AI-Suggested Database Indexes from Slow Query Logs

End-of-lesson check

10 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-ai-coding-LLM-database-index-suggestions-creators

1. What is the main idea of "AI-Suggested Database Indexes from Slow Query Logs"?

   1. Feed slow query logs to an LLM to draft index proposals — and the guardrails that keep them safe.
   2. Use AI as the final authority for the whole decision
   3. Avoid checking the answer once it sounds polished
   4. Focus only on speed instead of judgment

2. Which concept is most central to "AI-Suggested Database Indexes from Slow Query Logs"?

   1. indexes
   2. database-tuning
   3. slow-query-logs
   4. query-planning

3. Which use of AI fits this topic best?

   1. Predict write amplification on your traffic shape
   2. Let the AI decide what matters without your review
   3. Cluster slow queries by shape and propose covering indexes
   4. Use the answer before checking whether it fits the situation

4. Which limitation should you watch for in this topic?

   1. Cluster slow queries by shape and propose covering indexes
   2. Explain the topic in plain language
   3. Organize a draft for human review
   4. Predict write amplification on your traffic shape

5. What should a careful learner remember about "Index proposal prompt"?

   1. Use AI to draft or organize ideas about database-tuning, then verify before acting.
   2. Skip the context so the tool can guess faster
   3. Treat the output as private even after sharing it online
   4. Use the answer without checking the source

6. You want to use AI after this lesson. What is the safest next step?

   1. Act immediately because the AI answer is written clearly
   2. Use AI for drafting and comparison, but verify before publishing or relying on it.
   3. Hide uncertainty so the final answer looks cleaner
   4. Use private or sensitive details before checking permission

7. How should AI output about database-tuning be treated?

   1. As proof that no other source is needed
   2. As a replacement for context, consent, or expert review
   3. As a draft or helper output that still needs human judgment and verification
   4. As something that becomes correct when it sounds confident

8. Name one way to verify an AI answer about database-tuning.

9. Which action would help you apply "AI-Suggested Database Indexes from Slow Query Logs" responsibly?

   1. Know storage cost ceilings unless you tell it
   2. Use the tool to avoid thinking through the tradeoff
   3. Keep going even if the output conflicts with a trusted source
   4. Spot missing indexes on frequent join keys

10. Which choice is a bad use of AI for this lesson?

    1. Know storage cost ceilings unless you tell it
    2. Cluster slow queries by shape and propose covering indexes
    3. Ask for a plain-language explanation of indexes
    4. Compare the answer with a trusted source