines without validating behavior. Option C abandons difficult areas rather than improving generation. The standards should also direct Claude to inspect nearby tests and fixtures before writing new cases, identify the behavior or regression each test protects, and run the relevant suite afterward. Because CLAUDE.md consumes context in every session, the guidance should remain concise; lengthy testing procedures are better placed in a reusable skill referenced from the project instructions.
NEW QUESTION # 60
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 # 61
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