Data Science Model Deployments and Cloud Computing on GCP - Lab - Set Up Alerting for Cloud-Run Applications

The video tutorial guides viewers through deploying a Cloud Run application, monitoring its CPU and memory utilization, and creating dashboards and alerts in Google Cloud Platform (GCP). It explains how to set alert policies and test the application by overloading it with requests to observe alert triggers. The tutorial emphasizes understanding metrics and setting up monitoring tools effectively.

What is the primary reason for using the same example across different services in the tutorial?

• To reduce the complexity of the application

• To demonstrate different coding techniques

• To easily generate metrics from the application

• To save time during deployment

What are the two separate metrics discussed for Cloud Run services?

• Data throughput and response time

• Request count and error rate

• CPU utilization and memory utilization

• Network latency and disk usage

How is CPU utilization represented in the metrics?

• As a graphical chart

• As a ratio of used to available CPU

• As a discrete numeric value

• As a percentage of total memory

What is the first step in creating a dashboard in Metrics Explorer?

• Select the metric and scroll down

• Create an alerting policy

• Set up a notification channel

• Deploy the application

Which metric is selected for monitoring in the dashboard example?

• Container CPU allocation

• Startup latency

• Request latency

• Container memory utilization

What is the purpose of setting a low threshold value in the alerting policy demo?

• To reduce memory usage

• To avoid triggering alerts

• To demonstrate the alert in action

• To ensure high performance

What is the notification channel used in the alerting policy example?

• SMS notification

• Push notification

• Email notification

• In-app notification