Query profiling and optimization in dbt - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to enable query profiling in dbt.

dbt

profile: [1]

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

Aenabled

Bdisabled

Ctrue

Dfalse

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'disabled' or 'false' which turns profiling off.

Using 'true' which is not the correct keyword here.

Setting profile: enabled activates query profiling in dbt, allowing you to analyze query performance.

2fill in blank

medium

Complete the code to set the query timeout in dbt.

dbt

query_timeout: [1]

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

A30

B60

C0

D300

Attempts:

3 left

💡 Hint

Common Mistakes

Setting timeout to 0 which disables timeout.

Setting too low a value causing premature query stops.

Setting query_timeout: 60 sets the timeout to 60 seconds, a common default for query execution limits.

3fill in blank

hard

Fix the error in the dbt model config to enable incremental materialization with query optimization.

dbt

config(materialized='[1]')

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

Aephemeral

Btable

Cview

Dincremental

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'table' or 'view' which do not support incremental logic.

Using 'ephemeral' which is for temporary models.

The materialization must be set to incremental to enable incremental builds and optimize queries accordingly.

4fill in blank

hard

Fill both blanks to create a dbt model that filters rows and orders results for optimization.

dbt

select * from [1] where [2] > 100 order by created_at desc

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

Asource_table

Bid

Cscore

Duser_id

Attempts:

3 left

💡 Hint

Common Mistakes

Using a column name in place of the table name.

Filtering on an ID column which may not optimize well.

Using source_table as the table and filtering on score > 100 helps optimize the query by reducing rows processed.

5fill in blank

hard

Fill all three blanks to write a dbt model that aggregates data with grouping and filtering for optimization.

dbt

select [1], count(*) as total from [2] where [3] > 50 group by [1]

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

Acategory

Bevents

Cduration

Duser_id

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up table and column names.

Using a non-numeric column for filtering.

This query groups by category, counts rows from events, and filters where duration > 50 to optimize data aggregation.