AI and database index suggestions from query logs

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-ai-coding-llm-db-index-recommendations-creators

1. What is the core idea behind "AI and database index suggestions from query logs"?

   1. Use LLMs on slow query logs to recommend indexes worth testing.
   2. GitHub Pages
   3. Decide your tool's UX conventions across an organization
   4. deployer: runs deploy scripts in a narrow shell

2. Which term best describes a foundational idea in "AI and database index suggestions from query logs"?

   1. query logs
   2. indexes
   3. performance
   4. GitHub Pages

3. A learner studying AI and database index suggestions from query logs would need to understand which concept?

   1. indexes
   2. performance
   3. query logs
   4. GitHub Pages

4. Which of these is directly relevant to AI and database index suggestions from query logs?

   1. indexes
   2. query logs
   3. GitHub Pages
   4. performance

5. Which of the following is a key point about AI and database index suggestions from query logs?

   1. Cluster slow queries by table and predicate shape
   2. Propose composite indexes with expected selectivity
   3. GitHub Pages
   4. Decide your tool's UX conventions across an organization

6. What is one important takeaway from studying AI and database index suggestions from query logs?

   1. Decide on the storage cost trade-off
   2. Guarantee an index won't regress writes
   3. GitHub Pages
   4. Decide your tool's UX conventions across an organization

7. What is the key insight about "Index proposal prompt" in the context of AI and database index suggestions from query logs?

   1. GitHub Pages
   2. Decide your tool's UX conventions across an organization
   3. Paste 50 slow queries. Ask: 'Group by access pattern and propose indexes ranked by likely impact and write cost.'
   4. deployer: runs deploy scripts in a narrow shell

8. What is the key insight about "Benchmark before shipping" in the context of AI and database index suggestions from query logs?

   1. GitHub Pages
   2. Decide your tool's UX conventions across an organization
   3. deployer: runs deploy scripts in a narrow shell
   4. Add indexes in shadow first — bad ones can tank write throughput.

9. Which statement accurately describes an aspect of AI and database index suggestions from query logs?

   1. Slow query logs hide patterns; LLMs cluster queries and propose indexes worth benchmarking.
   2. GitHub Pages
   3. Decide your tool's UX conventions across an organization
   4. deployer: runs deploy scripts in a narrow shell

10. Which best describes the scope of "AI and database index suggestions from query logs"?

    1. It is unrelated to ai-coding workflows
    2. It focuses on Use LLMs on slow query logs to recommend indexes worth testing.
    3. It applies only to the opposite beginner tier
    4. It was deprecated in 2024 and no longer relevant

11. Which section heading best belongs in a lesson about AI and database index suggestions from query logs?

    1. GitHub Pages
    2. Decide your tool's UX conventions across an organization
    3. What AI does well here
    4. deployer: runs deploy scripts in a narrow shell

12. Which section heading best belongs in a lesson about AI and database index suggestions from query logs?

    1. GitHub Pages
    2. Decide your tool's UX conventions across an organization
    3. deployer: runs deploy scripts in a narrow shell
    4. What AI cannot do

13. Which of the following is a concept covered in AI and database index suggestions from query logs?

    1. indexes
    2. query logs
    3. performance
    4. GitHub Pages

14. Which of the following is a concept covered in AI and database index suggestions from query logs?

    1. indexes
    2. query logs
    3. performance
    4. GitHub Pages

15. Which of the following is a concept covered in AI and database index suggestions from query logs?

    1. indexes
    2. query logs
    3. performance
    4. GitHub Pages