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| Section | Weight | Objectives |
|---|---|---|
| Context Management & Reliability | 15% | - Managing context windows and information flow - Evaluation and reliability strategies - Production deployment considerations |
| Tool Design & MCP Integration | 18% | - Model Context Protocol (MCP) concepts and integration - Tool safety, reliability, and usability - Designing effective tools for Claude applications |
| Agentic Architecture & Orchestration | 27% | - Designing agentic systems and workflows - Agent coordination and orchestration patterns - Selecting appropriate Claude architectures |
| Claude Code Configuration & Workflows | 20% | - Developer productivity workflows - Integrating Claude Code into development processes - Claude Code usage and configuration |
| Prompt Engineering & Structured Output | 20% | - Improving Claude response quality and consistency - Structured output generation and validation - Prompt design strategies |
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NEW QUESTION # 166
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: A
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. Option A risks creating an oversized negative catalogue that consumes context, becomes difficult to maintain, and cannot anticipate every future variation. Option C filters text after generation and may suppress genuine findings that happen to use the selected keywords. Option D is dangerously vague: telling a reviewer to be conservative can suppress real but uncertain defects and reduce recall. The prompt should provide paired acceptable/problematic examples, explain why each classification differs, and require concrete code evidence for every reported finding. Anthropic prompting best practices
NEW QUESTION # 167
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 pipeline uses a tool called extract_metadata with a JSON schema for paper details. You've also defined lookup_citations and verify_doi tools for enrichment. During testing, you notice that when users include requests like "extract the metadata and tell me how cited it is," Claude sometimes calls lookup_citations first, which fails because it needs the DOI that extract_metadata would provide.
What's the most effective way to ensure structured metadata extraction happens first?
Answer: D
Explanation:
The dependency must be enforced by orchestration rather than left to probabilistic tool selection. Anthropic documents that tool_choice: {"type": "tool", "name": "..."} forces Claude to invoke the specified tool. By contrast, auto allows Claude to decide whether and which tool to call, while any requires some tool but does not force a particular one. ( https://platform.claude.com/docs/en/agents-and-tools/tool-use/define-tools ) Option A therefore establishes a deterministic two-stage workflow. The first API turn forces extract_metadata
, producing the DOI and other structured paper details. The application validates and stores that result. A subsequent turn then exposes or permits verify_doi and lookup_citations , passing the extracted DOI as explicit state. This design converts an implicit tool dependency into an application-controlled execution graph.
Option B is incorrect because array order is not a documented precedence mechanism and cannot guarantee selection. Option C forces extract_metadata on every call, including turns where enrichment should occur, potentially creating an infinite or non-progressing workflow. Option D guarantees only that one available tool is called; Claude could still select lookup_citations before the DOI exists.
For stronger input integrity, the tools can also use strict schemas so their arguments conform to the declared JSON Schema. The sequencing requirement, however, remains the responsibility of the orchestration layer.
Official references/topics: Tool Choice; Forced Tool Invocation; Multi-Turn Tool Orchestration; Tool Dependency Management.
NEW QUESTION # 168
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.
When analyzing complex legal cases that cite multiple precedents, the document analysis subagent processes each sequentially. A landmark case citing 12 precedents takes over 3 minutes to analyze completely. What's the most effective way to reduce this latency while preserving the coordinator's ability to monitor and debug the system?
Answer: B
Explanation:
Parallelizing independent precedent analyses reduces latency, while keeping delegation at the coordinator level preserves centralized visibility, tracing, and debugging. Recursive delegation would make monitoring more difficult.
NEW QUESTION # 169
An application frequently repeats the same background instructions in every API request. What is a potential downside?
Answer: C
Explanation:
Repeated instructions consume additional input tokens on every request, increasing cost and latency. Consolidating stable guidance into reusable prompt templates can improve efficiency while preserving consistent model behavior.
NEW QUESTION # 170
An enterprise wants Claude responses to remain professional across every interaction. Where should tone instructions primarily reside?
Answer: C
Explanation:
Persistent behavioral guidance belongs in the system prompt. Defining tone centrally ensures consistent communication across conversations regardless of varying user inputs or retrieved knowledge.
NEW QUESTION # 171
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