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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Context Management & Reliability | 15% | - Evaluation and reliability strategies - Managing context windows and information flow - Production deployment considerations |
| Topic 2: Agentic Architecture & Orchestration | 27% | - Agent coordination and orchestration patterns - Designing agentic systems and workflows - Selecting appropriate Claude architectures |
| Topic 3: Prompt Engineering & Structured Output | 20% | - Improving Claude response quality and consistency - Structured output generation and validation - Prompt design strategies |
| Topic 4: Tool Design & MCP Integration | 18% | - Model Context Protocol (MCP) concepts and integration - Tool safety, reliability, and usability - Designing effective tools for Claude applications |
| Topic 5: Claude Code Configuration & Workflows | 20% | - Developer productivity workflows - Claude Code usage and configuration - Integrating Claude Code into development processes |
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NEW QUESTION # 62
In addition to your CI pipeline, your organization has enabled Claude's managed Code Review through the Claude GitHub App on this repository, and reviews run automatically on every pull request. Reviews average
18 findings per pull request. Developer feedback reveals three categories of unwanted noise: (1) style and formatting issues already enforced by your CI linter, (2) findings on automatically generated template code under src/gen/*, and (3) rendering-helper patterns that are intentional project conventions but are flagged because they resemble common anti-patterns. Only approximately four findings per pull request are genuine logic bugs. What is the most effective way to reduce this noise while preserving the detection of real issues?
Answer: D
Explanation:
Option A uses the dedicated configuration mechanism for Anthropic's managed Code Review service. The official Code Review documentation states that a root-level REVIEW.md is injected into every review agent as the highest-priority instruction block. It can redefine severity, suppress categories already enforced by CI, skip generated paths, and require concrete evidence before particular findings are reported.
The proposed rules map directly to the observed noise. Skipping lint and formatting findings eliminates duplication with deterministic CI checks. Excluding src/gen/* prevents comments on machine-generated code. Requiring a specific source line demonstrating incorrect behavior raises the verification threshold for rendering-helper findings without suppressing genuine defects.
Options B and C configure self-hosted GitHub Actions workflows, but the scenario concerns managed Code Review running through the Claude GitHub App on Anthropic's infrastructure. Those workflow prompts do not govern the managed reviewer. Option D supplies useful general project context, but CLAUDE.md applies to all Claude Code tasks and has lower review-specific authority. The documentation states that REVIEW.md is the stronger control for changing what managed Code Review flags and how findings are reported. It therefore provides the most targeted solution with the least unnecessary infrastructure.
NEW QUESTION # 63
You are building a customer support resolution agent using the Claude Agent SDK. The agent handles high- ambiguity requests like returns, billing disputes, and account issues. It has access to your backend systems through custom Model Context Protocol (MCP) tools ( get_customer , lookup_order , process_refund , escalate_to_human ). Your target is 80%+ first-contact resolution while knowing when to escalate.
During a billing dispute resolution, your agent successfully retrieves customer info via get_customer and order details via lookup_order , but when attempting to call process_refund , the tool returns a timeout error.
The agent has enough information to explain the charges and verify refund eligibility, but cannot actually process the refund due to the backend failure.
What approach best balances first-contact resolution with appropriate error handling?
Answer: D
Explanation:
First-contact resolution does not require pretending that every backend operation succeeded. The agent can still resolve the informational portion of the interaction by explaining the charge and confirming eligibility using the successfully retrieved customer and order data. It must then distinguish that verified conclusion from the uncompleted refund transaction.
Anthropic's tool-error guidance states that Claude should receive the failure information so it can retry, request clarification, or explain the limitation. A tool timeout must therefore be surfaced honestly rather than converted into an unsupported success claim. ( https://platform.claude.com/docs/en/agents-and-tools/tool-use
/build-a-tool-using-agent?utm_source=chatgpt.com ) Anthropic also emphasizes transparent, simple agent designs and carefully constructed tool interfaces, which support explicit disclosure of tool failure and controlled escalation. ( https://www.anthropic.com/engineering/building-effective-agents ) Option A can create an unbounded or excessively long interaction; retries should be limited and conditioned on retryability. Option B falsely represents an incomplete financial operation as completed. Option D discards the useful work already performed and escalates before providing the customer with the available explanation.
Option C preserves trust, delivers the information already established, clearly states what remains incomplete, and gives the customer a practical next step through bounded retry or human escalation.
Official references/topics: Graceful tool failure, transparent customer communication, bounded retry, human escalation.
NEW QUESTION # 64
Your automated review CI jobs take 18 seconds to initialize before Claude begins analyzing code. Profiling reveals that the delay comes from automatically discovering hooks, MCP servers, plugins, skills, and multiple nested CLAUDE.md files throughout your monorepo. You need to reduce startup time while ensuring that reviews still enforce your team's coding standards, which are documented in the root-level CLAUDE.md file.
What is the most effective approach?
Answer: D
Explanation:
Claude Code's --bare option is specifically designed for faster scripted execution. It skips automatic discovery of CLAUDE.md files, hooks, skills, plugins, MCP servers, and auto-memory while retaining essential built-in capabilities such as Bash, file reading, and file editing. Because --bare also prevents automatic loading of the root CLAUDE.md, the required standards must be added explicitly.
Option C accomplishes both objectives. --append-system-prompt-file ./CLAUDE.md loads the project standards into the current invocation while preserving Claude Code's default coding-agent system prompt and tool-use guidance. According to the official Claude Code CLI reference , append flags add file contents to the default system prompt, whereas replacement flags discard that default guidance.
Option A could work functionally but duplicates the standards in every command and creates configuration drift. Option B replaces the complete default prompt, removing useful coding, safety, and tool-use instructions. Option D improves prompt-cache reuse across different machines, but it does not disable hooks, plugins, skills, MCP servers, or CLAUDE.md discovery and therefore does not directly address the measured initialization delay.
NEW QUESTION # 65
The automated review consistently flags patterns your team uses intentionally--force-unwrapping optionals in test files, using large coordinator classes that follow your established architecture, and importing internally maintained modules marked as deprecated in the public SDK.
Developers are dismissing approximately 30% of all findings as project-specific false positives.
Which approach prevents the model from generating these findings in the first place by supplying the project's conventions as persistent context during every review?
Answer: A
Explanation:
Option D supplies the missing repository-specific knowledge before Claude evaluates the code.
The project's CLAUDE.md should state that force-unwrapping is permitted within test paths, explain the coordinator-class architecture, and identify the internally maintained deprecated module as an approved dependency. These instructions should be concise and scoped carefully so exceptions do not become global permissions.
Anthropic's CLAUDE.md documentation defines project CLAUDE.md files as persistent context for coding standards, architecture, workflows, and team conventions. They are loaded at the beginning of applicable sessions, preventing the organization from repeatedly supplying the same corrections. Anthropic also recommends recording information that Claude should know whenever it works in the repository.
NEW QUESTION # 66
A prompt asks Claude to produce Markdown tables. Which addition MOST increases consistency?
Answer: C
Explanation:
Explicit structural requirements reduce ambiguity. Providing exact column names, ordering, and formatting expectations helps Claude consistently generate Markdown tables that downstream users or systems can reliably consume.
NEW QUESTION # 67
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