Analytical Data Modeling: 10 practice questions

Question 1 of 10

What does the grain of a fact table declare?

1. AThe business event or measurement represented by one row
2. BThe file format used to store every dimension
3. CThe schedule used to refresh source systems
4. DThe number of dimension tables in the warehouse for one subject area

Question 2 of 10

Which table primarily stores descriptive context for analytical facts?

1. AA dimension table
2. BA fact table
3. CA checkpoint table
4. DAn orchestration table

Question 3 of 10

What is at the center of a star schema?

1. AA source transaction connected to replicas
2. BA fact table connected directly to dimensions
3. CA raw file connected only to partitions
4. DA scheduler connected to task instances for every pipeline run

Question 4 of 10

Which item is a measure rather than a descriptive attribute?

1. ACustomer region
2. BCalendar month name
3. CProduct category
4. DSales amount

Question 5 of 10

Why is a dimensional model often preferred for analytics?

1. AIt presents facts and descriptive dimensions for aggregation-oriented queries
2. BIt removes all business keys from analytical data
3. CIt maximizes normalized transaction updates across many tables and source writes
4. DIt guarantees every table has the same grain

Question 6 of 10

Why can a dimension use both a business key and a surrogate key?

1. ATo make the source system assign warehouse history
2. BTo retain source identity while distinguishing warehouse row versions
3. CTo force every fact into the current dimension version
4. DTo replace all descriptive attributes with numeric measures

Question 7 of 10

A sales target table has one row per subcategory per quarter. What is its grain?

1. ACustomer and transaction
2. BSubcategory and quarter
3. CProduct and day
4. DCategory and year only

Question 8 of 10

An order-line fact has one row per order and product. A join to product descriptions returns three fact rows for one original line because the business key matches three historical versions. Totals must remain unchanged and history must be correct. What should the model do?

1. AJoin each fact to the single surrogate-keyed version valid for its event
2. BAggregate the multiplied rows and divide totals by the version count
3. CJoin every fact to all dimension versions sharing the business key
4. DReplace the order-line grain with one row per dimension version

Question 9 of 10

A reporting team needs frequent aggregation by customer, product, and date. Source data is highly normalized, query authors struggle with many joins, and update efficiency is not the reporting priority. Which model is the better serving design?

1. AThe unchanged normalized source model as the only reporting layer
2. BA separate table for every level of every descriptive hierarchy
3. CA dimensional star with facts and directly related dimensions
4. DA fact-free model containing only normalized business entities

Question 10 of 10

A customer moves from Madrid to Lisbon. Finance must keep last year's sales attributed to Madrid while new sales use Lisbon, and the source customer ID cannot change. Which key design fits?

1. AKeep the business key and create a new surrogate-keyed dimension version for new facts
2. BCreate a new business key and rewrite the source customer identity
3. CStore both cities on each fact and remove the customer dimension
4. DOverwrite the only dimension row so every fact reports Lisbon