Title: Quiz 13 — Data Infrastructure for AI
Course: AIINFRA 100: AI Systems & Development Environments > Section: Week 13 — Data & Storage Infrastructure for AI

Q: Which storage system applies a schema before data is written, making it ideal for BI dashboards and structured reporting?
- Data lake B) Data warehouse C) Feature store D) ETL pipeline
A: Data lake B) Data warehouse C) Feature store D) ETL pipeline

Q: A data lake can store unstructured data such as images, video, and free-text documents, not just spreadsheet-style rows and columns.
- True
- False
A: True

Q: What is the primary advantage of the Parquet file format over CSV for large-scale ML training data?
- It is easier for humans to read in a text editor B) It is a columnar, compressed, self-describing format that is faster to query at scale C) It cannot store numerical data D) It requires no schema at all, ever
A: It is easier for humans to read in a text editor B) It is a columnar, compressed, self-describing format that is faster to query at scale C) It cannot store numerical data D) It requires no schema at all, ever

Q: Name one thing a lakehouse (such as Delta Lake) combines from both data warehouses and data lakes.
Feedback: Sample/expected answer: Sample answer: the flexible, raw/multi-format storage of a data lake with the structure, reliability, and fast query performance of a data warehouse (accept any reasonable equivalent).

Q: "More training data is always better, regardless of its quality," is an accurate way to think about building AI models.
- True
- False
A: False

Q: A feature store's "online store" is best described as:
- A public website where anyone can download features B) A low-latency store of the most current feature values, used for real-time predictions C) A backup copy of the offline store D) A place where raw, uncleaned data is dumped
A: A public website where anyone can download features B) A low-latency store of the most current feature values, used for real-time predictions C) A backup copy of the offline store D) A place where raw, uncleaned data is dumped

Q: What term describes the problem where a model performs well during training but poorly in production because a feature was calculated differently in each environment?
Feedback: Sample/expected answer: Training-serving skew

Q: Which of the following is NOT one of the data-quality dimensions emphasized for AI systems in this week's lecture?
- Representativeness / bias B) Recency C) Font consistency D) Completeness
A: Representativeness / bias B) Recency C) Font consistency D) Completeness