Why optimization reduces warehouse costs in dbt - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to select all columns from the sales table.

dbt

SELECT [1] FROM sales

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

Aall

B*

Ccolumns

Deverything

Attempts:

3 left

💡 Hint

Common Mistakes

Using words like 'all' or 'columns' instead of '*'

Leaving the SELECT statement incomplete

The asterisk (*) selects all columns in SQL queries.

2fill in blank

medium

Complete the code to filter sales where amount is greater than 100.

dbt

SELECT * FROM sales WHERE amount [1] 100

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

A>

B=

C<

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>'

Using '=' which checks for equality

The '>' operator filters rows where amount is greater than 100.

3fill in blank

hard

Fix the error in the code to calculate total sales per product.

dbt

SELECT product_id, SUM([1]) FROM sales GROUP BY product_id

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

Aquantity

Bproduct_id

Ctotal

Dprice

Attempts:

3 left

💡 Hint

Common Mistakes

Summing product_id which is an identifier

Summing price instead of quantity

SUM should be applied to the quantity column to get total sales count.

4fill in blank

hard

Fill both blanks to create a dictionary of product sales where sales are above 50.

dbt

sales_dict = { [1]: [2] for [1] in products if sales[[1]] > 50 }

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

Aproduct

Bsales

Cproduct_id

Dquantity

Attempts:

3 left

💡 Hint

Common Mistakes

Using quantity as key instead of product_id

Using product as key but not matching loop variable

Use product_id as key and sales as value for products with sales over 50.

5fill in blank

hard

Fill all three blanks to create a filtered dictionary of products with quantity above 10.

dbt

filtered = { [1]: [2] for [1] in inventory if inventory[[1]] [3] 10 }

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

Aitem

Binventory

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' in filter

Mismatching key and loop variable names

Use item as key, inventory as value, and filter where inventory is greater than 10.

Practice

1. Why does optimizing SQL models in dbt help reduce warehouse costs?

easy

A. Because it reduces the amount of data scanned and processed

B. Because it increases the number of queries run

C. Because it stores more data for future use

D. Because it makes the warehouse hardware faster

Solution

1. Step 1: Understand data scanning impact on cost

   Warehouse costs often depend on how much data is scanned during queries.

2. Step 2: Connect optimization to data scanned

   Optimizing SQL models reduces unnecessary data scanned, lowering query time and cost.

3. Final Answer:

   Because it reduces the amount of data scanned and processed -> Option A

4. Quick Check:

   Less data scanned = lower cost [OK]

Hint: Less data scanned means lower cost [OK]

Common Mistakes:

• Thinking more queries reduce cost
• Assuming storing more data saves money
• Believing hardware speed affects cost directly

2. Which of the following is the correct way to write a dbt model that selects only necessary columns to optimize cost?

easy

A. SELECT * FROM raw_data

B. SELECT id, name FROM raw_data

C. SELECT COUNT(*) FROM raw_data

D. SELECT id, name, age, address, phone FROM raw_data

Solution

1. Step 1: Identify selecting necessary columns

   Choosing only needed columns reduces data scanned and processed.

2. Step 2: Compare options for column selection

   SELECT id, name FROM raw_data selects only 'id' and 'name', fewer columns than options selecting all columns or more columns.

3. Final Answer:

   SELECT id, name FROM raw_data -> Option B

4. Quick Check:

   Select only needed columns [OK]

Hint: Select only needed columns, not * [OK]

Common Mistakes:

• Using SELECT * which scans all columns
• Selecting unnecessary columns increases cost
• Confusing COUNT(*) with column selection

3. Given this dbt model SQL code:

SELECT user_id, SUM(amount) AS total
FROM sales
WHERE sale_date > '2024-01-01'
GROUP BY user_id

What is the main reason this query helps reduce warehouse costs compared to selecting all sales data without filtering?

medium

A. It runs the query multiple times to optimize

B. It increases the number of columns scanned

C. It stores the data permanently in the warehouse

D. It reduces the number of rows processed by filtering by date

Solution

1. Step 1: Analyze the WHERE clause effect

   The WHERE clause filters sales after 2024-01-01, reducing rows processed.

2. Step 2: Understand impact on warehouse cost

   Fewer rows scanned means less compute and storage cost during query execution.

3. Final Answer:

   It reduces the number of rows processed by filtering by date -> Option D

4. Quick Check:

   Filtering rows reduces cost [OK]

Hint: Filter rows early to reduce data scanned [OK]

Common Mistakes:

• Thinking more columns scanned reduces cost
• Confusing storage with query cost
• Assuming multiple runs optimize cost

4. This dbt model code is intended to optimize cost by filtering data, but it has an error:

SELECT user_id, amount
FROM sales
WHERE sale_date = '2024-01-01'

What is the issue that could cause higher warehouse costs?

medium

A. Using '=' instead of '>=' filters only one day, missing other data

B. Missing GROUP BY clause causes syntax error

C. Selecting user_id and amount scans all columns

D. No error, this is optimized

Solution

1. Step 1: Check the WHERE clause condition

   Using '=' filters only sales on exactly 2024-01-01, missing other recent data.

2. Step 2: Understand cost impact of missing filter range

   Missing broader date range means more queries or processing later, increasing cost.

3. Final Answer:

   Using '=' instead of '>=' filters only one day, missing other data -> Option A

4. Quick Check:

   Use correct date filters to optimize cost [OK]

Hint: Use range filters (>=) not exact (=) for dates [OK]

Common Mistakes:

• Thinking missing GROUP BY causes error here
• Assuming selecting two columns scans all columns
• Believing no error exists

5. You want to optimize a dbt model to reduce warehouse costs by minimizing data scanned. Which approach is best?

hard

A. Run the model multiple times to cache results

B. Select all columns and join all tables to get complete data

C. Select only needed columns, filter rows early, and avoid unnecessary joins

D. Store all raw data in the model without filtering

Solution

1. Step 1: Identify best practices to reduce data scanned

   Selecting only needed columns and filtering rows early reduces data scanned.

2. Step 2: Avoid unnecessary joins to reduce complexity and data size

   Unneeded joins increase data scanned and processing time, raising costs.

3. Final Answer:

   Select only needed columns, filter rows early, and avoid unnecessary joins -> Option C

4. Quick Check:

   Minimize data scanned by filtering and selecting [OK]

Hint: Filter early, select less, avoid extra joins [OK]

Common Mistakes:

• Selecting all columns wastes resources
• Joining all tables increases cost
• Relying on multiple runs instead of optimization