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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Context Management & Reliability | 15% | - Evaluation and reliability strategies - Managing context windows and information flow - Production deployment considerations |
| Topic 2: Prompt Engineering & Structured Output | 20% | - Improving Claude response quality and consistency - Prompt design strategies - Structured output generation and validation |
| Topic 3: Agentic Architecture & Orchestration | 27% | - Agent coordination and orchestration patterns - Designing agentic systems and workflows - Selecting appropriate Claude architectures |
| Topic 4: Tool Design & MCP Integration | 18% | - Tool safety, reliability, and usability - Model Context Protocol (MCP) concepts and integration - Designing effective tools for Claude applications |
| Topic 5: Claude Code Configuration & Workflows | 20% | - Developer productivity workflows - Claude Code usage and configuration - Integrating Claude Code into development processes |
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NEW QUESTION # 104
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.
The automated review consistently flags patterns your team uses intentionally-force-unwrapping optionals in test files, using large coordinator classes that follow your established architecture, and importing internally maintained modules marked as deprecated in the public SDK. Developers dismiss approximately 30% of all findings as project-specific false positives.
Which approach prevents the model from generating these findings in the first place by supplying the project' s conventions as persistent context during every review?
Answer: B
Explanation:
Option A supplies the missing project context before Claude evaluates the code. Anthropic's CLAUDE.md documentation defines project-level files as persistent, version-controlled instructions for coding standards, architecture, workflows, and conventions. The described exceptions are stable repository facts: force- unwrapping is permitted in tests, coordinator classes follow an intentional pattern, and an internally maintained dependency remains approved despite its public deprecation status. Recording these facts concisely allows every review session to interpret the patterns correctly.
Option B removes surrounding evidence and would make architectural and cross-file judgments less reliable.
Option C hides findings after generation and can suppress genuine bugs containing the same keywords.
Option D adds noise to source files and distributes policy across individual call sites instead of documenting it centrally. The CLAUDE.md entries should be specific and scoped-for example, limiting the force- unwrapping exception to test paths rather than approving it globally. Anthropic notes that shorter, precise instructions are followed more consistently, so the file should state the accepted convention, its scope, and any conditions that would still make the pattern reportable.
NEW QUESTION # 105
You are building developer productivity tools using the Claude Agent SDK. The agent helps engineers explore unfamiliar codebases, understand legacy systems, generate boilerplate code, and automate repetitive tasks. It uses the built-in tools (Read, Write, Bash, Grep, Glob) and integrates with Model Context Protocol (MCP) servers.
1.5An engineer asks the agent to understand how the caching layer works before adding a new cache invalidation trigger. After initial Grep searches, the agent has identified that caching logic spans 15 files including decorators, middleware, and service classes (~6,000 lines total).
What's the most effective next step for building understanding while managing context constraints?
Answer: B
Explanation:
The correct objective is to construct an architectural map before consuming the full implementation.
Identifying the base cache abstraction, its interface, and the classes that implement or invoke it gives the agent a dependency-guided path through the code. It can then inspect only the invalidation implementations and integration points relevant to the proposed trigger.
This approach protects the context window. Anthropic states that every file read occupies context and that model performance can deteriorate as the window fills. Its Claude Code guidance warns against unbounded investigation that reads large numbers of files and recommends narrowing the exploration or delegating it. (
https://code.claude.com/docs/en/best-practices )
Option A is too lexical: searching only for invalidate or expire can miss event-driven invalidation, overridden methods, cache-key mutation, and generic interface calls. Option B loads approximately 6,000 lines without first establishing relevance. Option C assumes that filename patterns and file size correlate with architectural importance; the largest files may contain incidental code while a small interface defines the entire design.
Option D follows control and type relationships rather than arbitrary file order. After reading the base class, the agent can search for subclasses, imports, construction sites, middleware hooks, and calls to the invalidation contract, progressively expanding only where evidence requires it.
Official references/topics: Context-Efficient Exploration; Dependency-Guided Reading; Architectural Interfaces; Narrowly Scoped Investigation.
NEW QUESTION # 106
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: A
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 # 107
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 monorepo contains shared coding standards in /docs/standards/ : security-rules.md (for services handling user data), testing-petterns.md (for all packages), and api-conventions.md (for API-facing services). Your 15 packages are organized by feature domain ( /packages/auth/,
/packages/billing/, /packages/notifications/ , etc.) without naming conventions indicating which handle user data or expose APIs. Package maintainers are expected to configure their own local development settings, as they understand their package's domain requirements. Currently, all package CLAUDE.md files duplicate all three standards, applying irrelevant guidance. What's the most effective approach?
Answer: C
Explanation:
Package maintainers know which domain-specific standards apply. Each package can import only the required shared files, avoiding duplicated content and irrelevant context while keeping the authoritative standards centralized. Claude Code supports @path/to/import in CLAUDE.md files.
NEW QUESTION # 108
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.
Your pipeline reviews every pull request using a single API call with a static prompt containing the diff and the full text of each changed file. Unchanged files are not included. Developers report that reviews consistently miss cross-file bugs--for example, a pull request renames a function's parameters, but the review does not identify callers in unchanged files that still use the old argument order.
Evaluation shows that cross-file bugs account for 35% of production incidents originating from reviewed pull requests.
What is the most effective change to the review design?
Answer: B
Explanation:
Option C gives the reviewer access to the evidence currently missing from its prompt. An agentic review can use Grep, Glob, Read, language-server tools, and test commands to locate callers, follow imports, inspect type definitions, and verify whether a suspected compatibility issue actually exists. A turn limit controls cost while still allowing targeted exploration.
Anthropic's context-engineering guidance recommends just-in-time retrieval: agents should retain lightweight references and dynamically load the information needed for the current task instead of preloading a large fixed context. Anthropic has also reported that code-review performance improves when the necessary repositories are available for gathering complete context.
NEW QUESTION # 109
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