Creating your first model in dbt - Practice Exercises

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this dbt model SQL?

Given the following dbt model SQL, what will be the resulting table's row count if the source table has 100 rows with 20 rows having NULL in the column status?

dbt

select * from {{ source('raw_data', 'orders') }} where status is not null

A 0 rows

B 100 rows

C 20 rows

D 80 rows

💡 Hint

Think about how the WHERE clause filters rows with NULL status.

The WHERE clause excludes rows where status is NULL, so only 80 rows remain.

❓ data_output

intermediate

What columns will the model output have?

Consider this dbt model SQL that selects specific columns from a source table. What columns will the resulting model table contain?

dbt

select order_id, customer_id, order_date from {{ source('raw_data', 'orders') }}

A All columns from orders table

B order_id, customer_id, order_date

C Only order_id

D order_id, customer_id, order_date, status

💡 Hint

Look at the SELECT statement columns.

The model explicitly selects only order_id, customer_id, and order_date columns.

🔧 Debug

advanced

Why does this dbt model fail to compile?

This dbt model SQL throws a compilation error. What is the cause?

dbt

select * from {{ source('raw_data', orders) }}

A Missing quotes around the table name 'orders' in the source function

B Missing schema name in the source function

C Using * is not allowed in dbt models

D The source function requires three arguments

💡 Hint

Check the syntax of the source function arguments.

The table name must be a string literal, so it needs quotes.

🚀 Application

advanced

How to create a model that aggregates total sales per customer?

You want to create a dbt model that shows total sales amount per customer from the orders source table with columns customer_id and amount. Which SQL snippet correctly achieves this?

A select customer_id, amount from {{ source('raw_data', 'orders') }} group by customer_id

B select customer_id, sum(amount) from {{ source('raw_data', 'orders') }}

C select customer_id, sum(amount) as total_sales from {{ source('raw_data', 'orders') }} group by customer_id

D select customer_id, total(amount) as total_sales from {{ source('raw_data', 'orders') }} group by customer_id

💡 Hint

Remember to use GROUP BY when aggregating and alias the sum.

Option C correctly sums amounts per customer and groups by customer_id.

🧠 Conceptual

expert

What is the main benefit of using dbt models for data transformation?

Choose the best explanation for why dbt models are useful in data projects.

A They allow version-controlled, modular SQL transformations that build a clear data pipeline

B They automatically clean data without writing any SQL code

C They replace the need for any database or data warehouse

D They provide a graphical interface to drag and drop data transformations

💡 Hint

Think about how dbt helps organize and manage SQL code.

dbt models let you write SQL in files that are version controlled and build a dependency graph for transformations.

Practice

1. What is the main purpose of a dbt model?

easy

A. To write Python scripts for data analysis

B. To store raw data without changes

C. To create visual dashboards

D. To transform raw data into clean, usable tables

Solution

1. Step 1: Understand the role of dbt models

   dbt models are SQL files that transform raw data into clean tables for analysis.

2. Step 2: Compare options with this role

   Only To transform raw data into clean, usable tables describes transforming raw data into clean tables, which matches the purpose of dbt models.

3. Final Answer:

   To transform raw data into clean, usable tables -> Option D

4. Quick Check:

   dbt model purpose = transform raw data [OK]

Hint: Remember: dbt models clean and transform data [OK]

Common Mistakes:

• Confusing models with dashboards
• Thinking models store raw data unchanged
• Assuming models are Python scripts

2. Which of the following is the correct way to define a simple dbt model SQL file?

easy

A. SELECT * FROM raw_data

B. CREATE MODEL my_model AS SELECT * FROM raw_data

C. dbt run SELECT * FROM raw_data

D. INSERT INTO model SELECT * FROM raw_data

Solution

1. Step 1: Recall dbt model syntax

   A dbt model is a SQL SELECT statement saved as a .sql file in the models folder.

2. Step 2: Evaluate each option

   SELECT * FROM raw_data is a simple SELECT statement, which is the correct way to define a model. Options B, C, and D use incorrect syntax or commands not used in dbt model files.

3. Final Answer:

   SELECT * FROM raw_data -> Option A

4. Quick Check:

   dbt model = simple SELECT statement [OK]

Hint: dbt models are just SELECT queries saved as files [OK]

Common Mistakes:

• Using CREATE MODEL syntax (not valid in dbt)
• Trying to run dbt commands inside SQL files
• Using INSERT statements instead of SELECT

3. Given the following dbt model SQL code saved as models/my_first_model.sql:

SELECT id, name FROM raw_customers WHERE active = true

What will be the output when you run dbt run?

medium

A. Nothing happens because dbt run does not create models

B. An error because of missing CREATE TABLE statement

C. A new table or view named my_first_model with active customers only

D. The raw_customers table will be deleted

Solution

1. Step 1: Understand what dbt run does

   Running dbt run executes model SQL files and creates tables or views with the model name.

2. Step 2: Analyze the model SQL

   The model selects id and name from raw_customers where active is true, so the output table will contain only active customers.

3. Final Answer:

   A new table or view named my_first_model with active customers only -> Option C

4. Quick Check:

   dbt run creates model tables = filtered active customers [OK]

Hint: dbt run creates tables from your SELECT queries [OK]

Common Mistakes:

• Expecting CREATE TABLE in model SQL
• Thinking dbt deletes source tables
• Believing dbt run does nothing

4. You wrote this dbt model SQL file named models/customer_summary.sql:

SELECT customer_id, order_id, COUNT(*) AS orders_count
FROM orders
GROUP BY customer_id

When you run dbt run, you get an error. What is the most likely cause?

medium

A. Missing a semicolon at the end of the SQL statement

B. The GROUP BY column does not match the SELECT columns

C. The SELECT statement is missing a FROM clause

D. The model file is not saved in the models folder

Solution

1. Step 1: Recall GROUP BY rules

   When using GROUP BY, all non-aggregated columns in SELECT must either be aggregated or included in GROUP BY.

2. Step 2: Analyze the SELECT and GROUP BY columns

   SELECT has customer_id (in GROUP BY), order_id (neither aggregated nor grouped), COUNT(*) (aggregated). Thus, GROUP BY does not match SELECT columns.

3. Final Answer:

   The GROUP BY column does not match the SELECT columns -> Option B

4. Quick Check:

   GROUP BY must include all non-aggregated SELECT columns [OK]

Hint: Ensure all non-aggregated SELECT columns are in GROUP BY [OK]

Common Mistakes:

• Forgetting to include non-aggregated columns in GROUP BY
• Assuming semicolon is required
• Saving model outside models folder

5. You want to create a dbt model that shows the total sales per product category, but only for categories with total sales above 1000. Which SQL code correctly implements this in your model file?

hard

A. SELECT category, SUM(sales) AS total_sales FROM sales_data GROUP BY category HAVING SUM(sales) > 1000

B. SELECT category, SUM(sales) AS total_sales FROM sales_data WHERE total_sales > 1000 GROUP BY category

C. SELECT category, SUM(sales) AS total_sales FROM sales_data GROUP BY category HAVING total_sales > 1000

D. SELECT category, SUM(sales) AS total_sales FROM sales_data WHERE SUM(sales) > 1000 GROUP BY category

Solution

1. Step 1: Understand filtering on aggregated values

   To filter groups by aggregated values, use HAVING with the aggregate function, not WHERE.

2. Step 2: Analyze each option

   SELECT category, SUM(sales) AS total_sales FROM sales_data GROUP BY category HAVING total_sales > 1000 uses HAVING total_sales > 1000, but total_sales is an alias and cannot be used directly in HAVING in many SQL dialects. SELECT category, SUM(sales) AS total_sales FROM sales_data GROUP BY category HAVING SUM(sales) > 1000 uses HAVING SUM(sales) > 1000, which is correct. Options B and D incorrectly use WHERE with aggregate functions, which is invalid.

3. Final Answer:

   SELECT category, SUM(sales) AS total_sales FROM sales_data GROUP BY category HAVING SUM(sales) > 1000 -> Option A

4. Quick Check:

   Use HAVING with aggregate functions to filter groups [OK]

Hint: Use HAVING with aggregate functions, not WHERE [OK]

Common Mistakes:

• Using WHERE to filter aggregated results
• Using alias names in HAVING clause
• Forgetting GROUP BY when aggregating