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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Claude Code Configuration & Workflows | 20% | - Claude Code usage and configuration - Integrating Claude Code into development processes - Developer productivity workflows |
| Topic 2: Context Management & Reliability | 15% | - Managing context windows and information flow - Evaluation and reliability strategies - Production deployment considerations |
| Topic 3: Agentic Architecture & Orchestration | 27% | - Designing agentic systems and workflows - Selecting appropriate Claude architectures - Agent coordination and orchestration patterns |
| Topic 4: Tool Design & MCP Integration | 18% | - Tool safety, reliability, and usability - Designing effective tools for Claude applications - Model Context Protocol (MCP) concepts and integration |
| Topic 5: Prompt Engineering & Structured Output | 20% | - Prompt design strategies - Structured output generation and validation - Improving Claude response quality and consistency |
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NEW QUESTION # 85
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 three requirements for Claude Code's behavior in your project
1. Claude must never modify files in the db/migrations/ directory
2. Claude should prefer your custom logging module over console.log
3. All TypeScript files must be auto-formatted with Prettier after
every edit
All three are currently written as instructions in your project's CLAUDE.md. During a complex refactoring session, a developer discovers that Claude edited a migration file, violating requirement #1. How should you restructure these requirements across Claude Code's configuration mechanisms?
Answer: C
Explanation:
Hard restrictions belong in permission controls, coding preferences belong in CLAUDE.md, and deterministic formatting belongs in an automated hook. Instructions alone cannot reliably prevent prohibited file edits.
NEW QUESTION # 86
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 implementing a complex graph traversal algorithm with specific performance requirements and edge cases to handle (disconnected nodes, cycles, weighted edges). You want to structure your workflow for efficient iterative refinement with Claude.
What approach will most effectively enable progressive improvement across multiple iterations?
Answer: C
Explanation:
Option C creates an objective verification loop. The tests encode expected traversal behavior for disconnected graphs, cycle handling, weighted edges, invalid inputs, and performance constraints. Claude can implement the algorithm, execute the suite, inspect concrete failures, and refine the implementation until the measurable conditions pass.
Anthropic emphasizes giving Claude a verification mechanism such as tests, builds, linters, or fixture comparisons. Without an executable pass-or-fail check, Claude can only determine that an implementation appears complete. With tests, it can perform work, evaluate the result, and iterate using evidence rather than subjective judgment. Anthropic also recommends reproducing defects with failing tests before applying corrections. ( https://code.claude.com/docs/en/best-practices ) Option A may produce a thoughtful initial design but does not guarantee progressive improvement after implementation. Option B risks inheriting assumptions or deficiencies from a reference that may not match the project's constraints. Option D depends on manual review and converts the developer into the primary verification system.
The test suite should include correctness fixtures, boundary cases, complexity-sensitive workloads, and regression tests added whenever a new failure is discovered. This makes every iteration cumulative: a correction must satisfy the new case without breaking previously validated behavior.
Official references/topics: Executable Verification; Test-Driven Iteration; Feedback Loops; Regression Testing.
NEW QUESTION # 87
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 validates outputs against JSON schemas, but you need to implement human review given limited reviewer capacity (they can handle approximately 5% of total extraction volume).
What's the most effective basis for selecting which extractions to route for human review?
Answer: B
Explanation:
Limited review capacity should be concentrated on records with the highest probability of semantic error. Schema validation confirms that the response has the correct structure and data types; it does not establish that the extracted values are accurate. Ambiguous wording, contradictory passages, missing evidence, and model-reported uncertainty are direct indicators that an extraction requires human judgment.
Anthropic's reliability guidance recommends permitting Claude to express uncertainty, grounding factual outputs in source material, and validating critical information because hallucination- reduction methods do not eliminate errors completely. These principles support routing uncertain or evidentially conflicted records to reviewers rather than treating syntactically valid output as automatically trustworthy.
NEW QUESTION # 88
Production reviews reveal inconsistent handling of uncertainty in final reports. Sometimes conflicting subagent findings are synthesized into a single confident statement (losing nuance), while other times reports over-hedge with excessive qualifications (becoming unhelpful). When the web search agent returns "industry analysts estimate $50B market size (methodology varies)" and the document analysis agent returns "peer-reviewed study estimates $35B (ยฑ$7B, 95% CI)," the coordinator either picks one arbitrarily or produces vague statements like "the market may be
$35B-$50B depending on factors." What systematic approach best addresses this?
Answer: A
Explanation:
Structuring synthesis around established versus contested findings preserves uncertainty without collapsing conflicting evidence into a false single answer. It allows the report to retain source- specific context, such as analyst methodology variability and peer-reviewed confidence intervals, while still presenting a clear and useful interpretation.
NEW QUESTION # 89
Your test-generation process produces unit tests for new code, but reviews show that 55% are low-value:
trivial assertions that verify only that functions do not throw exceptions, tests that duplicate existing coverage, or tests that ignore your team's fixture conventions. How should you reduce the rate of low-value tests being generated in the first place?
Answer: D
Explanation:
The failures reflect missing project-specific knowledge: Claude does not know which fixtures are preferred, what the existing suite already covers, or what the team considers meaningful behaviour. Option C provides this information as persistent project context before test generation begins. This changes generation quality at the source instead of filtering weak tests after spending tokens to produce them.
Anthropic's CLAUDE.md documentation recommends storing shared build and test commands, coding standards, architectural decisions, conventions, and common workflows in a project CLAUDE.md. Testing guidance can define required behavioural assertions, fixture selection rules, duplication checks, naming conventions, and representative examples of acceptable and unacceptable tests.
Option A avoids difficult directories rather than improving the model's understanding and sacrifices potentially useful automation. Option B may remove some weak tests, but it doubles model work and asks another probabilistic call to infer quality criteria that should have been stated explicitly. Option D treats line coverage as a quality metric even though a trivial test can increase coverage without validating meaningful behaviour, while a valuable regression test may cover already executed lines with better assertions. Persistent, concrete testing standards therefore offer the most direct and scalable improvement.
NEW QUESTION # 90
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