Why incremental models save time and cost in dbt - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to define an incremental model in dbt.

dbt

{{config(materialized='[1]')}}

select * from source_table

Drag options to blanks, or click blank then click option'

Aephemeral

Btable

Cview

Dincremental

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing 'table' causes full rebuilds every time.

Using 'view' does not store data physically.

Selecting 'ephemeral' runs the model inline without saving.

The incremental materialization tells dbt to only process new or changed data, saving time and cost.

2fill in blank

medium

Complete the code to add a condition that processes only new rows in an incremental model.

dbt

select * from source_table where updated_at > (select max(updated_at) from {{this}}) or [1]

Drag options to blanks, or click blank then click option'

Atrue

Bfalse

C1=0

Dupdated_at < max(updated_at)

Attempts:

3 left

💡 Hint

Common Mistakes

Using '1=0' excludes all rows.

Using 'false' excludes all rows.

Using 'updated_at < max(updated_at)' is logically incorrect.

The condition true ensures the full table is processed on the first run when the target table is empty.

3fill in blank

hard

Fix the error in the incremental model config to enable incremental updates.

dbt

{{config(materialized='[1]', unique_key='id')}}

select * from source_table

Drag options to blanks, or click blank then click option'

Aincremental

Btable

Cview

Dephemeral

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'table' causes full rebuilds.

Using 'view' or 'ephemeral' ignores unique_key.

Setting materialized='incremental' enables dbt to run incremental updates using the unique_key.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps table names to their row counts, but only for tables with more than 1000 rows.

dbt

{table: count for table, count in table_counts.items() if count [1] 1000 and table [2] 'archive'}

Drag options to blanks, or click blank then click option'

A>

B<

C!=

D==

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' includes small tables.

Using '==' includes only 'archive' table.

Using '!=' incorrectly excludes other tables.

The comprehension filters tables with counts greater than 1000 and excludes tables named 'archive'.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase table names to their row counts, but only for tables with counts greater than 500 and names not equal to 'temp'.

dbt

{ [1]: [2] for table, count in table_counts.items() if count [3] 500 and table != 'temp' }

Drag options to blanks, or click blank then click option'

Atable.upper()

Bcount

C>

Dtable.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using table.lower() instead of uppercase.

Using < or == for count filter.

Swapping keys and values in the comprehension.

This comprehension creates a dictionary with uppercase table names as keys and counts as values, filtering counts greater than 500 and excluding 'temp'.