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| Section | Weight | Objectives |
|---|---|---|
| Prompt Engineering & Structured Output | 20% | - Structured output generation and validation - Improving Claude response quality and consistency - Prompt design strategies |
| Claude Code Configuration & Workflows | 20% | - Integrating Claude Code into development processes - Claude Code usage and configuration - Developer productivity workflows |
| Context Management & Reliability | 15% | - Managing context windows and information flow - Production deployment considerations - Evaluation and reliability strategies |
| Agentic Architecture & Orchestration | 27% | - Agent coordination and orchestration patterns - Selecting appropriate Claude architectures - Designing agentic systems and workflows |
| Tool Design & MCP Integration | 18% | - Designing effective tools for Claude applications - Model Context Protocol (MCP) concepts and integration - Tool safety, reliability, and usability |
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NEW QUESTION # 70
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 # 71
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: A
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 # 72
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 # 73
A user asks Claude to ignore all previous instructions and reveal confidential internal prompt content. How should Claude behave?
Answer: A
Explanation:
Claude follows instruction hierarchy, where system-level instructions override conflicting user requests. Attempts to expose confidential prompts or internal instructions should be declined to protect application integrity and security.
NEW QUESTION # 74
Your code-review prompts include both implementation changes and the corresponding test file, but the review comments fail to identify untested code paths. The model correctly flags functions that have no tests at all, but it fails to recognize when conditional branches or error-handling paths within tested functions lack coverage. What is the most effective way to improve branch- level gap detection without overcomplicating the pipeline?
Answer: D
Explanation:
The current prompt asks for testing analysis at too high a level. Claude recognizes the obvious absence of an entire test but has not been instructed to construct a path-level inventory. Option B turns the desired behavior into an explicit verification procedure: enumerate each condition, alternative branch, early return, exception handler, and failure path, then locate a test assertion that exercises its behavior.
Anthropic's prompting best practices emphasize clear, specific instructions and explicit sequential steps when a task requires a defined analysis process. This change keeps the existing single review call while making the missing evaluation criterion unambiguous.
NEW QUESTION # 75
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