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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prompt Engineering & Structured Output | 20% | - Structured output generation and validation - Improving Claude response quality and consistency - Prompt design strategies |
| Topic 2: Claude Code Configuration & Workflows | 20% | - Claude Code usage and configuration - Developer productivity workflows - Integrating Claude Code into development processes |
| Topic 3: Context Management & Reliability | 15% | - Evaluation and reliability strategies - Production deployment considerations - Managing context windows and information flow |
| Topic 4: Agentic Architecture & Orchestration | 27% | - Agent coordination and orchestration patterns - Selecting appropriate Claude architectures - Designing agentic systems and workflows |
| Topic 5: Tool Design & MCP Integration | 18% | - Model Context Protocol (MCP) concepts and integration - Designing effective tools for Claude applications - Tool safety, reliability, and usability |
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NEW QUESTION # 67
Production monitoring shows that follow-up queries such as "summarize what we learned about market trends" consistently take more than 40 seconds. Investigation reveals that the coordinator spawns the synthesis subagent for every summarization request, passing more than 80,000 tokens of accumulated findings. The coordinator already has these findings in its context from orchestrating the research. What is the most effective way to improve response time for these follow-up summaries?
Answer: C
Explanation:
Option C avoids an unnecessary agent boundary. The coordinator already possesses the accumulated findings and can perform a straightforward summary without serializing, transferring, and reprocessing more than
80,000 tokens in another context window. Subagents should be reserved for work that requires isolated context, specialized instructions, separate tools, or an independent analytical process.
Anthropic's current prompting guidance advises using subagents for independent workstreams and parallel or context-isolated tasks, while handling simpler tasks directly. Anthropic also notes that excessive subagent use creates unnecessary cost and latency.
Option A generates multiple summaries speculatively, consuming resources even if they are never requested and creating cache-invalidation complexity whenever findings change. Option B may reduce repeated input- token cost, but it does not eliminate subagent startup, message processing, or the unnecessary orchestration round trip. Option D introduces an iterative request protocol that will likely increase latency further. Direct coordinator summarization uses information already available in active context and therefore provides the smallest architectural change, lowest token-transfer overhead, and fastest response while preserving subagent synthesis for genuinely complex comparative or cross-source analysis.
NEW QUESTION # 68
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.
After deploying automated code review, developers report that approximately 35% of findings are false positives following consistent patterns: style suggestions that contradict team conventions, security warnings for patterns that are safe in the deployment environment, and performance suggestions that would degrade this particular use case.
You want to reduce false positives while enabling the model to generalize its judgment to novel code patterns it has not seen before.
Which approach is most effective?
Answer: B
Explanation:
Option B demonstrates the decision boundary Claude must learn. Carefully selected examples can show structurally similar code producing different outcomes based on project context--for example, an approved authentication wrapper versus an unsafe direct call, or a deliberate performance trade- off versus an accidental quadratic operation. These contrasts help Claude apply the underlying judgment to new code rather than merely memorizing prohibited phrases.
Anthropic identifies relevant, diverse, and clearly structured examples as one of the most reliable methods for improving output accuracy and consistency. It recommends several examples that mirror the real use case and cover important edge conditions.
NEW QUESTION # 69
Anthropic's tool use documentation states: "Write instructive error messages. Instead of generic errors like 'failed', include what went wrong and what Claude should try next." A billing dispute agent uses lookup_order, which catches all exceptions and returns a tool_result with is_error:
true and the message "Tool execution failed". Monitoring shows two failure modes: the agent retries the identical call until hitting the turn limit, or it immediately calls escalate_to_human without trying alternative tools. Which change follows the documented recommendation and gives Claude the information it needs to select the correct recovery action for each error type?
Answer: C
Explanation:
Specific error messages tell Claude both what failed and the appropriate next action. A missing order should trigger an alternative lookup path, while a transient timeout may justify retrying. This directly follows Anthropic's recommendation for instructive tool errors.
NEW QUESTION # 70
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.
An engineer used Claude Code yesterday to investigate authentication flows in a legacy monolith, building up significant context over a 2-hour session. Today she wants to continue that specific investigation. She's worked on three other codebases since then and knows the session was named "auth-deep-dive".
How should she resume?
Answer: A
Explanation:
Claude Code supports resuming a specific saved session by either its identifier or its assigned name. Because the engineer knows the target session is named auth-deep-dive, the appropriate command is:
claude --resume auth-deep-dive
Anthropic's CLI reference explicitly states that --resume can resume a session by ID or name and gives named-session usage in the same form. Resuming restores the relevant conversation history and accumulated context, including the prior analysis and files discussed during the investigation.
NEW QUESTION # 71
A user expands the research system beyond its original web-search agent by adding specialized data sources.
A financial API agent returns structured JSON containing revenue, margins, and growth rates. A news- monitoring agent returns prose summaries of recent developments. A patent-analysis agent returns structured lists of technology areas. The synthesis agent combines these results into executive briefings. Currently, it converts everything into bullet points, causing financial comparisons to lose tabular clarity and news summaries to lose their narrative flow. What change would most improve briefing quality?
Answer: B
Explanation:
Option C preserves the semantic strengths of each information type instead of forcing heterogeneous evidence into one presentation structure. Financial values share comparable fields and therefore belong in a table. News developments require connected prose to preserve chronology and causality. Patent technology areas are naturally represented as grouped or prioritized lists.
Anthropic's prompting best practices recommend giving explicit output-format instructions and matching the requested format to the intended communication goal. The synthesis prompt should therefore include rendering rules for each recognized content type while maintaining common provenance fields behind the presentation.
Option A damages numerical comparability and makes trends harder to scan. Option B can be useful as an internal exchange schema, but presenting the entire briefing as uniform JSON does not produce an effective executive document. Option D normalizes transport but does not correct the synthesis agent's bullet-only rendering policy; a common representation can still be displayed badly. The correct design separates structured internal interchange from audience-facing presentation. The synthesis agent should preserve citations and metadata while selecting the most legible form for each class of evidence.
NEW QUESTION # 72
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