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| Section | Weight | Objectives |
|---|---|---|
| Context Management & Reliability | 15% | - Managing context windows and information flow - Evaluation and reliability strategies - Production deployment considerations |
| Prompt Engineering & Structured Output | 20% | - Prompt design strategies - Structured output generation and validation - Improving Claude response quality and consistency |
| Tool Design & MCP Integration | 18% | - Designing effective tools for Claude applications - Model Context Protocol (MCP) concepts and integration - Tool safety, reliability, and usability |
| Claude Code Configuration & Workflows | 20% | - Integrating Claude Code into development processes - Claude Code usage and configuration - Developer productivity workflows |
| Agentic Architecture & Orchestration | 27% | - Selecting appropriate Claude architectures - Agent coordination and orchestration patterns - Designing agentic systems and workflows |
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NEW QUESTION # 76
You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JavaScript Object Notation (JSON) schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
The system routes documents with extraction confidence below 85% to human review. A quarterly audit reveals that 12% of high-confidence extractions (85%) also contain errors--cases where the model finds plausible-but-incorrect values. Error sources vary: comparison tables showing competitor specs, appendices referencing different product variants, and ambiguous phrasing the model misinterprets. You need a sustainable strategy to catch these high-confidence errors and measure whether improvements reduce the error rate over time.
What approach is most effective?
Answer: D
Explanation:
Stratified random sampling provides both an ongoing quality-control mechanism and an unbiased measurement framework. By reviewing a fixed proportion of high-confidence outputs across meaningful strata--such as document type, field, source format, and business risk--the organization can estimate the residual error rate, compare performance across releases, and discover failure patterns that were not anticipated when existing rules were designed.
Anthropic recommends measurable success criteria and evaluations that mirror the real-world task distribution, including edge cases. Evaluation volume and repeatability are important because improvements must be demonstrated empirically rather than inferred from isolated examples. A weekly sampling program creates a stable benchmark and allows confidence intervals, trend analysis, regression detection, and error-taxonomy updates.
NEW QUESTION # 77
You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JavaScript Object Notation (JSON) schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
The system routes documents with extraction confidence below 85% to human review. A quarterly audit reveals that 12% of high-confidence extractions (#85%) also contain errors-cases where the model finds plausible-but-incorrect values. Error sources vary: comparison tables showing competitor specs, appendices referencing different product variants, and ambiguous phrasing the model misinterprets. You need a sustainable strategy to catch these high-confidence errors and measure whether improvements reduce the error rate over time.
What approach is most effective?
Answer: D
Explanation:
Stratified random sampling provides both an ongoing quality-control mechanism and an unbiased measurement framework. By reviewing a fixed proportion of high-confidence outputs across meaningful strata-such as document type, field, source format, and business risk-the organization can estimate the residual error rate, compare performance across releases, and discover failure patterns that were not anticipated when existing rules were designed.
Anthropic recommends measurable success criteria and evaluations that mirror the real-world task distribution, including edge cases. Evaluation volume and repeatability are important because improvements must be demonstrated empirically rather than inferred from isolated examples. ( https://docs.anthropic.com/en
/docs/build-with-claude/develop-tests ) A weekly sampling program creates a stable benchmark and allows confidence intervals, trend analysis, regression detection, and error-taxonomy updates.
Option A detects only disagreements between stochastic extractions. Two attempts may produce the same plausible but incorrect answer, so agreement is not equivalent to factual correctness. Option B addresses known patterns but will miss new failure modes and may generate excessive false positives. Option C actually lowers the review threshold in the wrong direction: documents between 70% and 85% would be treated as automated rather than reviewed if the routing rule remains "below threshold," and changing thresholds alone does not measure high-confidence error prevalence.
Official references/topics: Evaluation Design; Representative Test Distributions; Continuous Reliability Measurement; Human-in-the-Loop Sampling.
NEW QUESTION # 78
After the web search agent and document analysis agent complete their tasks, the coordinator invokes the synthesis agent. However, the synthesis agent responds that it cannot complete the task because no research findings were provided. What is the most likely cause of this issue?
Answer: D
Explanation:
The synthesis agent relies on the coordinator to provide relevant findings from prior subagents. If the coordinator fails to include these outputs in the synthesis prompt, the agent receives no actionable context and cannot produce a meaningful summary or analysis.
NEW QUESTION # 79
In production, final reports frequently contain claims without proper source attribution.
Investigation shows that while the web search and document analysis agents correctly attach citations to their outputs, the synthesis agent loses track of which sources support which conclusions when combining findings. What's the most effective architectural change?
Answer: D
Explanation:
Structured claim-to-source mappings ensure that the synthesis agent can merge findings without losing attribution. By preserving these explicit links through the workflow, the final report can include accurate citations for all claims, maintaining reliability and traceability.
NEW QUESTION # 80
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 the agent yesterday to analyze a legacy authentication module, identifying two distinct refactoring approaches: extracting a microservice versus refactoring in-place. Today, they want to explore both approaches in depth-having the agent propose specific code changes for each-before deciding which to implement.
What's the most effective way to structure this exploration?
Answer: B
Explanation:
Forking is specifically designed for exploring alternative directions from a shared body of prior analysis. Each fork starts with a copy of yesterday's conversation history, including the files read, architectural observations, dependency findings, and decisions already recorded. The microservice approach and the in-place refactoring approach can then develop independently under separate session IDs.
Anthropic's Agent SDK documentation states that a fork creates a new session from a copy of the original history while leaving the original session unchanged. Each resulting session can subsequently be resumed independently. The documented implementation combines resume with fork_session=True in Python or forkSession: true in TypeScript. ( https://code.claude.com/docs/en/agent-sdk/sessions ) Option B allows conclusions, assumptions, and proposed edits from the first approach to contaminate the evaluation of the second. Option C preserves context for only one branch and forces the engineer to reconstruct context manually for the other. Option D discards the detailed analysis already captured in the session and depends on potentially incomplete summaries.
Two forks provide equivalent starting conditions, preserve the parent investigation, and support a fair comparison of scope, migration risk, operational complexity, and required code changes. Filesystem edits should still be isolated through worktrees or checkpointing because session forking branches conversation history, not the working directory.
Official references/topics: Agent SDK Sessions; Session Forking; Alternative-Approach Exploration; Context Preservation.
NEW QUESTION # 81
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