Cost optimization strategies in Snowflake - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the main goal of cost optimization in cloud data platforms like Snowflake?

To reduce unnecessary spending while maintaining performance and scalability for data workloads.

beginner

How does Snowflake's auto-suspend feature help optimize costs?

It automatically pauses compute resources when not in use, so you only pay for active processing time.

intermediate

Why is choosing the right warehouse size important for cost optimization in Snowflake?

Because larger warehouses cost more but run queries faster; choosing the right size balances cost and performance.

intermediate

What role does query optimization play in reducing Snowflake costs?

Efficient queries use fewer compute resources and run faster, lowering the amount of billed compute time.

advanced

How can data clustering improve cost efficiency in Snowflake?

By organizing data to reduce the amount scanned during queries, it lowers compute usage and speeds up processing.

What happens when you enable auto-suspend on a Snowflake warehouse?

A The warehouse runs continuously regardless of usage.

B The warehouse pauses automatically when idle to save costs.

C The warehouse size increases automatically.

D The warehouse deletes old data to save space.

Auto-suspend pauses the warehouse when not in use, so you don't pay for idle compute time.

Which factor is NOT a direct way to reduce Snowflake compute costs?

A Optimizing queries to run faster

B Choosing the smallest warehouse size possible

C Using auto-suspend to pause warehouses

D Increasing data retention time

Increasing data retention affects storage costs, not compute costs directly.

Why might a very large warehouse increase costs unnecessarily?

A It uses more compute credits even if queries are small.

B It stores more data than needed.

C It automatically deletes data.

D It reduces query speed.

Larger warehouses consume more compute credits per time, increasing costs if not needed.

How does clustering data help reduce costs in Snowflake?

A By reducing the data scanned during queries

B By increasing warehouse size automatically

C By deleting unused data

D By suspending warehouses

Clustering organizes data to minimize scanned data, lowering compute usage.

What is a best practice to avoid unexpected Snowflake costs?

A Use the largest warehouse size always

B Keep warehouses running 24/7

C Set auto-suspend and auto-resume on warehouses

D Disable query optimization

Auto-suspend and auto-resume help control compute usage and avoid paying for idle time.

Explain how Snowflake's auto-suspend and auto-resume features help optimize costs.

Think about when you pay for compute resources and how these features control that.

Describe why query and warehouse size optimization are important for cost control in Snowflake.

Consider how faster queries and right-sized warehouses impact billing.