48 minutes ago
Development
[100% OFF] AI Agent & Vibe Coding Debugging Practice Tests (2026)
60 Production Scenario Questions on Context Rot, Subagent Collisions, AST/DOM Harnesses, WebAssembly & Edge Security
Course Description
Autonomous AI coding agents and multi-agent IDE workflows have transformed how modern engineering teams build software. However, once a codebase scales beyond a single prototype file, developers encounter severe production failure modes: context rot, subagent Git worktree collisions, specification gaming, mocked-out unit tests, visual layout overlaps, and silent security regressions.
This comprehensive Practice Test suite contains 60 scenario-based architectural debugging questions across 3 full-length exams designed specifically for software engineers, AI systems architects, and technical leads working with Cursor, Claude Code, Windsurf, and autonomous agent harnesses.
What You Will Master Across 3 Full-Length Practice Exams (60 Questions Total):
1. Exam 1 — Context Isolation, Prompt Injection & Anti-Slop Harnesses (20 Questions): Diagnose attention dilution in long trajectories, enforce immutable SHA-256 test-file locks, prevent reward hacking where agents delete or skip failing assertions, and neutralize indirect prompt injection inside Model Context Protocol tool outputs.
2. Exam 2 — Multi-IDE Rules, Deterministic Compilers & Visual/DOM Layouts (20 Questions): Eliminate instruction drift across multi-IDE rule compilers, build deterministic Abstract Syntax Tree and bounding-box DOM collision gates, prevent SVG viewBox clipping and off-screen viewport timeouts, and enforce two-layer deterministic plus semantic verification.
3. Exam 3 — Edge/Wasm Privacy, Programmatic SEO & Production Agent Ops (20 Questions): Debug client-side WebAssembly linear heap growth, 300 DPI JFIF binary header injection, linear-time Euclidean Distance Transforms, Content Security Policy network sandboxing, and Content-Addressable SHA-256 incremental build caches.
Every single question includes a realistic production failure scenario, four plausible architectural choices, a deep-dive root-cause explanation for the correct answer, and clear breakdowns of why each distractor fails in real-world engineering environments.
