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| Section | Weight | Objectives |
|---|---|---|
| Prompt Engineering & Structured Output | 20% | - Prompt design strategies - Structured output generation and validation - Improving Claude response quality and consistency |
| Agentic Architecture & Orchestration | 27% | - Selecting appropriate Claude architectures - Agent coordination and orchestration patterns - Designing agentic systems and workflows |
| Tool Design & MCP Integration | 18% | - Designing effective tools for Claude applications - Model Context Protocol (MCP) concepts and integration - Tool safety, reliability, and usability |
| Context Management & Reliability | 15% | - Managing context windows and information flow - Evaluation and reliability strategies - Production deployment considerations |
| 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 # 51
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.
Your infrastructure-as-code repository includes Terraform modules ( /terraform/ ), Kubernetes manifests (
/kubernetes/ ), and CI/CD pipeline scripts ( /pipelines/ ). Each requires different conventions, but your single root CLAUDE.md has grown to 500+ lines. When developers work on Kubernetes files, Terraform-specific rules load into context unnecessarily, consuming tokens.
What is the best approach to reorganize so only relevant guidance loads when editing specific file types?
Answer: B
Explanation:
Path-scoped rules directly satisfy the requirement that instructions load only when Claude works with matching files. Each infrastructure domain can have a separate Markdown file under .claude/rules/ , with YAML paths frontmatter targeting Terraform files, Kubernetes manifests, or pipeline configuration.
Anthropic documents that .claude/rules/ keeps project guidance modular and supports conditional loading based on glob patterns. Rules with a paths field apply only when Claude works with matching files, reducing irrelevant context and token consumption. Rules without path frontmatter load unconditionally. ( https://code.
claude.com/docs/en/memory )
Option B improves readability but leaves all 500-plus lines in every session. Option C can provide directory- specific CLAUDE.md guidance, but path-scoped rules are more precise when conventions depend on extensions or patterns spanning multiple directories. Option D still requires manual imports and does not provide the documented conditional rule-loading mechanism.
A suitable design would use rules such as terraform.md scoped to terraform/**/*.tf , kubernetes.md scoped to kubernetes/**/*.{yaml,yml} , and pipelines.md scoped to the pipeline directory. Broad project-wide commands and repository etiquette can remain in the root CLAUDE.md.
Official references/topics: .claude/rules/ ; Path-Specific Rules; YAML Frontmatter; Context-Efficient Instruction Loading.
NEW QUESTION # 52
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.
After implementing tool use with strict schema definitions, JSON syntax errors are eliminated, but 5% of extractions still contain empty arrays or null values for required fields such as citations and methodology.
Spot-checking reveals that the source documents contain this information, but in varied formats-inline citations versus bibliographies, and methodology sections versus details embedded in introductions.
What is the most effective way to address these failures?
Answer: C
Explanation:
Option B targets the remaining failure mode: semantic recognition across heterogeneous document structures.
Strict schemas eliminate malformed JSON and can guarantee that tool inputs conform to declared types, but they cannot force Claude to locate evidence that appears under unfamiliar headings or in atypical sections.
Anthropic's prompting guidance says that a few relevant, diverse, structured examples are among the most reliable ways to improve accuracy and consistency. Examples should therefore show inline citations, reference lists, numbered bibliographies, methodology sections, and methods embedded in introductions, each paired with the correct extracted structure. This teaches the intended evidence-location and granularity rules rather than merely repeating the same request. Option A retries an unchanged prompt and can reproduce the same omission. Option C introduces brittle regex rules that may miss nonstandard citations and mistake keyword mentions for methodology content. Option D suppresses validation failures by weakening the contract, but it does not improve extraction and would convert recoverable omissions into incomplete records.
The examples should be drawn from real failure cases, evaluated on a held-out set, and expanded when monitoring reveals new layouts. Schema constraints and few-shot coverage solve different layers of reliability and should be used together.
NEW QUESTION # 53
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.
Your team has connected a custom MCP server that provides DevOps workflow templates. The server exposes several MCP prompts (such as deploy_checklist and incident_response) in addition to tools. How do these MCP prompts become accessible within Claude Code?
Answer: B
Explanation:
Claude Code dynamically exposes MCP server prompts as commands using the format
/mcp__servername__promptname, such as /mcp__devops__deploy_checklist. Any prompt arguments are passed afterward, separated by spaces.
NEW QUESTION # 54
Your automated reviewer uses a single prompt covering security issues, API design, and business-logic correctness. Your evaluation suite shows strong recall for API-design findings at
82% but poor recall for business-logic edge cases in quiz scoring at 34%. When you add few-shot examples of logic bugs to the prompt, logic recall improves to 41%, but API-design recall drops to
68%. How should you address this trade-off to improve detection across both categories?
Answer: B
Explanation:
The evaluation results demonstrate prompt interference: improving attention to business-logic examples reduces performance on API design. Option C isolates the competing objectives so each model call can apply focused terminology, examples, evidence requirements, and evaluation criteria. The resulting structured findings can then be merged, deduplicated, and ranked before being posted.
Anthropic's Building Effective AI Agents describes parallelization through sectioning, where independent aspects of a task are handled by separate model calls and subsequently aggregated. This pattern is appropriate when one prompt must evaluate several distinct considerations and a single call does not handle all of them reliably.
NEW QUESTION # 55
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.
Your team's CLAUDE.md includes a rule: "Use 4-space indentation and always run Prettier formatting." Despite this, code reviews reveal that roughly 30% of files Claude Code generates use inconsistent formatting - sometimes 2-space indentation, sometimes missing trailing commas. Adding emphasis ("IMPORTANT: You MUST use Prettier formatting") reduces violations to about 15%, but doesn't eliminate them. What is the most effective way to ensure all generated code is consistently formatted?
Answer: B
Explanation:
Hooks provide deterministic enforcement. Running Prettier automatically after every file edit or write guarantees consistent formatting, whereas instructions, rules, skills, or prompt-based checks still depend on model compliance.
NEW QUESTION # 56
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