Cloud-Digital-Leader Google Cloud Digital Leader exam Question and Answers

Both Devs and SRE team must ensure that the error budget does not become exhausted. To avoid it, releases have to stop for the time being until the error budget resets. The team would have to reprioritize to focus on reliability to get it back to an acceptable state.

Google Cloud commitment to keep the data secure and private:

1. Org owns the data and not Google

2. Google does not sell data to 3rd parties

3. All customer data is encrypted by default

4. Google Cloud guards insider against your data

5. No backdoor access to any govt. entity

6. Google’s privacy practices are audited against international standards

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https://cloud.google.com/anthos

Explanation

In cloud computing, multitenancy means that multiple customers of a cloud vendor are using the same computing resources. Even though they share resources, cloud customers aren’t aware of each other, and their data is kept totally separate. Mul-titenancy is a crucial component of cloud computing; without it, cloud services would be far less practical. Multitenant architecture is a feature in many types of public cloud com-puting, including IaaS, PaaS, SaaS, containers, and serverless computing.

In a shared responsible model, hardware maintence and capacity management cloud provider is the responsible part.

- Because Compute Engine VMs are the preferred compute option as they are long-running.

Cloud Memorystore is an in-memory database that has sub-millisecond latency. This is ideal for caching application data that also changes once in a while.

https://cloud.google.com/memorystore

Because automation is a transformational approach which ensures changes are constant and low-risk.

Spanner, Cloud SQL, BigQuery

Spanner- Cloud Spanner is a fully managed, mission-critical, relational database service that offers transactional consistency at global scale, automatic, synchronous replication for high availability, and support for two SQL  Google Standard SQL  and PostgreSQL.

Cloud SQL- Cloud SQL is a fully-managed database service that helps you set up, maintain, manage, and administer your relational databases on Google Cloud Platform.

BigQuery- Google BigQuery is a cloud-based Architecture and provides exceptional performance as it can auto-scale up and down based on the data load and performs data analysis efficiently. On the other hand, SQL Server is based on client-server architecture and has fixed performance throughout unless the user scales it manually.

Explanation

Database Migration is the right one to use: "Simplifying migrations to Cloud SQL. Now available for MySQL and PostgreSQL migrations, with SQL Server coming soon." Since the customer also doesn't want to manage their own database installations in the future, Cloud SQL is the best option.

https://cloud.google.com/database-migration

VMware Engine helps migrate and run virtual machines in Google Cloud with minimal changes to the VM architecture.

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https://cloud.google.com/learn/what-is-a-virtual-machine

Looker is a business intelligence software and big data analytics platform that helps you explore, analyze and share real-time business analytics easily.

Recommendations AI enables you to build an end-to-end personalized recommendation system based on state-of-the-art deep learning ML models, without a need for expertise in ML or recommendation systems. With Vertex AI, both AutoML training and custom training are available options. Whichever option you choose for training, you can save models, deploy models, and request predictions with Vertex AI.

https://cloud.google.com/vertex-ai

A data lake can store all types of data with no fixed limitation on account size or file and with no specific purpose defined yet. The data comes from disparate sources and can be structured, semi-structured, or even unstructured. Data-lake data can be queried as needed.

https://cloud.google.com/learn/what-is-a-data-lake

A data lake is a centralized repository designed to store, process, and secure large amounts of structured, semistructured, and unstructured data. It can store data in its native format and process any variety of it, ignoring size limits.

Because Dataproc  is used to run Hadoop/Spark workloads

Compute Engine lets you purchase committed use contracts in return for deeply discounted prices for VM usage. These discounts are referred to as committed use discounts. Committed use discounts are ideal for workloads with predictable resource needs. When you purchase a committed use contract, you purchase Compute Engine resources—such as vCPUs, memory, GPUs, local SSDs, and sole-tenant nodes—at a discounted price in return for committing to paying for those resources for 1 year or 3 years. The discount is up to 57% for most resources like machine types or GPUs. The discount is up to 70% for memory-optimized machine types.

The Bare Metal solution is the recommended approach. You can deploy Oracle capabilities like clustered databases, replication, and all performance features at licensing costs that are similar to on-premise systems

https://cloud.google.com/architecture/migrating-bare-metal-workloads

Because  GCP provides three options for paid support which are Standard, Enhanced and Premium. 

Basic Support is included with your Google Cloud subscription which cover only Case, phone, and chat support for billing issues only

Reference link- https://cloud.google.com/support

Cloud Armor provides DDoS protection for applications. It can also "Filter your incoming traffic based on IPv4 and IPv6 addresses or CIDRs. Enforce geography-based access controls to allow or deny traffic based on source geo using Google’s geoIP mapping."

Web Security Scanner identifies security vulnerabilities in your App Engine, Google Kubernetes Engine (GKE), and Compute Engine web applications. It crawls your application, following all links within the scope of your starting URLs, and attempts to exercise as many user inputs and event handlers as possible.

Currently,  Web Security Scanner only supports public URLs and IPs that aren’t behind a firewall. Web Security Scanner currently supports the App Engine standard environment and App Engine flexible environments, Compute Engine instances, and GKE resources.

Reference link- https://cloud.google.com/security-command-center/docs/concepts-web-security-scanner-overview

Google provides a page that brings together everything needed around support. Its called the Support Hub

Reference link- https://cloud.google.com/support-hub

The question is about a dynamic way to provision VM, it can be achieved by a Deployment manager or by using terraform. MIG is creating multiple machines based on templates by load balancing

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

Enable Services and APIs

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Reference link- https://cloud.google.com/storage/docs/projects

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/

Firebase Cloud Messaging:

Firebase Cloud Messaging (FCM) is a cross-platform messaging solution that lets you reliably send messages at no cost.

Using FCM, you can notify a client app that new email or other data is available to sync. You can send notification messages to drive user re-engagement and retention. For use cases such as instant messaging, a message can transfer a payload of up to 4000 bytes to a client app.

Key capabilities of Firebase Cloud Messaging:

Send notification messages or data messages: Send notification messages that are displayed to your user. Or send data messages and determine completely what happens in your application code.

Versatile message targeting: Distribute messages to your client app in any of 3 ways—to single devices, to groups of devices, or to devices subscribed to topics.

Send messages from client apps: Send acknowledgments, chats, and other messages from devices back to your server over FCM’s reliable and battery-efficient connection channel.

Containers and Kubernetes are ideal for the kind of requirement mentioned here - separate microservices that need to scale independently.

Google Kubernetes Engine (GKE) provides a managed environment for deploying, managing, and scaling your containerized applications using Google infrastructure. The GKE environment consists of multiple machines (specifically, Compute Engine instances) grouped together to form a cluster.

Reference link- https://cloud.google.com/kubernetes-engine/docs/concepts/kubernetes-engine-overview

Customer-managed encryption keys for Cloud BigTable.

By default, all the data at rest in Cloud Bigtable is encrypted using Google's default encryption. Bigtable handles and manages this encryption for you without any additional action on your part.

If you have specific compliance or regulatory requirements related to the keys that protect your data, you can use customer-managed encryption keys (CMEK) for BigTable. Instead of Google managing the encryption keys that protect your data, your BigTable instance is protected using a key that you control and manage in Cloud Key Management Service (Cloud KMS).

Features

Security: CMEK provides the same level of security as Google's default encryption but provides more administrative control.

Data access control: Administrators can rotate, manage access to, and disable or destroy the key used to protect data at rest in BigTable .

Auditability: All actions on your CMEK keys are logged and viewable in Cloud Logging.

Comparable performance: BigTable CMEK-protected instances offer comparable performance to BigTable instances that use Google default encryption.

Flexibility: You can use the same CMEK key in multiple projects or instances or you can use separate keys, depending on your business needs.

Compute Engine provides machine type recommendations to help you optimize the resource utilization of your virtual machine (VM) instances. These recommendations are generated automatically based on system metrics gathered by the Cloud Monitoring service over the previous 8 days. Use these recommendations to resize your instance’s machine type to use the instance’s resources more efficiently. This feature is also known as rightsizing recommendations

Reference link- https://cloud.google.com/compute/docs/instances/apply-machine-type-recommendations-for-instances