AI for Tech Debt Tracking and Prioritization

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-ai-coding-AI-tech-debt-tracking-creators

1. Which of the following is something AI canNOT do for tech debt tracking?

   1. Replace the team's judgment about what priorities matter
   2. Track debt reduction over time with metrics
   3. Generate remediation proposals with suggested fixes
   4. Analyze codebases for known anti-patterns and outdated dependencies

2. In the context of tech debt prioritization, what does 'blast radius' measure?

   1. The time elapsed since the debt was first introduced
   2. How many engineers or teams are affected by the debt
   3. The monetary cost to completely rewrite the affected code
   4. The number of lines of code in a problematic file

3. How can AI help identify outdated dependencies in a codebase?

   1. By scanning for known security vulnerabilities and version history
   2. By automatically deleting dependencies it considers old
   3. By checking which developers wrote the dependency code
   4. By writing entirely new dependency management systems

4. A development team is choosing between addressing high-impact tech debt and starting a new feature. What does the lesson say about how to handle this?

   1. The feature should always come first to satisfy customers
   2. The highest-impact debt should always be addressed immediately
   3. The team should have a conversation weighing trade-offs, not just accept AI's ranking
   4. AI should make the final decision since it has all the data

5. Which of the following is a recommended integration point for AI tech debt tracking in team workflows?

   1. Customer support ticket triage
   2. Sales pipeline meetings
   3. Weekly team planning and sprint ceremonies
   4. Marketing campaign reviews

6. What type of output can an AI tech debt system generate to help developers?

   1. Remediation proposals with suggested fixes
   2. Performance reviews for each developer
   3. Marketing copy for feature releases
   4. Customer satisfaction surveys

7. What is required for AI-generated remediation proposals to be useful in practice?

   1. They must be reviewed and adapted by engineers who understand the context
   2. They must be automatically applied without human review
   3. They must be approved by customers before implementation
   4. They must match exactly what the highest-paid person suggests

8. How does AI prioritization differ fundamentally from a single engineer's manual prioritization?

   1. AI ignores business context entirely
   2. AI prioritizes randomly to introduce variety
   3. AI only prioritizes code written in the past month
   4. AI can analyze entire codebases consistently and at scale

9. When designing an AI tech debt tracking system, which component analyzes code for known problematic patterns?

   1. Time-series tracking dashboard
   2. Remediation proposal generator
   3. Codebase analysis methodology
   4. Impact scoring engine

10. In impact scoring for tech debt, what does 'business impact' consider?

    1. How many lines of code were written by contractors
    2. The programming languages used in the affected code
    3. Revenue effects, customer experience, and compliance risks
    4. Which developer originally wrote the problematic code

11. Which statement best summarizes "AI for Tech Debt Tracking and Prioritization"?

    1. It claims the subject can be safely ignored by everyday users.
    2. It says the topic is too dangerous to discuss with beginners.
    3. Tech debt usually rots in a wiki nobody reads. AI can analyze codebases to surface debt, prioritize by impact, and propose remediation.
    4. It argues that the topic is irrelevant outside academic settings.

12. What should you do with an AI-generated draft before using it?

    1. Forward it to a friend without reading it yourself.
    2. Read it carefully, check facts, and decide what (if anything) to keep.
    3. Submit it untouched and assume everything is correct.
    4. Delete the entire response and start over from scratch every time.

13. Which best captures the focus of "AI for Tech Debt Tracking and Prioritization"?

    1. It centers on tech debt, code analysis, prioritization.
    2. It is mainly about marketing strategies for retail stores.
    3. It explains how to bake bread and pastries at home.
    4. It focuses on hardware repair and soldering circuits.

14. Which habit is the biggest pitfall when applying these ideas?

    1. Asking for examples to make a concept clearer.
    2. Pausing to verify results before acting on them.
    3. Comparing answers from more than one source.
    4. Skipping review and trusting the first output without checking it.

15. Who is the intended audience for this material?

    1. It targets professional chefs working in commercial kitchens.
    2. It is written exclusively for licensed pilots in training.
    3. It is written for high-school and adult learners going deeper working on ai-coding.
    4. It is intended only for graduate researchers in physics.