Big Data vs. Good Data: The Tradeoff

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-data-big-vs-good

1. What is the core idea behind "Big Data vs. Good Data: The Tradeoff"?

   1. The old mantra was more data always wins. The new reality is more complicated. Sometimes a small, hand-crafted dataset beats a giant messy one.
   2. Use stratified sampling when collecting new data
   3. Model training loss curves (always plot on log-y)
   4. recidivism

2. Which term best describes a foundational idea in "Big Data vs. Good Data: The Tradeoff"?

   1. curation
   2. data-centric AI
   3. scaling laws
   4. Chinchilla

3. A learner studying Big Data vs. Good Data: The Tradeoff would need to understand which concept?

   1. data-centric AI
   2. scaling laws
   3. curation
   4. Chinchilla

4. Which of these is directly relevant to Big Data vs. Good Data: The Tradeoff?

   1. data-centric AI
   2. curation
   3. Chinchilla
   4. scaling laws

5. Which of the following is a key point about Big Data vs. Good Data: The Tradeoff?

   1. Broad world knowledge (need coverage of every topic)
   2. Rare languages and dialects
   3. Long-tail facts and entities
   4. Visual recognition in novel environments

6. Which of these does NOT belong in a discussion of Big Data vs. Good Data: The Tradeoff?

   1. Broad world knowledge (need coverage of every topic)
   2. Rare languages and dialects
   3. Use stratified sampling when collecting new data
   4. Long-tail facts and entities

7. Which statement is accurate regarding Big Data vs. Good Data: The Tradeoff?

   1. Reasoning with step-by-step solutions
   2. Instruction-following (a few thousand great examples beat millions of scraped Q&A pairs)
   3. Classification with clear labels (medical imaging)
   4. Domain-specific tasks (legal, financial)

8. Which of these does NOT belong in a discussion of Big Data vs. Good Data: The Tradeoff?

   1. Reasoning with step-by-step solutions
   2. Classification with clear labels (medical imaging)
   3. Use stratified sampling when collecting new data
   4. Instruction-following (a few thousand great examples beat millions of scraped Q&A pairs)

9. What is the key insight about "Phi's surprising result" in the context of Big Data vs. Good Data: The Tradeoff?

   1. Microsoft's Phi-3 Mini has only 3.8B parameters but matches or beats much larger models on reasoning tasks.
   2. Use stratified sampling when collecting new data
   3. Model training loss curves (always plot on log-y)
   4. recidivism

10. What is the recommended tip about "Ground your practice in fundamentals" in the context of Big Data vs. Good Data: The Tradeoff?

    1. Use stratified sampling when collecting new data
    2. Every AI capability has an underlying mechanism. Understanding that mechanism tells you where it'll fail — which is more…
    3. Model training loss curves (always plot on log-y)
    4. recidivism

11. Which statement accurately describes an aspect of Big Data vs. Good Data: The Tradeoff?

    1. Use stratified sampling when collecting new data
    2. Model training loss curves (always plot on log-y)
    3. For years, the Google philosophy was simple: more data wins. Peter Norvig famously argued in The Unreasonable Effectiveness of Data that sma…
    4. recidivism

12. What does working with Big Data vs. Good Data: The Tradeoff typically involve?

    1. Use stratified sampling when collecting new data
    2. Model training loss curves (always plot on log-y)
    3. recidivism
    4. Andrew Ng coined data-centric AI around 2021. His argument: instead of endlessly tweaking the model, spend your effort improving the data.

13. Which of the following is true about Big Data vs. Good Data: The Tradeoff?

    1. The big idea: the size-versus-quality debate is over. The answer is both, used for different stages.
    2. Use stratified sampling when collecting new data
    3. Model training loss curves (always plot on log-y)
    4. recidivism

14. Which best describes the scope of "Big Data vs. Good Data: The Tradeoff"?

    1. It is unrelated to foundations workflows
    2. It focuses on The old mantra was more data always wins. The new reality is more complicated. Sometimes a small, ha
    3. It applies only to the opposite beginner tier
    4. It was deprecated in 2024 and no longer relevant

15. Which section heading best belongs in a lesson about Big Data vs. Good Data: The Tradeoff?

    1. Use stratified sampling when collecting new data
    2. Model training loss curves (always plot on log-y)
    3. The counter-movement: data-centric AI
    4. recidivism