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'.

Practice

1. What is the main reason incremental models save time and cost in dbt?

easy

A. They skip data validation steps to speed up execution.

B. They run all transformations in parallel automatically.

C. They use less memory by compressing data during processing.

D. They process only new or changed data instead of the entire dataset.

Solution

1. Step 1: Understand incremental model behavior

   Incremental models update only the new or changed data instead of reprocessing everything.

2. Step 2: Connect behavior to time and cost savings

   Processing less data means less compute time and lower cloud costs.

3. Final Answer:

   They process only new or changed data instead of the entire dataset. -> Option D

4. Quick Check:

   Incremental = process new data only [OK]

Hint: Incremental means update only new or changed data [OK]

Common Mistakes:

• Thinking incremental models reprocess all data
• Confusing incremental with parallel processing
• Assuming incremental skips validation

2. Which dbt function is used inside an incremental model to filter only new or changed data?

easy

A. is_incremental()

B. filter_new()

C. increment_only()

D. new_data_check()

Solution

1. Step 1: Recall dbt incremental syntax

   dbt provides the function is_incremental() to detect incremental runs.

2. Step 2: Identify correct function for filtering

   This function is used in the WHERE clause to filter new or changed data during incremental runs.

3. Final Answer:

   is_incremental() -> Option A

4. Quick Check:

   Use is_incremental() to filter incremental data [OK]

Hint: Use is_incremental() to filter new data in incremental models [OK]

Common Mistakes:

• Using non-existent functions like filter_new()
• Confusing function names with similar words
• Not using any function to filter incremental data

3. Given this dbt incremental model snippet, what rows will be processed during an incremental run?

select * from source_table
{% if is_incremental() %}
where updated_at > (select max(updated_at) from {{ this }})
{% endif %}

medium

A. All rows from source_table every time

B. Only rows with updated_at newer than the max in the target table

C. Only rows with updated_at older than the max in the target table

D. No rows are processed during incremental runs

Solution

1. Step 1: Analyze the WHERE clause inside is_incremental()

   The condition filters rows where updated_at is greater than the max updated_at in the target table.

2. Step 2: Understand incremental run behavior

   During incremental runs, only new or updated rows with newer timestamps are processed.

3. Final Answer:

   Only rows with updated_at newer than the max in the target table -> Option B

4. Quick Check:

   Incremental filters rows newer than max timestamp [OK]

Hint: Check updated_at > max(updated_at) for incremental rows [OK]

Common Mistakes:

• Thinking all rows are processed every time
• Confusing greater than with less than in filter
• Assuming no rows processed during incremental

4. This incremental model code has an error. What is the problem?

select * from source_table
{% if is_incremental() %}
where updated_at < (select max(updated_at) from {{ this }})
{% endif %}

medium

A. The filter uses < instead of >, so it excludes new data.

B. The is_incremental() function is misspelled.

C. The model is missing a primary key declaration.

D. The select statement is missing a GROUP BY clause.

Solution

1. Step 1: Check the filter condition logic

   The condition uses < (less than) which filters older rows, not new or changed ones.

2. Step 2: Understand incremental filtering needs

   Incremental models must filter rows newer than the max timestamp to add new data.

3. Final Answer:

   The filter uses < instead of >, so it excludes new data. -> Option A

4. Quick Check:

   Incremental filter must use > for new data [OK]

Hint: Use > in incremental filter to include new data [OK]

Common Mistakes:

• Using less than instead of greater than in filter
• Ignoring is_incremental() spelling
• Thinking primary key or GROUP BY is required here

5. You have a large sales table updated daily. You want to build a dbt incremental model that only adds new sales since the last run. Which approach best saves time and cost?

hard

A. Use an incremental model but do not filter any rows inside is_incremental() block.

B. Use a full-refresh model that rebuilds the entire sales table every run.

C. Use an incremental model with is_incremental() filtering rows where sale_date > max(sale_date) in the target table.

D. Use a model that only selects sales from the current day without checking previous data.

Solution

1. Step 1: Identify incremental model best practice

   Filtering new rows using is_incremental() with a condition on sale_date ensures only new data is processed.

2. Step 2: Compare other options

   Full-refresh rebuilds all data (slow, costly). No filtering processes all data each run. Selecting only current day misses data if runs are delayed.

3. Final Answer:

   Use an incremental model with is_incremental() filtering rows where sale_date > max(sale_date) in the target table. -> Option C

4. Quick Check:

   Filter new data with is_incremental() for efficiency [OK]

Hint: Filter new rows with is_incremental() and max date check [OK]

Common Mistakes:

• Choosing full-refresh for large tables
• Not filtering rows in incremental runs
• Selecting only current day without history check