Why packages accelerate dbt development - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import a dbt package in your project.

dbt

packages:
  - package: [1]

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

Apandas

Bdbt_utils

Cnumpy

Dmatplotlib

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing packages unrelated to dbt like pandas or numpy.

Forgetting to add the package under the packages section.

The dbt_utils package is a common dbt package that provides useful macros to speed up development.

2fill in blank

medium

Complete the code to use a macro from a dbt package in a model.

dbt

select * from [1].get_relations_by_prefix('my_schema', 'my_prefix')

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

Amatplotlib

Bpandas

Cnumpy

Ddbt_utils

Attempts:

3 left

💡 Hint

Common Mistakes

Using Python package names instead of dbt package names.

Not referencing the package name before the macro.

The get_relations_by_prefix macro is part of the dbt_utils package, which helps with database tasks.

3fill in blank

hard

Fix the error in the dbt model code by completing the blank.

dbt

select
  [1]('column_name') as column_length
from my_table

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

Alength

Blen

Ccount

Dsum

Attempts:

3 left

💡 Hint

Common Mistakes

Using Python's len() instead of SQL's length().

Using aggregate functions like count or sum incorrectly.

In SQL, the function to get the length of a string is length, not len.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that filters words longer than 3 characters.

dbt

{word: [1] for word in words if len(word) [2] 3}

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

Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as the value instead of its length.

Using less than < instead of greater than >.

The dictionary comprehension uses len(word) as the value and filters words with length greater than 3 using >.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase keys to values greater than zero.

dbt

result = [1]: [2] for k, v in data.items() if v [3] 0}

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

Ak.upper()

Bv

C>

Dk.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase keys instead of uppercase.

Using wrong comparison operators like < or ==.

The keys are converted to uppercase with k.upper(), values are v, and the filter keeps values greater than zero with >.