Dan Sullivan - Google Cloud Certified Professional Cloud Architect Study Guide

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An indispensable guide to the newest version of the Google Certified Professional Cloud Architect certification The newly revised Second Edition of the
delivers a proven and effective roadmap to success on the latest Professional Cloud Architect accreditation exam from Google. You'll learn the skills you need to excel on the test and in the field, with coverage of every exam objective and competency, including focus areas of the latest exam such as Kubernetes, Anthos, and multi-cloud architectures. The book explores the design, analysis, development, operations, and migration components of the job, with intuitively organized lessons that align with the real-world job responsibilities of a Google Cloud professional and with the PCA exam topics. Architects need more than the ability to recall facts about cloud services, they need to be able to reason about design decisions. This study guide is unique in how it helps you learn to think like an architect: understand requirements, assess constraints, choose appropriate architecture patterns, and consider the operational characteristics of the systems you design. Review questions and practice exams use scenario-based questions like those on the certification exam to build the test taking skills you will need.
In addition to comprehensive material on compute resources, storage systems, networks, security, legal and regulatory compliance, reliability design, technical and business processes, and more, you'll get:
The chance to begin or advance your career as an in-demand Google Cloud IT professional Invaluable opportunities to develop and practice the skills you'll need as a Google Cloud Architect Access to the Sybex online learning center, with chapter review questions, full-length practice exams, hundreds of electronic flashcards, and a glossary of key terms The ideal resource for anyone preparing for the Professional Cloud Architect certification from Google,
is also a must-read resource for aspiring and practicing cloud professionals seeking to expand or improve their technical skillset and improve their effectiveness in the field.

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The business requirements do not explicitly call for using managed services, but given the emphasis on predictive analytics, it is likely that Vertex AI and other machine learning services, such as GPUs and TPUs, will be employed.

The business requirements also emphasize the importance of developer productivity, including remote workers.

Business requirements are a starting point for formulating a technical solution. Architects must apply their knowledge of systems design to map business requirements into possible technical requirements. After that, they can dig into explicit technical requirements to start to formulate technical solutions.

The key point of this section is that business requirements are not some kind of unnecessary filler in the case studies. They provide the broad context in which a solution will be developed. While they do not provide enough detail to identify solution components on their own, they do help us narrow the set of feasible technical solutions. Business requirements may help you rule out options to an exam question. For example, a data storage solution that distributes data across multiple regions by default may meet all technical requirements, but if a business requirement indicates the data to be stored must be located in a specific country, then the correct answer is the one that lets you limit where the data is stored.

Application Design and Cost Considerations

In addition to specifying business and product strategy, business requirements may state things that you should consider in application design, such as a preference for managed services and the level of tolerance for disruptions in processing. Implicit in business requirements is the need to minimize costs while meeting business objectives.

One measure of costs is total cost of ownership ( TCO) . TCO is the combination of all expenses related to maintaining a service, which can include the following:

Software licensing costs

Cloud computing costs, including infrastructure and managed services

Cloud storage costs

Data ingress and egress charges

Cost of DevOps personnel to develop and maintain the service

Cost of third-party services used in an application

Charges against missed service-level agreements

Network connectivity charges, such as those for a dedicated connection between an on-premises data center and Google Cloud

While you will want to minimize the TCO, you should be careful not to try to minimize the cost of each component separately. For example, you may be able to reduce the cost of DevOps personnel to develop and maintain a service if you increase your spending on managed services. Also, it is generally a good practice to find a feasible technical solution to a problem before trying to optimize that solution for costs.

Some of the ways to reduce costs while meeting application design requirements include managed services, using preemptible virtual machines, and data lifecycle management. Google also offers sustained uses discounts and reserved VMs, which can help reduce costs.

Managed Services

Managed services are Google Cloud Platform services that do not require users to perform common configuration and maintenance operations. For example, Cloud SQL is a managed relational database service providing MySQL, SQL Server, and PostgreSQL databases. Database administration tasks, such as backing up data and patching operating systems, are performed by Google and not by customers using the service. Managed services are good options in the following cases:

Users do not need low-level control over the resources providing the service, such as choosing the operating system to run in a VM.

Managed services provide a functionality that would be difficult or expensive to implement, such as developing a machine vision application.

There is little competitive advantage to performing resource management tasks. For example, the competitive advantage that may come from using Apache Spark for analytics stems from the algorithms and analysis methodologies, not from the administration of the Spark cluster.

Architects do not necessarily need to know the details of how managed services work. This is one of their advantages. Architects do not need to develop an expertise in natural language processing to use the Natural Language AI, but they do need to understand what kinds of functions managed services provide. See Table 2.1for brief descriptions of some of the Google Cloud Platform managed services.

TABLE 2.1 Examples of Google Cloud Platform managed services

Service Name Service Type Description
AutoML Tables AI and machine learning Machine learning models for structured data
Recommendations AI AI and machine learning Personalized recommendations
Natural Language AI AI and machine learning Entity recognition, sentiment analysis, and language identification
Cloud Translation AI and machine learning Translate between languages
Cloud Vision AI and machine learning Understand contents of images
Dialogflow Essentials AI and machine learning Development suite for voice and text conversational apps
BigQuery Analytics Data warehousing and analytics
Cloud Datalab Analytics Interactive data analysis tool based on Jupyter Notebooks
Dataproc Analytics Managed Hadoop and Spark service
Cloud Data Fusion Data management Data integration and ETL tool
Data Catalog Data management Metadata management service
Dataflow Data management Stream and batch processing
Cloud Spanner Database Global relational database
Cloud SQL Database Regional relational database
Cloud Deployment Manager Development Infrastructure-as-code service
Cloud Pub/Sub Messaging Messaging service
Cloud Composer Orchestration Data workflow orchestration service
Bigtable Storage Wide column, NoSQL database
Cloud Data Transfer Storage Bulk data transfer service
Cloud Memorystore Storage Managed cache service using Redis or memcached
Cloud Storage Storage Managed object storage service

Google Cloud Certified Professional Cloud Architect Study Guide - изображение 4This table is provided to show the breadth of Google Cloud Platform managed services. The services offered change over time. Managed services may be generally available, or they can be in beta. For a list of current services, see the Google Platform Services Summary at cloud.google.com/terms/services.

Reduced Levels of Services

One way to reduce costs is to accept lower levels of service in exchange for lower costs. In GCP there are a number of opportunities to reduce cost in exchange for lower levels of service, including the following:

Using preemptible virtual machines instead of standard virtual machines

Using standard tier networking instead of Premium tier

Using Pub/Sub Lite instead of Pub/Sub

Using Durable Reduced Availability Storage

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