Data Science Model Deployments and Cloud Computing on GCP - Caching and Its Use Cases

The video tutorial explains the importance of caching in data retrieval, especially when dealing with applications deployed on platforms like Google App Engine. It describes how caching can prevent unnecessary data fetching from databases like Bigquery by storing frequently accessed data temporarily. The tutorial covers the workings of caching mechanisms, the types of caching databases such as Redis and Memcache, and the differences between shared and dedicated cache storage in Memcache. It concludes with a brief overview of implementing caching in a Python application.

Why is caching important in data retrieval?

• To reduce the cost of data storage

• To avoid unnecessary querying of unchanged data

• To ensure data is always fetched from the primary source

• To increase the complexity of data systems

What is the primary benefit of using a cache database?

• It permanently stores all data

• It increases data redundancy

• It provides faster data retrieval

• It replaces the primary database

Which of the following is a key-value NoSQL database used for caching?

• Redis

• PostgreSQL

• Oracle

• MySQL

What is a characteristic of a shared cache in Memcache?

• It works on a best effort basis

• It is free of cost

• It provides a performance guarantee

• It is only available on AWS

When should a dedicated cache be used?

• For production-level deployments

• When caching is not critical

• For small-scale applications

• When data changes frequently