Why dbt transformed data transformation workflows - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to select all columns from the transformed table.

dbt

SELECT * FROM [1];

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

Araw_data

Btransformed_data

Cstaging_table

Danalytics

Attempts:

3 left

💡 Hint

Common Mistakes

Selecting from raw_data instead of transformed_data

Using staging_table which is intermediate

Choosing analytics which is usually for reporting

The transformed_data table contains the cleaned and modeled data after dbt transformations.

2fill in blank

medium

Complete the code to create a new table with dbt using a SELECT statement.

dbt

CREATE TABLE [1] AS SELECT * FROM source_data;

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

Atransformed_data

Braw_data

Canalytics

Dtemp_table

Attempts:

3 left

💡 Hint

Common Mistakes

Creating raw_data which is usually the input

Using analytics which is for reporting

Choosing temp_table which is temporary

dbt creates transformed_data tables by applying SQL transformations on source data.

3fill in blank

hard

Fix the error in the dbt model SQL by completing the WHERE clause to filter active users.

dbt

SELECT * FROM users WHERE status = [1];

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

A"active"

Bactive

C'active'

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using unquoted active causing syntax error

Using double quotes which may not work in all SQL dialects

Using 1 which is a number, not a string

The status value is a string and must be enclosed in single quotes in SQL.

4fill in blank

hard

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

dbt

{table: [1] for table, [2] in table_counts.items() if [1] > 1000}

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

Acount

Brows

Dnum_rows

Attempts:

3 left

💡 Hint

Common Mistakes

Using the same variable name for both blanks

Using variables not defined in the loop

Not matching variable names in the condition

The comprehension uses count as the value and rows as the variable for counts in the loop.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase table names to their row counts, filtering counts greater than 500.

dbt

{ [1]: [2] for [3], count in table_counts.items() if count > 500 }

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

Atable.upper()

Bcount

Ctable

Dtable.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase instead of uppercase for keys

Mixing variable names in the loop

Using count as key instead of value

The comprehension maps the uppercase table name (table.upper()) to the count, iterating over table and count.