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| Section | Weight | Objectives |
|---|---|---|
| Agentic Architecture & Orchestration | 27% | - Agent coordination and orchestration patterns - Selecting appropriate Claude architectures - Designing agentic systems and workflows |
| Prompt Engineering & Structured Output | 20% | - Improving Claude response quality and consistency - Prompt design strategies - Structured output generation and validation |
| Tool Design & MCP Integration | 18% | - Tool safety, reliability, and usability - Model Context Protocol (MCP) concepts and integration - Designing effective tools for Claude applications |
| Context Management & Reliability | 15% | - Production deployment considerations - Managing context windows and information flow - Evaluation and reliability strategies |
| Claude Code Configuration & Workflows | 20% | - Developer productivity workflows - Integrating Claude Code into development processes - Claude Code usage and configuration |
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NEW QUESTION # 161
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.
In addition to your CI pipeline, your organization has enabled Claude's managed Code Review through the Claude GitHub App on this repository, and reviews run automatically on every pull request. Reviews average 18 findings per pull request. Developer feedback reveals three categories of unwanted noise: (1) style and formatting issues already enforced by your linter in CI, (2) findings on automatically generated template code under src/gen/, and (3) rendering- helper patterns that are intentional project conventions but get flagged because they resemble common anti-patterns. Only approximately four findings per pull request are genuine logic bugs.
What is the most effective way to reduce this noise while preserving the detection of genuine issues?
Answer: B
Explanation:
Option A uses the dedicated control surface for managed Claude Code Review. Anthropic's Code Review documentation states that a root-level REVIEW.md is injected into every review agent as the highest-priority instruction block. It can define skip paths, suppress categories already enforced by CI, recalibrate severity, cap nit volume, and require source evidence before reporting particular findings. The documentation explicitly identifies generated code, linting, and verification requirements as appropriate uses.
NEW QUESTION # 162
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.
You're tasked with adding real-time updates to the application. This could be implemented using WebSockets, Server-Sent Events, or polling, each with different complexity, browser support, and infrastructure requirements.
What's the most effective way to begin this task?
Answer: B
Explanation:
The implementation approach is unresolved and carries architectural consequences. WebSockets, Server-Sent Events, and polling differ in connection lifecycle, bidirectional communication, proxy compatibility, scaling requirements, deployment topology, reconnection behavior, and operational complexity. Claude should therefore inspect the existing application and infrastructure before modifying code.
Anthropic recommends separating exploration and planning from implementation when the approach is uncertain, the work affects multiple files, or the developer is unfamiliar with the relevant architecture. In plan mode, Claude can read files and analyze the system without making changes, produce a detailed proposal, and allow the developer or team to review the plan before execution. ( https://code.claude.com/docs/en/best- practices ) Option A commits to polling before determining whether its latency and load characteristics meet the requirement. Option B delegates a consequential architectural decision and immediate implementation to a single execution step without a review checkpoint. Option D prematurely selects the most infrastructure- sensitive option and assumes later refactoring will be inexpensive.
Option C creates the correct sequence: examine the frontend and backend architecture, document requirements, compare alternatives, identify infrastructure constraints, obtain approval, and only then implement the selected design with appropriate tests and observability.
Official references/topics: Plan mode, explore-plan-implement workflow, architectural trade-off analysis, review checkpoints.
NEW QUESTION # 163
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 pipeline occasionally receives responses that cannot be parsed as valid JSON, causing downstream processing failures. The current implementation prompts Claude to return JSON in the response text and then parses it.
What is the most reliable approach to ensure Claude returns valid, schema-compliant structured data?
Answer: D
Explanation:
Option C is the strongest choice among the listed approaches because it moves structured data generation from unconstrained response text into a schema-governed interface. Prompt-only JSON instructions, regular-expression extraction, and corrective retries are probabilistic recovery mechanisms: they may reduce failures, but none guarantees that the first response satisfies the contract. Anthropic now provides two schema-constrained mechanisms: JSON Structured Outputs through output_config.format and strict tool use through a tool definition with strict: true.
Strict tool use uses grammar-constrained sampling so the tool input conforms to the declared JSON Schema, including required properties and data types. Therefore, the production implementation represented by option C should define the extraction tool's input_schema and enable strict mode. The application reads the tool_use block as the extracted record; it does not need to scrape prose or search for braces.
NEW QUESTION # 164
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 contracts that frequently include amendments. When a contract contains both original terms and later amendments (e.g., original clause specifies "30- day payment terms" while Amendment 1 changes this to "45 days"), the model inconsistently extracts one value or the other with no indication of which applies.
What's the most effective approach to improve extraction accuracy for documents with amendments?
Answer: B
Explanation:
The document contains multiple factually valid values whose applicability depends on chronology and legal context. Collapsing those values into a single scalar field discards essential provenance. Option B corrects the data model by representing each term as a structured record containing the extracted value, source location, document or amendment identifier, and effective date.
Anthropic's Structured Outputs feature is designed for data-extraction use cases in which nested objects and arrays must conform to a defined JSON Schema. Anthropic also recommends grounding factual outputs in direct source material and making claims auditable through supporting evidence. A provenance-aware schema applies both principles: it retains the original clause and the amendment instead of forcing Claude to resolve a potentially complex legal precedence question during extraction.
NEW QUESTION # 165
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 system extracts event metadata (date, location, organizer, attendee_count) from news articles using a JSON schema with all nullable fields. During evaluation, you observe the model frequently generates plausible but incorrect values for fields not mentioned in the article--for example, outputting "500" for attendee_count when the source contains no attendance information.
What's the most effective way to reduce these false extractions?
Answer: B
Explanation:
The schema already supports the correct representation of missing evidence: null. The remaining defect is behavioral. Claude must be explicitly instructed that absence of information is a valid outcome and that values may be populated only when directly supported by the supplied article.
Anthropic's hallucination-reduction guidance recommends explicitly permitting Claude to express uncertainty, grounding outputs in the source, and retracting claims that lack supporting evidence.
It also recommends restricting the model to the provided documents rather than allowing unsupported external knowledge. Option C translates those controls directly into the extraction contract: when no source evidence exists, the model returns null.
NEW QUESTION # 166
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