Claude Code Interview Questions for 2026 [ANSWERED]

By 2026, an estimated 70% of developers will use AI coding assistants daily—but there's a fundamental difference between autocomplete tools and agentic systems. While GitHub Copilot suggests code snippets, Claude Code autonomously executes multi-step tasks: searching codebases, modifying files, running tests, and creating commits. Understanding this paradigm shift is becoming essential interview knowledge for senior developers.

Understanding Agentic Coding: The Foundation

Before diving into specific questions, let's establish what makes Claude Code different from the AI tools you're probably already using. This understanding frames everything else.

What Is Claude Code and How Does It Differ from GitHub Copilot?

When interviewers ask this, they're testing whether you understand the fundamental paradigm shift from reactive to agentic AI assistance.

The 30-second answer: Claude Code is an agentic coding assistant that autonomously executes multi-step tasks through terminal commands and file operations. Unlike GitHub Copilot which provides inline autocomplete suggestions, Claude Code can independently read files, search codebases, run tests, commit changes, and manage entire workflows without requiring constant user intervention.

The 2-minute answer (when they want more depth): The distinction runs deeper than just features—it's a different interaction model entirely. GitHub Copilot, Tabnine, and Amazon CodeWhisperer operate as "smart autocomplete." They predict what you're about to type and suggest code snippets based on context. You remain the primary actor, writing code line-by-line with AI assistance.

Claude Code, by contrast, is an agentic system. You describe what you want to accomplish—"add rate limiting to all API endpoints" or "debug why the integration tests are failing"—and it independently plans and executes the necessary steps. It can grep through your entire codebase using pattern matching, read multiple files simultaneously, modify code across different files, execute terminal commands, run tests, and even create pull requests.

Let me show you the difference with a concrete example:

# GitHub Copilot Workflow
1. You: Open authentication.js
2. Copilot: Suggests function completion
3. You: Accept suggestion, type next line
4. Copilot: Suggests next line
5. You: Manually save, run tests, commit
6. (Repeat for each file)
 
# Claude Code Workflow
1. You: "Add JWT authentication to replace session-based auth"
2. Claude: Searches codebase for auth files
3. Claude: Reads relevant files, analyzes architecture
4. Claude: Modifies authentication.js, middleware.js, config.js
5. Claude: Runs tests, reports results
6. Claude: Creates commit with descriptive message
7. (All automated in one task)

This architectural difference means Claude Code excels at higher-level tasks—refactoring, debugging complex issues, implementing features across multiple files—while traditional extensions excel at reducing typing overhead during active code composition. They serve complementary purposes, and interviewers want to see that you understand when to use each.

What interviewers are looking for: Recognition that "agentic" means autonomous task execution, not just smarter suggestions. Ability to articulate the trade-offs between the two paradigms.

The Role of CLAUDE.md: Your Project's AI Memory

This question reveals whether you've actually configured Claude Code for real projects or just read about it.

The 30-second answer: CLAUDE.md is a project instructions file placed at your repository root that provides Claude Code with essential context about your project—build commands, architecture decisions, development workflows, and coding conventions. It persists knowledge across sessions, so you don't need to re-explain your setup every time.

The deeper explanation: Think of CLAUDE.md as onboarding documentation specifically written for an AI team member who will work autonomously on your codebase. Unlike README files that focus on getting humans started, CLAUDE.md is optimized for AI consumption.

The critical insight: Claude Code's system message explicitly states that instructions in CLAUDE.md "OVERRIDE any default behavior and you MUST follow them exactly as written." This gives your project-specific instructions priority over Claude's general behaviors. If your project uses unconventional patterns, CLAUDE.md is how you teach Claude about them.

Here's a structure that's worked well across projects I've consulted on:

## Project Overview
Multi-tenant SaaS platform using Clean Architecture.
Backend: Node.js/Express with TypeScript. Database: PostgreSQL with Prisma.
Frontend: React with Vite.
 
## Development Commands
 
### Local Development
npm run dev  # Starts both frontend and backend
 
### Testing
npm test                    # Run all tests
npm test -- auth.test.ts    # Specific test file
 
### Build
npm run build  # Production build with type checking
 
## Architecture Decisions
- Use named exports, never default exports
- All API endpoints follow REST conventions
- Business logic lives in /src/domain, never in controllers
- Tests go in __tests__ directories adjacent to source files
 
## Code Conventions
- Prefer composition over inheritance
- Use early returns to reduce nesting
- No console.log in production code—use the logger

What interviewers are looking for: Understanding that CLAUDE.md is for AI, not humans. Knowledge of what belongs there versus in README or other documentation.

Common follow-up: "How would you handle secrets or environment-specific configuration?" Strong candidates know that CLAUDE.md is checked into version control, so sensitive information goes in environment variables or .env files (which CLAUDE.md can reference by name without exposing values).

MCP: Extending Claude's Capabilities

The Model Context Protocol is where Claude Code gets genuinely powerful—and where interview questions start separating developers who've just used Claude from those who've extended it.

What Is MCP and What Transport Types Does Claude Code Support?

The 30-second answer: MCP (Model Context Protocol) is an open protocol that enables Claude to securely connect to external data sources and tools. Claude Code supports three transport types: stdio for local processes (most common), HTTP with Server-Sent Events for remote servers, and WebSocket for real-time bidirectional communication.

Here's where it gets interesting: MCP servers act as bridges between Claude and external resources. When you configure an MCP server, you're essentially giving Claude permission to interact with specific tools, databases, or services through a standardized interface.

The three transport types serve different use cases. Stdio (standard input/output) is for local processes running on your machine. It's the most common transport type—communication happens via stdin/stdout streams. Think of local file system servers or database clients that run alongside your terminal.

HTTP/SSE (HTTP with Server-Sent Events) is for remote servers accessible via HTTP. The server-sent events enable server-to-client streaming, which is essential for real-time updates from cloud-based data sources or remote APIs.

WebSocket maintains persistent bidirectional connections for real-time communication. Use this for streaming services or collaboration tools where both client and server need to send messages at any time.

A practical stdio configuration looks like this:

{
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-filesystem", "/path/to/allowed/directory"],
      "transport": "stdio"
    }
  }
}

What interviewers are looking for: Understanding that MCP is about extending Claude's reach beyond its built-in capabilities. Ability to choose the right transport type for different scenarios.

Configuring Authenticated MCP Servers

This question tests security awareness alongside technical knowledge.

The 30-second answer: Configure authenticated MCP servers using environment variable substitution with ${VARIABLE_NAME} syntax. Never hardcode credentials in configuration files. Use the env object for passing environment variables to local processes, and the headers object for HTTP authentication headers.

The secure approach in practice: Authentication configuration is where many developers make security mistakes. The pattern I recommend—and what I look for when reviewing configurations—is strict separation between credentials and configuration.

For a GitHub MCP server that needs a personal access token:

{
  "mcpServers": {
    "github": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-github"],
      "transport": "stdio",
      "env": {
        "GITHUB_TOKEN": "${GITHUB_PERSONAL_ACCESS_TOKEN}"
      }
    }
  }
}

For HTTP-based servers requiring Bearer token authentication:

{
  "mcpServers": {
    "api-server": {
      "url": "https://api.example.com/mcp",
      "transport": "http",
      "headers": {
        "Authorization": "Bearer ${API_TOKEN}",
        "X-API-Key": "${API_KEY}"
      }
    }
  }
}

The ${VARIABLE_NAME} syntax tells Claude Code to read the value from your environment variables at runtime, keeping the actual tokens out of files that might be committed or shared.

What interviewers are looking for: Security-first thinking. Knowledge of the difference between env (for stdio processes) and headers (for HTTP servers). Understanding of why environment variable substitution matters.

Slash Commands and Workflow Automation

Custom slash commands are where Claude Code becomes truly personalized to your workflow. Interview questions here test your ability to create reusable, maintainable automation.

Creating Custom Slash Commands

The 30-second answer: Custom slash commands are Markdown files stored in .claude/commands/ (project-level) or ~/.claude/commands/ (personal). The filename becomes the command name—commit.md becomes /commit. Files can include YAML frontmatter for metadata like description, allowed-tools, and argument-hint, plus the prompt template that gets expanded when invoked.

Building a practical command: Let me walk through creating a command that's saved me hours: a safe commit workflow that stages changes, generates a conventional commit message, and handles pre-commit hook failures.

---
description: Stage changes and create a commit with conventional format
allowed-tools: Bash(git add:*), Bash(git status:*), Bash(git commit:*), Bash(git diff:*)
argument-hint: [type] [message]
---
 
Current repository status:
!`git status --short`
 
Recent commits for style reference:
!`git log -5 --oneline`
 
Staged changes:
!`git diff --staged`
 
Create a commit following these rules:
1. Type must be one of: feat, fix, docs, style, refactor, test, chore
2. Message should be imperative ("Add feature" not "Added feature")
3. Keep the subject line under 72 characters
4. Include the Co-Authored-By line for Claude
 
Commit type: $1
Message guidance: $2

Notice the frontmatter restricts which tools the command can use—only specific git operations, nothing else. The !`command` syntax executes bash commands and injects their output into the prompt, giving Claude context about the current repository state.

The difference between @file and !command: These two syntaxes serve different purposes. @file includes the contents of a file directly in the prompt—@src/config.js would inject the JavaScript source code. !`command` executes a shell command and includes its output—!`git status` would run git status and inject the result.

Use @file for static context (configuration files, source code you want reviewed). Use !`command` for dynamic context (current git state, test output, system information).

What interviewers are looking for: Understanding of the frontmatter fields and their purposes. Ability to scope permissions appropriately with allowed-tools. Knowledge of when to use @file versus !command.

Agents and Subagents: Delegating Complex Work

This is where Claude Code's agentic nature becomes most apparent. Understanding the agent system is essential for anyone claiming to work productively with the tool.

Commands vs. Agents: When to Use Each

The 30-second answer: Commands are user-initiated shortcuts that inject prompts into your main conversation thread. Agents (subagents) are autonomous, specialized AI instances that operate in isolated context windows to handle complex multi-step tasks independently. Commands keep you in the driver's seat; agents work as delegated specialists.

The mental model that clicks: Think of commands as macros or keyboard shortcuts—they expand into actions in your current context. Agents are more like specialized team members you assign work to. When you delegate to an agent, it works in parallel without cluttering your main conversation with intermediate steps.

The isolation is key. Each subagent has its own context window, which means all the files it reads and searches it performs don't consume space in your main conversation. For complex tasks like "research all the authentication libraries we're using and write a security audit," an agent can consume 100K tokens of context exploring the codebase while your main session stays focused on your current work.

What interviewers are looking for: Clear understanding of context isolation and why it matters. Ability to decide when a task warrants agent delegation versus direct command execution.

The Explore Subagent

The 30-second answer: The Explore subagent is a built-in, Haiku 4.5-powered agent specialized for fast, read-only codebase exploration. It automatically triggers when Claude needs to search or understand code without making changes, keeping exploration results out of your main context.

When you'll see it activate: If you ask "how does our authentication middleware work?" or "find all the places we use the deprecated API," Claude delegates to the Explore subagent rather than handling it directly. The Explore agent has access to read-only tools: ls, git status, git log, find, cat, head, tail. It cannot modify files, run builds, or execute tests.

This design serves two purposes. First, it keeps your main conversation clean—the Explore agent might read 50 files to answer your question, but only the summary comes back to your main thread. Second, it's faster because Haiku is optimized for quick turnaround on search-and-discovery tasks.

What interviewers are looking for: Knowledge of when Explore triggers automatically. Understanding that it's read-only by design and why that matters.

Background Agents for Parallel Work

The 30-second answer: Background agents run asynchronously in separate context windows, allowing you to continue working while long-running operations complete. Use them for research, code reviews, test runs, or any task that would otherwise block your workflow.

A pattern that's changed how I work: Imagine you're implementing a feature that requires understanding a third-party API's authentication flow. Without background agents, you'd either research first (blocking implementation) or implement first (possibly getting it wrong). With background agents:

1. Spawn a research agent to explore the API documentation
2. Continue writing your implementation with best-guess assumptions
3. When the research agent completes, it notifies your main session
4. Adjust your implementation based on findings

The keyboard shortcut Ctrl+B runs any suggested command in the background. For programmatic execution, use run_in_background: true. Check running tasks with /tasks.

What interviewers are looking for: Understanding of non-blocking workflows. Practical scenarios where background execution improves productivity.

Hooks and Lifecycle Events

Hooks are Claude Code's extension points—they let you inject custom behavior at specific moments during operation. Security-conscious organizations care deeply about this system.

The Hook Lifecycle

The 30-second answer: Claude Code provides lifecycle events including SessionStart (when a session begins), Stop (when it ends), SubagentStop (when a subagent completes), and PreToolUse/PostToolUse (before and after tool execution). Hooks execute shell commands in response to these events.

Implementing a secret-prevention hook: Here's a practical example that blocks commits containing potential secrets—exactly the kind of safety mechanism enterprises want to see:

{
  "hooks": {
    "PreToolUse": [
      {
        "matcher": "Write|Edit",
        "hooks": [
          {
            "type": "command",
            "command": ".claude/hooks/prevent-secrets.sh"
          }
        ]
      }
    ]
  }
}

The hook script scans for patterns like AWS keys, GitHub tokens, or private key headers:

#!/bin/bash
FILE_CONTENT="$2"
 
# Secret patterns
if echo "$FILE_CONTENT" | grep -qE 'AKIA[0-9A-Z]{16}|ghp_[0-9a-zA-Z]{36}|-----BEGIN.*PRIVATE KEY-----'; then
    echo "ERROR: Potential secret detected"
    exit 1
fi
 
exit 0

A non-zero exit code blocks the operation. This prevents Claude from accidentally writing secrets to files that might be committed.

What interviewers are looking for: Understanding of when each hook fires. Practical security applications. Knowledge that hook exit codes control whether operations proceed.

CLAUDE_ENV_FILE for Dynamic Configuration

The 30-second answer: CLAUDE_ENV_FILE is an environment variable pointing to a temporary file where hooks can export environment variables for the current session. Hooks write KEY=value pairs to this file, and Claude Code loads them automatically.

A common use case: Session initialization that detects project type and configures appropriate variables:

#!/bin/bash
# .claude/hooks/session-start.sh
 
if [ -z "$CLAUDE_ENV_FILE" ]; then
    exit 0
fi
 
# Detect project type
if [ -f "package.json" ]; then
    echo "NODE_PROJECT=true" >> "$CLAUDE_ENV_FILE"
    echo "PACKAGE_MANAGER=$(command -v yarn >/dev/null && echo 'yarn' || echo 'npm')" >> "$CLAUDE_ENV_FILE"
fi
 
if [ -f "pyproject.toml" ]; then
    echo "PYTHON_PROJECT=true" >> "$CLAUDE_ENV_FILE"
fi

This lets Claude automatically know which package manager to use, what test framework is available, and other project-specific context—without repeating it in every session.

What interviewers are looking for: Understanding of the dynamic configuration flow. Practical applications beyond basic setup.

Security and Permissions

Security questions are increasingly common as companies deploy AI tools with real system access. These questions separate developers who've thought about implications from those who haven't.

The Principle of Least Privilege in Plugin Development

The 30-second answer: Grant plugins only the minimum permissions necessary. Explicitly list each required tool in allowedTools rather than using wildcards. Restrict file access to specific directories with allowedPaths. Avoid patterns like mcp__* that grant access to all MCP server tools.

What this looks like in practice:

{
  "mcpServers": {
    "my-plugin": {
      "command": "node",
      "args": ["./plugin.js"],
      "allowedTools": [
        "Read",
        "Grep",
        "mcp__github__get-issue"
      ],
      "allowedPaths": [
        "/home/user/projects/my-repo"
      ]
    }
  }
}

Compare this to the dangerous alternative: "allowedTools": ["*"]. The wildcard grants access to every tool including Write, Edit, Bash—far more than a code analysis plugin needs. If the plugin is compromised or behaves unexpectedly, the blast radius is enormous.

What interviewers are looking for: Security-first thinking without prompting. Understanding that convenience (wildcards) trades off against security.

File Permissions and Path Traversal Prevention

The 30-second answer: Settings files should have chmod 600 permissions (owner read/write only) because they often contain credentials. Prevent path traversal by validating paths against an allowlist, rejecting .. patterns, and always resolving to absolute canonical paths before access.

Path validation that actually works:

const path = require('path');
 
const ALLOWED_PATHS = [
  '/home/user/projects/my-repo',
  '/home/user/documents/safe-data'
];
 
function isPathAllowed(requestedPath) {
  const resolvedPath = path.resolve(requestedPath);
 
  return ALLOWED_PATHS.some(allowedPath => {
    const resolvedAllowed = path.resolve(allowedPath);
    return resolvedPath.startsWith(resolvedAllowed + path.sep) ||
           resolvedPath === resolvedAllowed;
  });
}

The key is resolving paths before checking them. An attacker might request /home/user/projects/my-repo/../../../etc/passwd, which looks like it starts with an allowed path until you resolve it.

What interviewers are looking for: Awareness of common vulnerability patterns. Implementation-level understanding of defenses.

The Claude Agent SDK

For developers building AI-powered applications, the SDK questions test whether you can move from using Claude Code to building with it.

The Two Official SDKs

The 30-second answer: The two official SDKs are the Python SDK (claude-agent-sdk, installed via pip, requires Python 3.10+) and the TypeScript SDK (@anthropic-ai/claude-agent-sdk, installed via npm, requires Node.js 18+). Both provide the same tools, agent loop, and context management that power Claude Code.

Basic usage in both languages:

Python:

from claude_agent_sdk import ClaudeSDKClient
 
client = ClaudeSDKClient(api_key=os.getenv('ANTHROPIC_API_KEY'))
response = client.run("Analyze this codebase and suggest improvements")

TypeScript:

import { ClaudeAgent } from '@anthropic-ai/claude-agent-sdk';
 
const agent = new ClaudeAgent({
  apiKey: process.env.ANTHROPIC_API_KEY
});
 
const result = await agent.run("Analyze this codebase and suggest improvements");

What interviewers are looking for: Awareness that programmatic access exists. Basic familiarity with installation and initialization patterns.

Secure API Key Storage

The 30-second answer: Store API keys in .env files loaded via environment variables. Never hardcode keys. Never commit .env files. Include .env.example in version control showing required variable names without values.

The pattern that prevents accidents:

# .env (NEVER COMMITTED)
ANTHROPIC_API_KEY=sk-ant-api03-xxx
 
# .env.example (COMMITTED)
ANTHROPIC_API_KEY=your-api-key-here

# .gitignore
.env
.env.local
*.local.json

Loading in Python:

from dotenv import load_dotenv
import os
 
load_dotenv()
api_key = os.getenv('ANTHROPIC_API_KEY')
 
if not api_key:
    raise ValueError("ANTHROPIC_API_KEY environment variable not set")

What interviewers are looking for: Understanding that convenience must never override security. Familiarity with standard secret management patterns.

Git Workflow Automation

Claude Code's git integration is one of its most practical features for daily development. These questions test whether you understand what's happening under the hood.

The Commit-and-PR Workflow

The 30-second answer: Claude Code automates git workflows by running git status and git diff in parallel to analyze changes, learning commit style from git log, staging relevant files, generating contextual commit messages, and using gh CLI to create pull requests with comprehensive summaries based on all commits in the branch.

How style learning works: When you ask Claude to create a commit, it first examines your repository's recent history:

git log -20 --pretty=format:'%s'

From this, it extracts patterns: Do you use conventional commits (feat:, fix:)? Imperative mood or past tense? Capitalization after prefixes? It then drafts messages that match your existing style.

Every Claude-generated commit includes attribution:

feat(auth): add email validation to login flow

🤖 Generated with Claude Code

Co-Authored-By: Claude <noreply@anthropic.com>

What interviewers are looking for: Understanding that Claude learns from existing history, not just applies generic rules. Knowledge of the full workflow from analysis through PR creation.

Plugins and the Skills System

The plugin and skills architecture is where Claude Code becomes genuinely extensible. These questions test understanding of the extension model.

The Skills System

The 30-second answer: Skills are specialized capabilities packaged as directories containing a SKILL.md file with instructions and optional supporting files. Unlike slash commands (user-invoked), Skills are model-invoked—Claude decides when to activate them based on matching task descriptions to skill descriptions.

The progressive disclosure model: At session start, Claude loads only skill names and descriptions (~30-50 tokens each) into its context. When a user's request matches a skill's description, Claude invokes the Skill tool, which loads the full instructions. This keeps context usage efficient while providing deep specialization when needed.

A skill structure:

my-skill/
├── SKILL.md          # Required: Core instructions with YAML frontmatter
├── scripts/          # Optional: Helper scripts
├── references/       # Optional: Detailed documentation
└── assets/           # Optional: Templates, data files

What interviewers are looking for: Understanding of model-invoked versus user-invoked patterns. Knowledge of the progressive disclosure architecture.

The CLAUDE_PLUGIN_ROOT Variable

The 30-second answer: CLAUDE_PLUGIN_ROOT is an environment variable containing the absolute path to a plugin's installation directory. It enables plugin-relative path resolution across different installations and platforms.

Why this matters: Claude Code copies plugins to a cache directory rather than running them in-place. Without CLAUDE_PLUGIN_ROOT, plugins couldn't reliably reference their own files:

{
  "hooks": {
    "PostToolUse": [
      {
        "matcher": "Write|Edit",
        "hooks": [
          {
            "type": "command",
            "command": "$CLAUDE_PLUGIN_ROOT/scripts/format-code.sh"
          }
        ]
      }
    ]
  }
}

What interviewers are looking for: Awareness of the caching model and why dynamic path resolution is necessary.

Practical Scenarios

These questions test ability to apply knowledge to realistic situations—exactly what interviewers want to evaluate.

Creating a Test-Build-Release Workflow

The question: A developer wants a command that runs tests, builds the project, and creates a release. How would you structure this?

The approach that works: Create a custom slash command with explicit sequencing and validation between steps:

---
description: Execute complete release workflow
allowed-tools: Bash(npm:*), Bash(git:*), Bash(gh:*)
argument-hint: [major|minor|patch]
---
 
# Release Workflow
 
Execute these steps in strict sequence. Stop immediately if any step fails.
 
## Pre-flight Checks
1. Verify we're on main branch
2. Verify working directory is clean
3. Verify we're up to date with remote
 
## Test Suite
1. Execute `npm test`
2. If ANY tests fail, STOP and report
 
## Build
1. Run `npm run build`
2. Verify build artifacts exist
 
## Version and Release
1. Run `npm version $1`
2. Push with tags
3. Create GitHub release with `gh release create`
 
If any step fails, report the error and DO NOT continue.

The key design principles: explicit ordering, validation checkpoints, fail-fast behavior, and clear tool scoping.

What interviewers are looking for: Systematic thinking about multi-step workflows. Understanding that AI agents need explicit sequencing instructions.

Debugging MCP Server Connections

The question: How would you debug a failing MCP server connection?

The systematic approach:

1. Verify configuration files are valid JSON and point to the right locations
2. Check environment variables are actually set and exported
3. Test the server independently with debug flags enabled
4. Enable verbose logging in the MCP configuration:

{
  "mcpServers": {
    "my-server": {
      "command": "node",
      "args": ["dist/index.js", "--debug"],
      "env": {
        "DEBUG": "*",
        "LOG_LEVEL": "debug"
      }
    }
  }
}

5. Inspect stderr output where MCP servers log errors
6. For HTTP servers, test the endpoint directly with curl

Common failure modes: missing environment variables, timeout issues (increase MCP_TIMEOUT), stdout contamination (MCP servers must use stderr for logging), and authentication failures.

What interviewers are looking for: Systematic debugging methodology. Knowledge of where to look for errors.

Implementing Standardized Code Reviews

The question: Your team wants standardized code reviews. How would you implement this with Claude Code?

A two-tier solution:

First, create a custom /code-review command that codifies your team's review checklist:

---
description: Review code with team standards
allowed-tools: Bash(git:*), Read, Grep
---
 
# Team Code Review Checklist
 
## Architecture
- [ ] Follows layered architecture
- [ ] Proper separation of concerns
 
## Code Quality
- [ ] Functions are single-purpose
- [ ] No magic numbers
 
## Security
- [ ] No hardcoded secrets
- [ ] Input validation present
 
Provide: required changes (blocking) and suggestions (non-blocking)

Second, create a specialized subagent for automated PR reviews that can run via GitHub Actions:

name: Automated Code Review
on:
  pull_request:
    types: [opened, synchronize]
 
jobs:
  review:
    runs-on: ubuntu-latest
    steps:
      - uses: anthropics/claude-code-action@v1
        with:
          mode: 'review'
          anthropic-api-key: ${{ secrets.ANTHROPIC_API_KEY }}

What interviewers are looking for: Layered approach combining human-initiated and automated review. Integration with CI/CD.

Quick Reference: Key Concepts

Preparing for Your Interview

The candidates who impress me most in Claude Code interviews aren't the ones who've memorized syntax—they're the ones who understand trade-offs. Why would you use a subagent instead of a command? Because context isolation matters for complex tasks. Why restrict allowed-tools? Because least privilege isn't just a security checkbox. When would you use useLayoutEffect over useEffect in a hook? Because timing relative to browser paint determines whether users see a flicker.

That reasoning is what senior developers bring to real decisions, and it's what interviewers want to see.

If you want to practice these concepts with hands-on questions, our collection includes 800+ interview questions covering Claude Code, AI development, and traditional frontend/backend topics—each with the detailed explanations that help you understand the reasoning, not just the answers.

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