Title: Quiz 3 — Dataset Design and Data Preparation for Fine-Tuning
Course: AIINFRA 201: Model Adaptation — Fine-Tuning & Quantization > Section: Week 03 — Dataset Design and Data Preparation for Fine-Tuning

Q: The LIMA study demonstrated that a 65-billion-parameter model fine-tuned on just 1,000 curated instruction-response pairs could reach output quality comparable to models trained on much larger datasets.
- True
- False
A: True

Q: According to this week's lecture, what should you define first, before collecting or selecting any fine-tuning examples?
- The learning rate schedule
- The task distribution (what inputs/outputs the model will see in production)
- The total number of GPUs available
- The model's parameter count
A: The task distribution (what inputs/outputs the model will see in production)

Q: Name one reason why duplicate examples in a fine-tuning dataset can actively hurt model performance rather than simply being neutral.
Feedback: Sample/expected answer: Duplicates over-weight a single pattern in the training loss, causing the model to over-fit to that pattern while getting less relative exposure to the rest of the task distribution — reducing generalization.

Q: Below roughly 1,000 examples per task, models tend to memorize training examples verbatim instead of learning generalizable patterns.
- True
- False
A: True

Q: What is the primary purpose of IterableDataset streaming in the Hugging Face datasets library?
- To automatically clean PII from a dataset
- To let you fine-tune without any GPU
- To process data that is too large to materialize fully in memory or on disk
- To generate synthetic instruction-response pairs
A: To process data that is too large to materialize fully in memory or on disk

Q: A common 2026 best practice for scaling a domain-specific dataset when labeled data is scarce is to:
- Scrape any publicly available text regardless of relevance
- Use an LLM to generate additional instruction-response pairs from human-written seed examples
- Skip fine-tuning entirely and rely only on prompting
- Duplicate existing examples to increase apparent dataset size
A: Use an LLM to generate additional instruction-response pairs from human-written seed examples

Q: A colleague says, "We should just collect as much data as possible — more data always makes fine-tuning better." Using evidence from this week, explain what is wrong with this claim.
Feedback: Sample/expected answer: Data quality, not raw volume, is the dominant factor in fine-tuning outcomes. LIMA showed \~1,000 well-curated examples can match much larger datasets, and oversized low-quality datasets (with duplicates or errors) can actively harm performance rather than improve it.

Q: Curating for coverage and correctness of a defined task distribution should happen before worrying about the total volume of training examples.
- True
- False
A: True