Why advanced patterns solve complex analytics in dbt - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to select all columns from the 'orders' table.

dbt

SELECT [1] FROM orders;

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

Aeverything

Ball

C*

Dcolumns

Attempts:

3 left

💡 Hint

Common Mistakes

Using words like 'all' or 'columns' instead of the symbol *.

Leaving the blank empty.

The asterisk (*) selects all columns from the table.

2fill in blank

medium

Complete the code to filter rows where 'status' equals 'completed'.

dbt

SELECT * FROM orders WHERE status [1] 'completed';

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

A=

B!=

C<>

DLIKE

Attempts:

3 left

💡 Hint

Common Mistakes

Using '!=' or '<>' which mean 'not equal'.

Using LIKE without wildcards.

The equals sign (=) is used to filter rows where the column matches a specific value.

3fill in blank

hard

Fix the error in the code to correctly join 'orders' and 'customers' on 'customer_id'.

dbt

SELECT orders.id, customers.name FROM orders JOIN customers ON orders.[1] = customers.customer_id;

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

Aname

Bid

Corder_id

Dcustomer_id

Attempts:

3 left

💡 Hint

Common Mistakes

Joining on 'id' or 'order_id' which do not link the two tables.

Using 'name' which is not an ID column.

The join should be on the 'customer_id' column in the 'orders' table to match 'customers.customer_id'.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths for words longer than 3 characters.

dbt

{word: [1] for word in words if [2]

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

Alen(word)

Bword > 3

Clen(word) > 3

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'word > 3' which compares a string to a number.

Not using len() to get word length.

The dictionary maps each word to its length using len(word). The condition filters words longer than 3 characters using len(word) > 3.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their values if the value is greater than 0.

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

Attempts:

3 left

💡 Hint

Common Mistakes

Using k instead of k.upper() for keys.

Using '<' or '=' instead of '>' in the condition.

The key is the uppercase version of k using k.upper(). The value is v. The condition filters items where v is greater than 0.

Practice

1. Why do advanced patterns in dbt help solve complex analytics problems?

easy

A. They break down complex tasks into smaller, manageable steps.

B. They make the code longer and harder to read.

C. They avoid using ref() and CTEs.

D. They remove the need for testing data models.

Solution

1. Step 1: Understand the role of advanced patterns

   Advanced patterns help by dividing complex analytics into smaller parts, making them easier to handle.

2. Step 2: Recognize the benefits in dbt

   Using features like ref() and CTEs supports this breakdown, improving clarity and maintainability.

3. Final Answer:

   They break down complex tasks into smaller, manageable steps. -> Option A

4. Quick Check:

   Advanced patterns simplify complexity = C [OK]

Hint: Think: smaller steps make big tasks easier [OK]

Common Mistakes:

• Assuming advanced patterns make code harder
• Ignoring the use of ref() in dbt
• Believing testing is not needed with advanced patterns

2. Which of the following is the correct way to reference another model in dbt using advanced patterns?

easy

A. SELECT * FROM ref('model_name')

B. SELECT * FROM model_name()

C. SELECT * FROM {{ ref('model_name') }}

D. SELECT * FROM {{ model('model_name') }}

Solution

1. Step 1: Recall dbt syntax for referencing models

   In dbt, the correct way to reference another model is using the Jinja function {{ ref('model_name') }}.

2. Step 2: Identify the correct SQL syntax

   The reference must be inside double curly braces and used as a table name in the FROM clause.

3. Final Answer:

   SELECT * FROM {{ ref('model_name') }} -> Option C

4. Quick Check:

   Use {{ ref() }} with curly braces = B [OK]

Hint: Remember: always use {{ ref('model') }} in dbt SQL [OK]

Common Mistakes:

• Omitting curly braces around ref()
• Using parentheses like a function call without {{ }}
• Confusing ref() with model()

3. Given this dbt model SQL code using CTEs, what will be the final output table's column total_sales?

WITH sales AS (
  SELECT customer_id, amount
  FROM {{ ref('raw_sales') }}
),
filtered_sales AS (
  SELECT customer_id, amount
  FROM sales
  WHERE amount > 100
)
SELECT customer_id, SUM(amount) AS total_sales
FROM filtered_sales
GROUP BY customer_id

medium

A. Sum of all sales amounts per customer including amounts <= 100

B. Sum of sales amounts per customer where each amount is greater than 100

C. Count of sales transactions per customer

D. Average sales amount per customer

Solution

1. Step 1: Analyze the filtering condition in filtered_sales CTE

   The CTE filters sales to only include rows where amount > 100.

2. Step 2: Understand the aggregation in the final SELECT

   The final query sums the filtered amounts per customer, so total_sales is sum of amounts > 100 per customer.

3. Final Answer:

   Sum of sales amounts per customer where each amount is greater than 100 -> Option B

4. Quick Check:

   Filter then sum = D [OK]

Hint: Filter first, then aggregate sums [OK]

Common Mistakes:

• Ignoring the WHERE filter on amount
• Confusing sum with count or average
• Assuming all sales are included

4. You wrote this dbt model but it fails with a compilation error. What is the likely cause?

WITH base AS (
  SELECT *
  FROM ref('orders')
)
SELECT * FROM base

medium

A. Missing curly braces around ref function

B. Incorrect CTE name 'base'

C. SELECT * is not allowed in dbt models

D. Missing GROUP BY clause

Solution

1. Step 1: Check the usage of ref() in the CTE

   The ref function must be wrapped in double curly braces like {{ ref('orders') }} to be recognized by dbt.

2. Step 2: Identify the error cause

   Without curly braces, dbt treats ref('orders') as plain text, causing a compilation error.

3. Final Answer:

   Missing curly braces around ref function -> Option A

4. Quick Check:

   Always use {{ ref() }} with curly braces = A [OK]

Hint: Always wrap ref() in {{ }} in dbt SQL [OK]

Common Mistakes:

• Forgetting curly braces around ref()
• Thinking CTE names cause errors
• Assuming SELECT * is invalid in dbt

5. You want to create a dbt model that calculates the average order amount per customer but only for customers with more than 5 orders. Which advanced pattern best helps you write this cleanly and efficiently?

hard

A. Calculate average without filtering orders count

B. Write a single SELECT with multiple JOINs without CTEs

C. Use multiple separate models without referencing each other

D. Use nested CTEs: one to count orders per customer, another to filter, then calculate average

Solution

1. Step 1: Understand the problem requirements

   You need to filter customers by order count > 5, then calculate average order amount.

2. Step 2: Identify the best advanced pattern

   Using nested CTEs breaks the problem into steps: count orders, filter customers, then compute average, making code clear and maintainable.

3. Final Answer:

   Use nested CTEs: one to count orders per customer, another to filter, then calculate average -> Option D

4. Quick Check:

   Break complex tasks into steps with CTEs = A [OK]

Hint: Use nested CTEs to split filtering and aggregation [OK]

Common Mistakes:

• Trying to do all in one complex query
• Not filtering customers before averaging
• Avoiding model references and CTEs