aggregation and analysis is performed in Google Cloud. Significant amounts of sensor data from manufacturing plants are stored in legacy inventory and logistics management applications running in private data centers. Those data centers have multiple network interconnects to GCP.
Business sponsors want to predict and detect vehicle malfunctions and ship replacement parts just in time for repairs. They also want to reduce operational costs, increase development speed, support remote work, and provide custom API services for partners.
An HTTP API access layer for legacy systems will be developed to minimize disruptions when moving those services to the cloud.
Developers will use a modern CI/CD platform as well as a self-service platform for creating new projects.
Cloud-native solutions for key management will be used along with identity-based access management.
Architecture Considerations
For data that is transmitted in real time, Cloud Pub/Sub can be used for ingestion. If there is additional processing to be done on that data, Cloud Dataflow could be used to read the data from a Pub/Sub topic, process the data, and then write the results to persistent storage. BigQuery would be a good option for additional analytics.
The other data that is uploaded in batch may be stored in Cloud Storage where a Cloud Dataflow job could decompress the files, perform any needed processing, and write the data to BigQuery.
BigQuery has the advantages of being a fully managed, petabyte-scale analytical database that supports the creation of machine learning models without the need to export data. Also, the machine learning functionality is available through SQL functions, making it accessible to relational database users who may not be familiar with specialized machine learning tools.
TerramEarth is a good use case for Vertex AI. Assuming much of the sensor data is highly structured, that is, it is not images or videos, then AutoML Tables may be used for developing models. If deep learning models are used, then GPUs and TPUs may be used as well.
For workflows with more complex dependencies, Cloud Composer is a good option since it allows you to define workflows as directed acyclic graphs. Consider an MLOps workflow that includes training a machine learning model using the latest data, using the model to make predictions about data collected in real time, and initiating the shipment of replacement parts when a component failure is predicted. If the model is not successfully trained, then the existing prediction job should not be replaced. Instead, the training job should be executed again with an update to the prediction job to follow only if training is successful. This kind of workflow management is handled automatically in Cloud Composer.
Summary
The Google Cloud Professional Architect exam covers several broad areas, including the following:
Planning a cloud solution
Managing a cloud solution
Securing systems and processes
Complying with government and industry regulations
Understanding technical requirements and business considerations
Maintaining solutions deployed to production, including monitoring
These areas require business as well as technical skills. For example, since architects regularly work with nontechnical colleagues, it is important for architects to understand issues such as reducing operational expenses, accelerating the pace of development, maintaining and reporting on service-level agreements, and assisting with regulatory compliance. In the realm of technical knowledge, architects are expected to understand functional requirements around computing, storage, and networking as well as nonfunctional characteristics of services, such as availability and scalability.
The exam includes case studies, and some exam questions reference the case studies. Questions about the case studies may be business or technical questions.
Exam Essentials
Assume every word matters in case studies and exam questions. Some technical requirements are stated explicitly, but some are implied in business statements. Review the business requirements as carefully as the technical requirements in each case study. Similarly, when reading an exam question, pay attention to all the statements. What may look like extraneous background information at first may turn out to be information that you need to choose between two options.
Study and analyze case studies before taking the exam. Become familiar with the case studies before the exam to save time while taking the text. You don't need to memorize the case studies, as you'll have access to them during the test. Watch for numbers that indicate the scale of the problem. For example, if you need 10 Gbps, then you should consider a Cloud Interconnect solution over a VPN solution, which works up to about 3 Gbps for each VPN tunnel.
Understand what is needed in the near term and what may be needed in the future. For example, we don't have specific MLOps workflows in the TerramEarth case study. Initially, predictions may be based only on structured data, but some vehicles may have cameras to create images of machine components or the operating environment. In the future, there may be an opportunity to use images of operating environments to automatically detect a problem in the environment that could damage the vehicle. In that case, AutoML Vision Edge may be useful for performing image classification in real time. This requirement is not stated, and not even implied, but it is the kind of planning for the future that architects are expected to do.
Understand how to plan a migration. Migrations are high-risk operations. Data can be lost, and services may be unavailable. Know how to plan to run new and old systems in parallel so that you can compare results. Be able to identify lower-risk migration steps so that they can be scheduled first. Plan for incremental migrations.
Know agile software development practices. You won't have to write code for this exam, but you will need to understand continuous integration/continuous delivery and how to maintain development, test, staging, and production environments. Understand what is meant by an infrastructure-as-code service and how that helps accelerate development and deployment.
Keep in mind that solutions may involve non-Google services or applications. Google has many services, but sometimes the best solution involves a third-party solution. For example, Jenkins and Spinnaker are widely used tools to support continuous integration and deployment. Google Cloud has a code repository, but many developers use GitHub. Sometimes businesses are locked into existing solutions, such as a third-party database. The business may want to migrate to another database solution, but the cost may be too high for the foreseeable future.
Review Questions
1 You have been tasked with interviewing line-of-business owners about their needs for a new cloud application. Which of the following do you expect to find?A comprehensive list of defined business and technical requirementsThat their business requirements do not have a one-to-one correlation with technical requirementsBusiness and technical requirements in conflictClear consensus on all requirements
2 You have been asked by stakeholders to suggest ways to reduce operational expenses as part of a cloud migration project. Which of the following would you recommend?Managed services,
