Data Science Model Deployments and Cloud Computing on GCP - Lab - Implement Caching Mechanism in Python Application - Pa

The video tutorial covers the integration of Flask applications with memcache and BigQuery. It explains how to pass parameters to BigQuery, cache results using memcache, and retrieve data efficiently. The tutorial also touches on the role of Datastore as the underlying database for memcache and provides a brief overview of its functionality. The video concludes with a preview of an upcoming assignment involving memcache.

What is the initial default value set in the Flask application code?

• Count of 1

• Count of 0

• Count of 10

• Count of 5

Why is it important to convert the post ID to a string in the Python runtime?

• Because Python runtime only accepts float data types

• Because Python runtime only accepts integer data types

• Because Python runtime only accepts string data types

• Because Python runtime only accepts boolean data types

What is the main advantage of using memcache in the application?

• It complicates the data retrieval process

• It increases the application size

• It reduces the need to fetch data from BigQuery repeatedly

• It makes the application slower

What is the role of 'kind' in Datastore?

• It sets the speed of data retrieval

• It defines the color of the data

• It determines the size of the data

• It specifies the type of entity being stored

How does memcache utilize Datastore?

• Memcache does not interact with Datastore

• Memcache and Datastore are the same

• Memcache replaces Datastore

• Memcache uses Datastore as its underlying database