Here you have the best Google Professional Cloud Architect practice exam questions
Your company has decided to make a major revision of their API in order to create better experiences for their developers. They need to keep the old version of the API available and deployable, while allowing new customers and testers to try out the new API. They want to keep the same SSL and DNS records in place to serve both APIs.
What should they do?
Using separate backend pools for each API path behind the load balancer allows traffic to be directed to the appropriate backend based on the API path. This approach ensures that both the old and new APIs can coexist and be independently deployable while sharing the same SSL and DNS records. It provides isolation and simplifies the management of the two versions, aligning with the requirement to keep the old API available while new customers and testers try out the new API.
Your company plans to migrate a multi-petabyte data set to the cloud. The data set must be available 24hrs a day. Your business analysts have experience only with using a SQL interface.
How should you store the data to optimize it for ease of analysis?
To store and analyze a multi-petabyte dataset while ensuring it is available 24/7 and accessible through a SQL interface, the best choice is Google BigQuery. BigQuery is a serverless, highly scalable data warehouse designed for large-scale data analysis. It supports SQL queries, making it suitable for business analysts familiar with SQL. Additionally, it provides the necessary infrastructure to handle extremely large datasets efficiently, which Google Cloud SQL cannot support due to its storage limitations. Google Cloud Datastore is a NoSQL database, and Google Cloud Storage does not offer a SQL interface, rendering them unsuitable for this use case.
The operations manager asks you for a list of recommended practices that she should consider when migrating a J2EE application to the cloud.
Which three practices should you recommend? (Choose three.)
When migrating a J2EE application to the cloud, it's important to ensure that best practices around monitoring, automation, and continuous integration are followed. Instrumenting the application with a monitoring tool like Stackdriver Debugger is crucial for tracking performance metrics and diagnosing issues. Selecting an automation framework to reliably provision the cloud infrastructure helps maintain consistent environments and reduces manual effort. Implementing a continuous integration tool with automated testing in a staging environment ensures that code changes are tested and integrated efficiently, reducing the potential for defects to reach production.
A news feed web service has the following code running on Google App Engine. During peak load, users report that they can see news articles they already viewed.
What is the most likely cause of this problem?
The most likely cause of the problem is that the session variable is local to just a single instance. In the provided code, the `sessions` variable is a dictionary that stores the viewed news articles for each user. However, this variable is only stored in memory on the instance that handles the request, and it is not shared between instances. During peak traffic, Google App Engine may spin up multiple instances to handle the load, and requests from the same user may be handled by different instances. Since these instances do not share the `sessions` data, users may see news articles they have already viewed when handled by a different instance. To avoid this issue, a shared session management system should be used to store the session data, accessible by all instances, ensuring session consistency across the application.
An application development team believes their current logging tool will not meet their needs for their new cloud-based product. They want a better tool to capture errors and help them analyze their historical log data. You want to help them find a solution that meets their needs.
What should you do?
The team needs a solution that captures errors and helps analyze historical log data for their new cloud-based product. A logical first step is to help them define their requirements clearly, which will ensure that any chosen logging tool meets their needs effectively. Assessing viable logging tools allows for a comprehensive comparison of options and ensures the selected tool provides all necessary features. This approach is more thorough than simply directing them to a specific tool or resource without understanding their specific requirements.