Why optimization controls Snowflake costs - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to select data from a table using the most cost-effective warehouse.

Snowflake

USE WAREHOUSE [1]; SELECT * FROM sales_data WHERE region = 'US';

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ALARGE_WH

BSMALL_WH

CMEDIUM_WH

DXLARGE_WH

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing a larger warehouse unnecessarily increases costs.

Using a smaller warehouse like SMALL_WH reduces compute costs while still processing the query efficiently.

2fill in blank

medium

Complete the code to create a clustered table to optimize query performance and cost.

Snowflake

CREATE TABLE orders_clustered CLUSTER BY ([1]) AS SELECT * FROM orders;

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Acustomer_id

Bproduct_id

Corder_id

Dorder_date

Attempts:

3 left

💡 Hint

Common Mistakes

Clustering by a unique ID like order_id does not improve filtering.

Clustering by order_date helps queries filter by date efficiently, reducing compute time and cost.

3fill in blank

hard

Fix the error in the query that causes unnecessary compute cost by avoiding a full table scan.

Snowflake

SELECT * FROM customers WHERE [1] = 'NY';

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Acustomer_id

Bcustomer_name

Cstate_code

Demail

Attempts:

3 left

💡 Hint

Common Mistakes

Filtering by customer_name or email causes full scans and higher costs.

Filtering by state_code uses the clustered column to reduce scanned data and cost.

4fill in blank

hard

Fill both blanks to create a materialized view that reduces compute costs for frequent queries.

Snowflake

CREATE MATERIALIZED VIEW recent_orders AS SELECT * FROM orders WHERE order_date [1] CURRENT_DATE - INTERVAL '[2]' DAY;

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A>

B<

C7

D30

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' selects older data, not recent, increasing compute costs.

Using order_date > CURRENT_DATE - INTERVAL '30' DAY creates a view of recent orders, speeding up queries and saving costs.

5fill in blank

hard

Fill all three blanks to optimize a query by selecting only necessary columns and filtering efficiently.

Snowflake

SELECT [1], [2] FROM sales WHERE region = '[3]';

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

Aproduct_id

Bsales_amount

CUS

Dcustomer_name

Attempts:

3 left

💡 Hint

Common Mistakes

Selecting customer_name adds unnecessary data increasing cost.

Selecting only product_id and sales_amount for region US reduces data scanned and lowers costs.

Practice

1. Why does optimizing queries in Snowflake help reduce costs?

easy

A. Because it increases the size of the data stored

B. Because it uses fewer computing resources and runs faster

C. Because it requires more warehouses to run

D. Because it disables auto-suspend on warehouses

Solution

1. Step 1: Understand resource usage in Snowflake

   Snowflake charges based on compute resources used, so less compute means lower cost.

2. Step 2: Link query optimization to resource use

   Optimized queries run faster and scan less data, using fewer resources.

3. Final Answer:

   Because it uses fewer computing resources and runs faster -> Option B

4. Quick Check:

   Optimization reduces compute = lower cost [OK]

Hint: Less compute time means lower cost in Snowflake [OK]

Common Mistakes:

• Thinking more data scanned reduces cost
• Believing bigger warehouses always save money
• Ignoring auto-suspend settings

2. Which Snowflake warehouse setting helps save costs by stopping compute when not in use?

easy

A. Manual scaling

B. Auto-resume

C. Auto-suspend

D. Clustering keys

Solution

1. Step 1: Identify warehouse settings related to cost

   Auto-suspend stops the warehouse after inactivity, saving compute time.

2. Step 2: Differentiate from other options

   Auto-resume starts warehouse automatically but does not save cost alone; clustering keys optimize data but don't stop compute.

3. Final Answer:

   Auto-suspend -> Option C

4. Quick Check:

   Auto-suspend stops compute = saves cost [OK]

Hint: Auto-suspend pauses warehouse to save money [OK]

Common Mistakes:

• Confusing auto-resume with auto-suspend
• Thinking clustering keys stop compute
• Assuming manual scaling always saves cost

3. Given a query scanning 1TB of data on a large warehouse, what happens if clustering reduces scanned data to 100GB?

medium

A. Query runs faster and costs less

B. Query runs faster but costs more

C. Query runs slower and costs more

D. Query runs slower but costs less

Solution

1. Step 1: Understand clustering effect on data scanned

   Clustering reduces data scanned by organizing data, so less data is processed.

2. Step 2: Link data scanned to query speed and cost

   Less data scanned means faster query and lower compute cost.

3. Final Answer:

   Query runs faster and costs less -> Option A

4. Quick Check:

   Less data scanned = faster + cheaper query [OK]

Hint: Less data scanned means faster and cheaper queries [OK]

Common Mistakes:

• Assuming clustering increases cost
• Thinking faster queries cost more
• Ignoring data scanned impact

4. A Snowflake user notices high costs despite small warehouses. What is a likely cause?

medium

A. Queries scan large amounts of unnecessary data

B. Warehouses are set to auto-suspend after 1 minute

C. Clustering keys are properly defined

D. Warehouses are paused when idle

Solution

1. Step 1: Analyze cost factors with small warehouses

   Small warehouses reduce compute size but scanning large data still increases cost.

2. Step 2: Identify inefficient query behavior

   Queries scanning unnecessary data waste compute time and increase cost despite warehouse size.

3. Final Answer:

   Queries scan large amounts of unnecessary data -> Option A

4. Quick Check:

   Unoptimized queries cause high cost even on small warehouses [OK]

Hint: Check data scanned, not just warehouse size [OK]

Common Mistakes:

• Assuming auto-suspend causes high cost
• Believing clustering keys increase cost
• Ignoring query data scanned

5. You want to reduce Snowflake costs for a large dataset queried daily. Which combined approach is best?

hard

A. Use large warehouses with auto-resume and no clustering

B. Use manual scaling and avoid query tuning

C. Disable auto-suspend and run queries on medium warehouses

D. Use small warehouses with auto-suspend and optimize queries with clustering

Solution

1. Step 1: Evaluate warehouse size and auto-suspend

   Small warehouses with auto-suspend save cost by limiting compute time and size.

2. Step 2: Consider query optimization and clustering

   Optimizing queries and clustering reduce data scanned, speeding queries and lowering cost.

3. Step 3: Combine for best cost control

   Using small warehouses with auto-suspend plus query optimization and clustering balances cost and performance.

4. Final Answer:

   Use small warehouses with auto-suspend and optimize queries with clustering -> Option D

5. Quick Check:

   Small warehouse + auto-suspend + clustering = cost control [OK]

Hint: Combine small warehouse, auto-suspend, and clustering for savings [OK]

Common Mistakes:

• Choosing large warehouses without auto-suspend
• Ignoring query optimization
• Disabling auto-suspend increases cost