Google Cloud Digital Leader Certification Notes
Key Links:
Certification Details: Cloud Digital Leader | Google Cloud
Exam Guide: Google Cloud Digital Leader Certification Exam | Google Cloud
Pathways:
Coursera: Google Cloud Digital Leader Training Professional Certificate | Coursera
Pluralsight:Google Cloud Digital Leader Training Path | Pluralsight
Cloud Skills Boost: Cloud Digital Leader Learning Path | Google Cloud Skills Boost
Practice Tests:
Exampro : https://app.exampro.co/student/journey/cdl
Exam Topics : https://www.examtopics.com/exams/google/cloud-digital-leader/
Whizlabs: https://www.whizlabs.com/google-cloud-certified-cloud-digital-leader/
Google: https://cloud.google.com/certification/cloud-digital-leader
Google Cloud Platform Fundamentals: Core Infrastructure — Coursera Quiz Answers | Quizerry
Google Cloud-Digital-Leader today updated questions — Verified by Google Experts (certensure.com)
Quizlet Study Sets:
GCP Cloud Digital Leader Flashcards | Quizlet
Google Cloud — CDL Flashcards | Quizlet
Cloud Digital Leader Flashcards | Quizlet
Digital cloud leader, Cloud fundamentals Flashcards | Quizlet
Cert Prep: Cloud Digital Leader Flashcards | Quizlet
GCP -Cloud Digital Leader Flashcards | Quizlet
Certification Notes:
1. Introduction to digital transformation with Cloud (1.5 hours)
- When an organization takes advantage of new technologies such as cloud to redesign and redefine relationships with their customers, employees, and partners the result is a companywide digital transformation.
- Abandoning old technology for a new one is commonly referred to as the “burning platform” effect. It requires organizations to take a leap of faith and to continually adapt as new technologies create new paradigm shifts
- Computing is the ability to process information and automate tasks most often done by a computer program. Compute power, refers to the speed at which a computer is able to process data
- The first disruption comes from processors that are specifically meant for this type of application, and which we call TPUs, for Tensorflow Processing Units.
They are 50 times more powerful than traditional chips
- The second disruption comes from quantum computing, which is a hundred million times more powerful.
- Modernization Focus Areas: Infrastructure, Business Platforms, Applications
- Google Cloud Solution Pillers: Infrastructure Modernization, Business Applications Platform Portfolio, Application Modernization, Database and Storage Solutions, Smart Analytics, Artificial Intelligence, Security
- Six Core Tenants: Talent, Environment, Structure, Strategy, Empowerment, Innovation
- Innovation Mindset: Three simple rules for innovation
- Focus on the customer/user
Access : Fast, Easy Always on
Engagement: Up to date, reliable
Customization: Needs and preferences
Communication: Feedback with two way
- Think 10X and generate big ideas
- Launch and iterate
2. Innovating with Data in google cloud (1.5 hours)
Value of Data and Data Analytics
Data — Any useful information which will support business to scale — Capturing and realizing value in digital transformation
Inside organization: Financial data, logistics, production output, reports
External information (customer & industry data): Industry benchmark reports, customer data
Limitations vs What GCP offers:
Processing volumes: Economics of scale
Consume, store and process terabytes of data in real-time, and run queries — that is, requests to retrieve and use data, instantly.
Cost-effective solution & Variety of data: Resources are distributed to ensure no data loss without extra overhead to business. Data can be combined, analyzed, and served to business teams quickly and cost-effectively.
Dataset Category:
User Data
Corporate Data
Industry Data
Data Types:
Structured Data (Tables, Views, Index, Constraints, OLTP, OLAP) — Organized data
Unstructured Data: Businesses can extract value from unstructured data by using an Application Programming Interface to create structure. E.g., Bloomberg has reporters from different regions reporting in multiple languages, Google API is used to translate the information so it can be published in a language the customer wants.
Using the Right cloud tool, we can extract value from unstructured data. Speed and Elasticity are the advantages of storing and managing data in the public cloud.
Database in Cloud: Data integrity and scalability are top two priorities in data management.
Data integrity is achieved by implementing a set of rules when a database is first designed and through ongoing error checking and validation.
Scalability is achieved using auto-scaling options available in cloud hosting options.
Cloud SQL: Fully managed relational database management service, or RDBMS
Cloud Spanner: It’s another fully managed database service, and it’s designed for global scale. With Cloud Spanner, data is automatically and instantly copied across regions
Data warehouse: Assemble data from multiple sources including databases, built to enable rapid analysis of large and multi-dimensional datasets.
1. Different types of data can be transformed and consolidated into
the warehouse so that they are useful for analysis.
2. Data warehouse allows businesses to consolidate data that is
structured and semi-structured.
3. Data warehouses can transform unstructured data into semi-structured data that can be used for analysis.
4. Data warehouse providers link storage and computing together, so customers
are charged for compute capacity whether they are running a query or not.
BigQuery & Dataflow:
- Big Query is serverless (compute power is automatically provisioned behind the scenes as needed to run your queries)
- Big Query is a fully managed Datawarehouse with downtime-free upgrades and maintenance and seamless scaling
- Big Query allows you to analyze petabytes of data using fast speeds and zero operational overheads
- Pub-Sub is a service for the real-time ingestion of data
- Data flow is a service for the large-scale processing of data
- Pub Sub and Data flow can work together to bring unstructured data into the cloud and transform it into semi-structured data
- This transformed data can be sent directly from Data flow to Big Query for analysis
Data Lakes:
Data lakes are a repository for raw data and tend to serve many purposes. They also hold data that is historic and not relevant to day-to-day business operations
Cloud Storage:
Cloud Storage offers multi-regional storage. It’s ideal for serving content to users worldwide.
Regional storage offered by Cloud Storage is ideal when an organization wants to use the data locally; it gives added throughput and performance by storing data in the same region.
Storage Classes
- Near line Storage: Data that is accessed at least once a month
- Cold line Storage: Data that is accessed at least once a quarter
- Archive Storage: Data that is accessed at least once a year
Looker:
Looker is a Google Cloud business intelligence solution.
It’s a data platform that sits on top of any analytics database and makes it simple to describe your data and define business metrics.
Innovation with Machine Learning
AI: It’s a broad field or term that describes any kind of machine capable of acting autonomously.
ML:It’s a branch in the field of AI. Computers that can “learn” from data without using a complex set of rules
Accuracy of decision is based on data cleanliness. Erronous data will leads to bugs and inaccurate result.
Incomplete data can limit the performance of the ML model
Qualities of Good Data: It has coverage, is clean and is complete
Google Cloud AI Platform is a unified, simply managed platform that makes machine learning easy to adopt by analysts and developers. It provides modern ML services, with the ability to generate tailored models and use pre-trained models
The AI Hub is a hosted repository of plug-and-play AI components, including end-to-end AI pipelines and out-of-the-box algorithms.
3. Infrastructure & App modernization (1.5 hours)
Modernizing IT Infrastructure with Google Cloud:
Central to an organization’s ability to thrive in the new era is the way in which they structure and use its IT resources.
One way to achieve is to move away from investing resources to run and maintain existing IT infrastructure.
Using cloud technology to truly transform a business and increase organization’s productivity and innovation mindset.
Hardware is often heavily under utilized, organizations are preferring “Pay for what you use” instead of “Fixed capacity” model.
Virtual machines: Optimize the same pool of computer processing, storage, and networking resources. Enables businesses to have multiple applications running at the same time on a server.
Hypervisor. A hypervisor sits on top of physical hardware, and multiple VMs are built on top of it. It’s like having multiple computers that only use one piece of hardware.
Containerization: hold exactly what’s needed for the particular application that they support. They start faster, use less memory, and allow developers to create predictable environments
Serverless computing(Function as a service):compute power, are automatically provisioned behind-the-scenes as needed. Businesses do not pay for compute power unless they are actually running a query or application.
Cloud Types
- Private Cloud — own data centers to create its own private on-premises environment.
- Hybrid Cloud — using a combination of on-premises or private cloud infrastructure and public cloud services
- Multi-Cloud — using multiple public cloud providers as part of its architecture
Modernizing Applications with Google Cloud
Invent in greenfield: build innovative applications that will help drive the business forward
Invent in brownfield: Invent a new application in the cloud environment that will replace an existing legacy application
Handling existing Monolith applications:
Updating existing applications that have been built on-premises with a monolithic architecture can be difficult. When an application is updated, the entire application needs to be deployed and tested, even if the change is only small
Solution is to design with Microservices architecture by separating a large application into small, loosely coupled services.
Adopting CI/CD:
Implementation of Sustainable Continuous integration and deployment process help you increase your application release velocity and reliability. You can test and roll out changes incrementally
The Value of APIs
4. Google Cloud & Security Operations (1.5 hours)
Financial Governance in the Cloud
Cost management changes with cloud
- Managing cloud costs requires vigilance and real-time monitoring in parallel.
- Budgeting is no longer a one-time operational process completed annually. Because of the variable nature of cloud resources and their costs, spending must be monitored and controlled on an ongoing basis
- Often, the accessibility that makes cloud services attractive leads to reduced control and significant overspending.
- Finance teams control cloud costs, but may struggle to understand or to keep up with cloud spend on a daily, weekly, or monthly basis
- Solution to the problem can be planned through
People: Cloud Center of Excellence
Process: Process factoring cost measure against business strategy and asking questions on which cloud resources used by whom and when
Technology: Utilizing tools available within GCP to take the right decision to reduce the risk of overspending, and get intelligent recommendations to optimize costs and usage.
Total Cost of Ownership:
- TCO (Total cost of ownership) vary based on cloud adoption strategy on-premises/single cloud/multi-cloud/ Hybrid.
- Spending substantial amount of money upfront is Capital Expenditure which is applicable for on-prem.
- Using public cloud services, much of their capital expenditure shifts toward a pay-as-you-go OpEx model.
The goals of the cost management
Visibility : Built-in reporting tools, Custom dashboards, Pricing calculator
Accountability: Can be done by defining clear ownership for projects and sharing cost views with the departments and teams that are using cloud resources.
Control: Only authorized individuals in an organization should have the power to deploy cloud resources. Creating budgets and alerts to notify key stakeholders when spending is getting off track is an important practice to keep costs under control.
Intelligence: Help optimize usage, save time on management, and minimize costs. The recommendations can easily be applied for immediate cost savings and greater efficiency.
Security in the Cloud
Fundamentals:
Privacy: data an organization or an individual has access to and who they can share that data with.
Security: the policies, procedures and controls put in place to keep data safe
Compliance: meeting standards set by a third party.
Availability: how much time the cloud service provider guarantees data and services will be running or accessible
Identity and Access Management:
WHO-> Google account, a Google group, a service account, or a Google Workspace or Cloud Identity domain.
Can do What –> This is the Role
- Primitive (Owner, Editor Viewer)
- Predefined (Granular Roles, set of predefined roles that aligned with typical responsibilities of people using those services)
- Custom (More Granular, “least-privilege” model)
Resource Hierarchy: Resource hierarchy refers to the way your IT team can organize your business’ Google Cloud environment and how that service structure maps to your organization’s actual structure.
Monitoring Cloud IT Services and Operations
Service level agreement (SLA)
- SLA is a contractual commitment between the cloud service provider and the customer
- SLA provides the baseline level for the quality, availability, and reliability of the service
- If the baseline service is not met by the customer, then the end user gets affected. And in this case, the cloud provider would incur a cost usually paid out to the customer.
Service level objective (SLO)
- SLO is the goal for the cloud service performance level shared between cloud provider and customer.
- If the Service performance meets or exceeds the SLO, it means the customers, end users and internal stakeholders are happy.
Service level indicator (SLI)
- SLI is a measure of the Service provided and SLI often include reliability and latency
Error budget is typically the space between the SLA and the SLO
Five objectives of DevOps:
- Reduce silos — Shared ownership between Dev & Ops
- Accept failure as normal. — Holding a blameless “lessons learned” discussion after an incident occurs.
- Implement gradual change — Canary release, Feature toggle
- Leverage tooling and automation -Reduces the amount of manual, repetitive work
- Measure everything.
Google Cloud operational tools:
Terminologies: (https://storage.googleapis.com/gweb-cloudblog-publish/images/DarkPoster-lowres.max-2200x2200.jpg)
- Hypervisor: The software or firmware that create a Virtual Machine
- Google Cloud Console: A web-based unified console to interact with Google Cloud Services
- Cloud SDK: a collection of software development tools in one installable package.
- Cloud CLI: A command line interface to process commands to a computer program in the form of lines of text
- Cloud Shell: is a free online environment, with command-line access and code editor.
- Organization: The root node in your resource hierarchy, represents your company.
- Folders: A logical groups of projects or other folders
- Projects: A logical grouping of resources, resources must belong to a project
- Global Infrastructure: the global presence of datacenters, networking and cloud resources available to the customer
- Regions: Independent geographic areas that consist of zones.
- Zones: a physical location made up of one or more datacenter.
You should strive to always run workloads across 3 Zones to be Highly Available (HA)
- Data Residency: physical or geographic location of where an organization or cloud resources reside
- Cloud Interconnect: Provides direct physical connections between your on-premises network and Google’s network
- Dedicated: Data transfer through a co-location facility (carrier hotel) speeds between 10 to 200 Gbps
- Partner: Data transfer through a trusted third party data center speeds between 50 Mbps to 10 Gbps
- Compute Engine: Launch a Virtual Machine, Choose your OS, and Compute Type (combination of vCPUs and Memory)
- App Engine: Platform as a Service, deploy a web-application without having to worry about the underlying infrastructure
- Google Kubernetes Engine (GKE): deploying containers for microservice workloads
- Bare Metal: when you need to install your own hypervisor or have the most control of your compute for security and performance
- Sole-tenant Nodes: Dedicated virtual machines (a single customer to a physical machine), not a multi-tenant
- Cloud Functions: Functions as as Service (FaaS). Upload single-purpose functions of code. Serverless compute
- Container Registry: a repository (storage) for container images
- Artifact Registry: replacement for Container Registry, same feature set and more
- Cloud Build: a service that runs containers design to build artifacts, container images or assets, aka Build Server
- Container-Optimized-OS: an option for Compute Engine to enable container mode to run docker containers
- Preemptible VM: Google Compute Engine (GCE) virtual machine (VM) instance that can be purchased for a steep discount as long as the customer accepts that the instance will terminate after 24 hours.
- BigQuery: Serverless Datawarehouse. Store terabytes or petabytes of data using a NoSQL wide-column database service. Built in ML!
- Cloud BigTable: Fully managed NoSQL databases for large analytic and
operational workloads.
- Firestore: a NoSQL document database access to mobile and web apps.
- Cloud Spanner: A proprietary relational database designed for scale. Uses SQL
- Cloud SQL: MySQL, PostgreSQL, and SQL Server database services
- Memorystore: Achieve extreme performance using a managed in-memory data store service.
- Database Migration Service (DMS): easy, minimal downtime migrations to Cloud SQL
- Cloud Storage: is a serverless object storage service.
- Standard Storage: Best for short-term storage and frequently accessed data
- Nearline Storage: Best for backups and data accessed less than once a
month
- Coldline Storage: Best for disaster recovery and data accessed less than
once a quarter
- Archive Storage: Best for long-term digital preservation of data accessed less than once a year
- Filestore: fully managed, high-performance NFS file servers on Google Cloud.
- Persistent Disk: block storage, attaching a virtual disk to a Virtual Machine
- Virtual Private Cloud (VPC): is a logically isolated section of the Google Cloud
- Subnets: a logical partition of an IP network into multiple smaller network segments.
- Cloud Armor: Help protect your services against DoS and web attacks.
- Cloud CDN: Cache your content close to your users using Google’s global network.
- Cloud Load Balancing: Scale and distribute app access with high-performance load balancing.
- Cloud DNS: Publish and manage your domain names using Google’s reliable, resilient, low-latency DNS serving.
- Cloud VPN: Securely extend your on-premises network to Google’s network through an IPsec VPN tunnel.
- Private Google Cloud: allows your instances to reach Google APIs and services using an internal IP address rather than a public IP address
- Dataproc: Perform batch processing, querying, and streaming using a managed Apache Spark and Hadoop service.
- Dataflow: Develop real-time batch and stream data processing pipelines. (Apache Beam)
- Cloud Data Fusion: Quickly build and manage data pipelines using fully managed, code-free data integration with a graphical interface
- Anthos: allow you to deploy and maintain compute on GCP, on-premise and other cloud service providers.
- Migrate for Anthos: Migrate VMs from on-premises or other clouds directly into containers in GKE.
- Migrate for Compute Engine: Migrate VMs to Compute Engine
- Cloud Deployment Manager: the process of managing and provisioning cloud services through machine-readable definition eg. Infrastructure as Code (IaC)
- Firebase: Google’s fully-managed platform for rapidly developing and deploying web and mobile applications.
- Cloud Storage Transfer Service: Transfer data between cloud storage services such as AWS S3 and Cloud Storage.
- Transfer Appliance: Ship large volumes of data to Google Cloud using trackable storage
- Google Cloud Directory: Sync enables administrators to synchronize users, groups and other data from an Active Directory/LDAP service to their Google Cloud domain directory.
- Cloud Trace : Google Cloud solution for monitoring application performance. It is a distributed tracing system that helps developers debug or fix and optimize their code
- Cloud Debugger : Helps monitor application performance. IT teams can inspect the state of a running application in real time, without stopping or slowing it down.
- Google Cloud Logging : Fully managed service that performs at scale and can ingest application and system log data, as well as custom log
- Cloud Monitoring : Foundation for Site Reliability Engineering because it provides visibility into the performance, uptime, and overall health of cloud-powered applications.
