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| Section | Weight | Objectives |
|---|---|---|
| Claude Code Configuration & Workflows | 20% | - Claude Code
|
| Context Management & Reliability | 15% | - Context handling
|
| Tool Design & MCP Integration | 18% | - Tool integration
|
| Prompt Engineering & Structured Output | 20% | - Prompt design
|
| Agentic Architecture & Orchestration | 27% | - Agentic architecture patterns
|
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NEW QUESTION # 150
A company is building its first production Claude application. Which principle should guide the initial deployment?
Answer: A
Explanation:
Successful enterprise AI projects usually begin with a straightforward architecture that can be measured and refined over time. Iterative evaluation enables teams to improve prompts, retrieval, safety controls, and user experience using real-world feedback rather than unnecessary initial complexity.
NEW QUESTION # 151
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: B
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 # 152
You are building a multi-agent research system using the Claude Agent SDK. A coordinator agent delegates to specialized subagents: one searches the web, one analyzes documents, one synthesizes findings, and one generates reports. The system researches topics and produces comprehensive, cited reports.
The coordinator provides detailed step-by-step instructions to the web-search subagent, specifying exact search queries, source priorities, and date filters. Production monitoring reveals three issues: (1) the subagent reports "insufficient results" rather than trying alternative approaches when the pre-specified searches fail, (2) research quality drops for emerging topics that do not match expected patterns, and (3) the subagent rarely surfaces valuable tangential sources.
What is the most effective way to improve subagent adaptability?
Answer: C
Explanation:
Option A defines what successful research must achieve without hard-coding a brittle search procedure. The subagent receives measurable objectives-breadth, diversity, recency, and relevance-but retains authority to reformulate queries, follow promising leads, and adjust source selection when the topic does not match familiar patterns. Anthropic's account of its multi-agent research system states that subagents need an objective, expected output format, guidance about tools and sources, and clear task boundaries. This is more informative than option B's vague instruction, yet more adaptive than prescribing every query. Option C improves only one known failure path and still constrains the agent to a predefined procedure that may be unsuitable for emerging subjects. Option D adds orchestration complexity and a potentially incorrect classification step without solving over-prescription within either category. The coordinator should describe mandatory constraints separately from strategic guidance: authoritative-source requirements and time boundaries can remain fixed, while query formulation and exploration paths remain agent-controlled.
Evaluation should then measure source coverage, evidence quality, duplication, and discovery of relevant secondary leads rather than whether the subagent followed a particular series of searches.
NEW QUESTION # 153
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.
A security audit requires updating your authentication library from v2 to v3. The migration guide documents breaking changes: authenticate() now returns a Promise instead of accepting a callback, the User type has restructured fields, and three deprecated methods were removed.
Grep shows the library is imported in 45 files across several modules.
What's the most effective approach?
Answer: C
Explanation:
This migration is a high-impact, cross-module change with several independent breaking changes. Before editing, Claude must determine how the callback-based API is currently used, where the restructured User fields propagate, whether deprecated methods are wrapped or re- exported, and which downstream modules depend on the affected behavior. Plan mode is therefore the correct starting point because it allows Claude to inspect the repository, identify affected code paths, and produce an implementation strategy without modifying source files.
Anthropic recommends separating exploration and planning from implementation when the approach is uncertain, the change affects multiple files, or the developer is unfamiliar with the impacted code. Direct execution is better reserved for small, clearly scoped changes that can be described as a simple diff.
NEW QUESTION # 154
Your content curation agent discovers articles, analyzes each for relevance, then adds selected articles to themed collections. With separate discover_articles(topic), analyze_article(id), and add_to_collection(article_id, collection_id) tools, you observe 18+ sequential tool calls per request, causing latency issues. The agent must make editorial judgments about which articles fit a collection's theme - this requires seeing all candidates with their analysis scores simultaneously to select a cohesive set. What tool composition best addresses efficiency while preserving editorial judgment?
Answer: C
Explanation:
Combining discovery and analysis into a single tool call returns all candidate articles with their scores at once, allowing the agent to make informed editorial decisions efficiently. Keeping the collection addition separate preserves selective control while reducing sequential calls and latency.
NEW QUESTION # 155
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