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| Section | Weight | Objectives |
|---|---|---|
| Prompt Engineering & Structured Output | 20% | - Prompt design
|
| Tool Design & MCP Integration | 18% | - Tool integration
|
| Claude Code Configuration & Workflows | 20% | - Claude Code
|
| Context Management & Reliability | 15% | - Context handling
|
| Agentic Architecture & Orchestration | 27% | - Agentic architecture patterns
|
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NEW QUESTION # 38
A company is building its first production Claude application. Which principle should guide the initial deployment?
Answer: C
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 # 39
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.
Your extraction pipeline processes restaurant menus and must output structured JSON with fields for item names, descriptions, prices, and dietary tags. Some menus use inconsistent formatting-- prices as "$12" vs "12.00", dietary info as icons vs text.
What's the most reliable approach?
Answer: B
Explanation:
The most reliable architecture separates semantic interpretation from deterministic normalization.
Claude is well suited to identifying that "$12" and "12.00" represent prices, or that a leaf icon represents a dietary classification. However, canonical conversion--removing currency symbols, converting values to decimal types, mapping icons to controlled labels, and enforcing locale- specific rules--is more predictably performed in application code.
Structured Outputs guarantee that Claude returns valid JSON matching the supplied schema, but that guarantee concerns structural conformance. It does not by itself guarantee that every semantically equivalent source representation will be normalized identically. Anthropic's evaluation guidance identifies code-based checks as the fastest, most reliable, and most scalable mechanism for rule- based validation.
Option D therefore minimizes model responsibility: Claude extracts the evidence as represented, and deterministic post-processing converts it into the canonical downstream format. This also makes normalization rules independently testable, version-controlled, and auditable.
NEW QUESTION # 40
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.
You have configured the system so that all four subagents have access to the complete set of 18 tools. During testing, agents frequently call tools outside their specialization-the synthesis agent attempts web searches, and the report generator tries to analyze documents.
What is the primary cause of this poor tool-selection behavior?
Answer: B
Explanation:
Option C identifies the architectural contradiction. A specialized subagent's role prompt says what work it should perform, but granting it every tool simultaneously signals that unrelated actions are available and potentially legitimate. The synthesis agent can therefore interpret web search as a permissible way to fill gaps, while the report generator may invoke document-analysis tools even though those responsibilities belong elsewhere. Anthropic's Agent SDK documentation describes subagents as focused workers with tailored instructions and explicitly supports restricting each subagent to specific tools; it notes that tool restrictions reduce unintended actions. There is no documented universal threshold at which choosing among 18 tools becomes unreliable, so option B invents a "four or five tools" limit. Context consumption in option A may affect efficiency, but it does not directly explain the role-specific cross-calling pattern. Option D is also incorrect because the observed behavior occurs after delegation, inside agents that already possess the tools.
The remedy is least-capability configuration: give the web agent search tools, the document agent document tools, the synthesis agent only the inputs and utilities required to combine findings, and the report agent formatting or file-output tools. Aligning role, description, and tool allowlist removes the conflicting affordances.
NEW QUESTION # 41
You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JSON schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Your extraction system parses e-commerce product descriptions to extract specifications such as dimensions, weight, and materials into JSON. Despite having a well-defined schema, the model inconsistently extracts the materials field-sometimes returning "cotton blend," other times "Cotton/Polyester mix," and occasionally omitting the field when material information is clearly present in the source.
What is the most effective way to improve extraction consistency?
Answer: D
Explanation:
Option D addresses the observed inconsistency at the model-behavior level. A schema can require that materials be a string, but it cannot teach Claude which lexical form the application considers canonical or demonstrate when a source phrase should populate the field. Anthropic identifies examples as one of the most reliable ways to steer output format, structure, and consistency, recommending several relevant and diverse examples that mirror the real task in its prompting best practices . Complete input-output pairs show both recognition and normalization: for example, a description containing "60% cotton, 40% polyester" can consistently map to the chosen "cotton/polyester blend" representation. They can also include difficult cases where material information is embedded indirectly in prose. Temperature zero reduces sampling variability but does not repair an underspecified transformation rule. A more capable model likewise lacks the missing formatting convention. Making the field required is dangerous because documents may legitimately omit materials; it can force unsupported values and increase hallucinations. Few-shot examples therefore supply the missing decision boundary while preserving truthful absence handling. The examples should be evaluated on held-out descriptions and supplemented by deterministic post-processing if downstream systems require an exact controlled vocabulary.
NEW QUESTION # 42
Your automated review generates many findings per pull request, but developer feedback shows that roughly half are dismissed as "not worth addressing." Analysis reveals that dismissed findings are often technically accurate but involve minor style preferences or patterns that are acceptable in your codebase. Before adding infrastructure complexity, what prompt-design change could most effectively reduce dismissals while maintaining the detection of genuine issues?
Answer: A
Explanation:
The problem is an undefined relevance threshold. Claude is finding technically valid observations, but the prompt does not clearly distinguish actionable defects from acceptable conventions and low-value style preferences. Option A establishes explicit positive and negative reporting criteria, allowing the model to apply the team's actual definition of a useful finding during generation.
Anthropic's prompt-engineering guidance emphasizes clear, specific instructions and well-defined success criteria. The managed Code Review documentation similarly recommends defining skipped categories, generated paths, severity rules, and evidence requirements. Reporting "bugs affecting correctness or security" while excluding "formatting handled by CI and documented local conventions" is substantially more precise than asking the model to be generally conservative.
Option B adds cost, latency, another probabilistic decision, and a new evaluation surface before improving the original prompt. Option C confuses confidence with importance: Claude may be highly confident about a trivial style observation. Option D is dangerously broad because it can suppress legitimate but uncertain bugs, reducing recall. Explicit relevance criteria address the demonstrated failure directly while preserving the model's ability to investigate and report genuine correctness or security problems.
NEW QUESTION # 43
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