Data Science Model Deployments and Cloud Computing on GCP - Introduction

The video tutorial introduces Google Cloud Functions, a serverless execution environment for building and connecting cloud services. It explains that Cloud Functions allow you to write single-purpose functions that respond to events from cloud infrastructure, eliminating the need for over-provisioned servers. The tutorial highlights the stateless nature and automatic scalability of Cloud Functions, emphasizing their short execution time. It also covers various use cases, including microservices, data processing, and IoT applications. The session concludes with a preview of a lab session to create a Cloud Function in Python.

What is the primary advantage of using Cloud Functions on GCP?

• They are serverless and automatically scale.

• They require manual scaling.

• They store data in memory.

• They are only used for large-scale applications.

How do Cloud Functions differ from traditional server-based applications?

• They are stateful and store data in memory.

• They have longer execution times.

• They are stateless and do not require server maintenance.

• They require more server maintenance.

Which of the following is a characteristic of Cloud Functions?

• They are stateful.

• They have a long execution time.

• They require manual server provisioning.

• They automatically scale without hassle.

In what scenario would you use Cloud Functions for data processing?

• To store large datasets in memory.

• To maintain a persistent connection to a database.

• To apply transformation logic after a file upload.

• To manually scale server resources.

How are Cloud Functions utilized in IoT applications?

• They process real-time logs generated by IoT devices.

• They are used to maintain a constant server connection.

• They store IoT device data in memory.

• They require manual scaling for each IoT device.