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.

Practice

1. What is one main reason dbt changed how data transformation workflows are done?

easy

A. It breaks complex data tasks into smaller, clear steps called models.

B. It replaces SQL with a new programming language.

C. It removes the need for testing data transformations.

D. It stores data in a new type of database automatically.

Solution

1. Step 1: Understand dbt's approach to data workflows

   dbt organizes data transformations into small, manageable pieces called models, making workflows clearer.

2. Step 2: Compare options to dbt's features

   Only It breaks complex data tasks into smaller, clear steps called models. correctly describes this key feature; others are incorrect or unrelated.

3. Final Answer:

   It breaks complex data tasks into smaller, clear steps called models. -> Option A

4. Quick Check:

   dbt uses models to simplify workflows = B [OK]

Hint: Remember: dbt splits work into models for clarity [OK]

Common Mistakes:

• Thinking dbt replaces SQL
• Believing dbt removes testing
• Assuming dbt changes database types

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

easy

A. dbt run my_model;

B. CREATE MODEL my_model AS SELECT * FROM source_table;

C. SELECT * FROM source_table;

D. DEFINE MODEL my_model SELECT * FROM source_table;

Solution

1. Step 1: Recall dbt model definition syntax

   In dbt, a model is defined simply by writing a SQL SELECT statement in a .sql file.

2. Step 2: Evaluate each option

   SELECT * FROM source_table; is just a SELECT statement, which is the correct way. The other options use incorrect syntax such as CREATE MODEL, dbt run command, or DEFINE MODEL.

3. Final Answer:

   SELECT * FROM source_table; -> Option C

4. Quick Check:

   dbt models are SQL SELECT queries = A [OK]

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

Common Mistakes:

• Trying to use CREATE MODEL syntax
• Using dbt commands inside SQL files
• Adding extra keywords like DEFINE

3. Given this dbt model SQL code:

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

What will be the output of this model?

medium

A. A table with each customer_id and their total number of orders.

B. A list of all orders without grouping.

C. An error because COUNT(*) cannot be used with GROUP BY.

D. A table with order_count but no customer_id.

Solution

1. Step 1: Analyze the SQL query

   The query groups orders by customer_id and counts orders per customer.

2. Step 2: Determine the output structure

   The output will have two columns: customer_id and order_count, showing total orders per customer.

3. Final Answer:

   A table with each customer_id and their total number of orders. -> Option A

4. Quick Check:

   GROUP BY customer_id with COUNT(*) = grouped counts [OK]

Hint: GROUP BY + COUNT(*) gives counts per group [OK]

Common Mistakes:

• Thinking COUNT(*) can't be used with GROUP BY
• Expecting ungrouped list
• Missing customer_id in output

4. You wrote this dbt model SQL:

SELECT user_id, SUM(amount) AS total FROM sales

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

medium

A. dbt requires CREATE TABLE statements in models.

B. Missing GROUP BY clause for user_id in aggregation.

C. user_id is not a valid column name.

D. SUM(amount) cannot be used in dbt models.

Solution

1. Step 1: Identify the SQL error

   Using SUM(amount) with user_id requires GROUP BY user_id to aggregate correctly.

2. Step 2: Check options against SQL rules

   Missing GROUP BY clause for user_id in aggregation. correctly points out the missing GROUP BY clause causing the error.

3. Final Answer:

   Missing GROUP BY clause for user_id in aggregation. -> Option B

4. Quick Check:

   Aggregations need GROUP BY for non-aggregated columns [OK]

Hint: Always add GROUP BY for columns outside aggregation [OK]

Common Mistakes:

• Thinking SUM() is disallowed in dbt
• Assuming column names cause error without checking
• Expecting CREATE TABLE in dbt models

5. You want to build a dbt model that calculates the average order value per customer but only for customers with more than 5 orders. Which SQL snippet correctly implements this in dbt?

hard

A. SELECT customer_id, AVG(order_value) AS avg_value FROM orders HAVING COUNT(*) > 5 GROUP BY customer_id

B. SELECT customer_id, AVG(order_value) AS avg_value FROM orders WHERE COUNT(*) > 5 GROUP BY customer_id

C. SELECT customer_id, AVG(order_value) AS avg_value FROM orders GROUP BY customer_id WHERE COUNT(*) > 5

D. SELECT customer_id, AVG(order_value) AS avg_value FROM orders GROUP BY customer_id HAVING COUNT(*) > 5

Solution

1. Step 1: Understand filtering after grouping

   To filter groups by aggregate conditions, use HAVING after GROUP BY.

2. Step 2: Check SQL syntax correctness

   SELECT customer_id, AVG(order_value) AS avg_value FROM orders GROUP BY customer_id HAVING COUNT(*) > 5 correctly places HAVING COUNT(*) > 5 after GROUP BY customer_id.

3. Final Answer:

   SELECT customer_id, AVG(order_value) AS avg_value FROM orders GROUP BY customer_id HAVING COUNT(*) > 5 -> Option D

4. Quick Check:

   HAVING filters groups after GROUP BY = A [OK]

Hint: Use HAVING to filter groups, not WHERE [OK]

Common Mistakes:

• Using WHERE with aggregate functions
• Placing HAVING before GROUP BY
• Confusing order of clauses