Cloud-Digital-Leader Exam Dumps - Google Cloud Digital Leader exam

Autoscaling is supported in both Flexible and Standard environments. Flexible Environment does run a Docker container that includes a custom runtime or source code written in other programming languages.

Reference link - https://cloud.google.com/appengine/docs/the-appengine-environments

When you create a virtual machine (VM) instance, you must also create a boot disk for the VM. You can use a public image, a custom image, or a snapshot that was taken from another boot disk. When you create a boot disk, limit the disk size to 2 TB to account for the limitations of MBR partitioning.

Compute Engine automatically creates a boot persistent disk when you create an instance. If you require additional data storage space for your instances, add one or more secondary instance storage options.

You might need to create a standalone boot persistent disk and attach it to an instance later, or resize a boot persistent disk to improve performance and add more space for additional applications or operating system files. That process is described in Add or resize a persistent disk.

As a best practice, do not use regional persistent disks for boot disks. In a failover situation, they do not force-attach to a VM.

After you create a snapshot of a boot persistent disk, you can apply data from that snapshot to new persistent disks. It is only possible to apply data from a snapshot when you first create a persistent disk. You cannot apply a snapshot to an existing persistent disk, or apply a snapshot to persistent disks that belong to a different project than that snapshot.

Cloud Bigtable

A fully managed, scalable NoSQL database service for large analytical and operational workloads with up to 99.999% availability.

- Consistent sub-10ms latency—handle millions of requests per second

- Ideal for use cases such as personalization, ad tech, fintech, digital media, and IoT

- Seamlessly scale to match your storage needs; no downtime during reconfiguration

- Designed with a storage engine for machine learning applications leading to better predictions

- Easily connect to Google Cloud services such as BigQuery or the Apache ecosystem

BigQuery ML allows you to create ML models using standard SQL queries. Those familiar with BigQuery and ML will be able to create ML models with just a basic understanding of machine learning.

https://cloud.google.com/bigquery-ml/docs/

https://newrelic.com/devops/measuring-devops#toc-devops-measurments-for-team-health

Firebase/Firestore is easy to build and is suitable for user information that could bvary in nature.