Why Snowflake separates compute from storage - Challenge Your Understanding

Challenge - 5 Problems

🧠 Conceptual

intermediate

Why does Snowflake separate compute from storage?

Snowflake separates compute resources from storage. What is the main benefit of this design?

A It limits the number of users who can access data at the same time.

B It forces users to pay for compute and storage together, simplifying billing.

C It requires all data to be copied between compute and storage before queries run.

D It allows independent scaling of compute and storage, so you can increase one without affecting the other.

💡 Hint

Think about how flexibility in resource use can save money and improve performance.

Separating compute from storage lets Snowflake scale each independently. This means you can add more compute power for faster queries without increasing storage costs, or add storage without paying for extra compute.

❓ service_behavior

intermediate

What happens when multiple users query Snowflake simultaneously?

Snowflake separates compute from storage. How does this affect simultaneous queries by many users?

A All queries share the same compute cluster, causing delays when many users run queries.

B Queries are queued and run one at a time to avoid conflicts.

C Each user query runs on its own compute cluster without waiting, improving concurrency.

D Users must manually switch between compute clusters to run queries.

💡 Hint

Consider how separating compute allows multiple compute clusters to run independently.

Because compute is separate, Snowflake can create multiple compute clusters (virtual warehouses). Each cluster handles queries independently, so many users can run queries at the same time without waiting.

❓ Architecture

advanced

How does Snowflake's separation of compute and storage improve fault tolerance?

Snowflake separates compute from storage. Which statement best explains how this design improves fault tolerance?

A If a compute cluster fails, storage remains intact and other clusters can continue processing queries.

B If storage fails, compute clusters automatically recreate data from cache.

C Compute and storage failures always cause the entire system to stop.

D Compute clusters store copies of data locally to avoid storage failures.

💡 Hint

Think about what happens if one part of the system stops working.

Since compute and storage are separate, a failure in compute does not affect stored data. Other compute clusters can continue running queries using the intact storage layer, improving system reliability.

❓ security

advanced

How does separating compute from storage enhance data security in Snowflake?

Snowflake separates compute and storage. Which security advantage does this separation provide?

A It allows strict access controls on storage independently from compute, reducing risk of unauthorized data access.

B It requires users to have full access to both compute and storage to run queries.

C It stores all data unencrypted to speed up compute performance.

D It merges compute and storage permissions, simplifying but weakening security.

💡 Hint

Consider how controlling access separately can improve security.

Separating compute and storage lets Snowflake apply different security policies to each. Storage can be tightly controlled and encrypted, while compute can be managed separately, reducing risk of unauthorized data exposure.

✅ Best Practice

expert

What is the best practice for cost optimization using Snowflake's separated compute and storage?

Given Snowflake's separation of compute and storage, which approach best optimizes costs?

A Always use the largest compute cluster to minimize query time regardless of workload.

B Scale compute clusters up or down based on query load while keeping storage size steady.

C Increase storage size to improve query speed instead of scaling compute.

D Keep compute clusters running 24/7 to avoid startup delays, regardless of usage.

💡 Hint

Think about paying only for what you use when you need it.

Because compute and storage are separate, you can save money by adjusting compute resources to match workload demands while keeping storage constant. This avoids paying for unused compute power.