Module 4 Quiz – Analytics Engineering

Question 1: What is the main goal of data modeling? *

A) Improve website load times

B) Design how data is stored and related

C) Make data pretty

D) Format CSV files

Question 2: Which of the following best describes a dimension in dimensional modeling? *

A) A numeric metric like revenue

B) A record of table creation history

C) A descriptive attribute like product or customer

D) A compressed index of tables

Question 3: What does a fact table typically contain? *

A) File metadata

B) Unique user IDs only

C) Measurable data like sales or clicks

D) SQL queries

Question 4: What is a star schema? *

A) A central fact table connected to surrounding dimension tables

B) A diagram for machine learning models

C) A database backup plan

D) A type of JSON layout

Question 5: In normalization, what is the main objective? *

A) Merge all data into one table

B) Eliminate redundancy and improve data integrity

C) Speed up dashboards

D) Format data for Excel

Question 6: What is a downside of excessive normalization in a data warehouse? *

A) More storage usage

B) Complex joins that hurt query performance

C) Incomplete records

D) Poor JSON support

Question 7: What’s a key reason to denormalize data in a data warehouse? *

A) Reduce ETL errors

B) Decrease table size

C) Improve query performance and reduce joins

D) Save bandwidth

Question 8: Which of the following is NOT a typical dimension? *

A) Time

B) Customer

C) Product

D) Total Revenue

Question 9: In dimensional modeling, what does “grain” refer to? *

A) Level of detail for each row in a fact table

B) Speed of query execution

C) Table size on disk

D) Number of columns

Question 10: Which schema is more normalized: star or snowflake? *

A) Star

B) Snowflake

C) Flat

D) Galaxy