You need to reduce the number of unplanned rollbacks of erroneous production deployments in your company's web hosting platform. Improvement to the QA/
Test processes accomplished an 80% reduction.
Which additional two approaches can you take to further reduce the rollbacks? (Choose two.)
To further reduce the number of unplanned rollbacks of erroneous production deployments, introducing a green-blue deployment model and fragmenting the monolithic platform into microservices are effective strategies. A green-blue deployment model allows for a gradual and controlled release of new versions, reducing the risk of deployment failures affecting users. Fragmenting the monolithic platform into microservices isolates different components, minimizing the impact of errors in one part of the system and making deployments more manageable.
To reduce costs, the Director of Engineering has required all developers to move their development infrastructure resources from on-premises virtual machines
(VMs) to Google Cloud Platform. These resources go through multiple start/stop events during the day and require state to persist. You have been asked to design the process of running a development environment in Google Cloud while providing cost visibility to the finance department.
Which two steps should you take? (Choose two.)
To run a development environment in Google Cloud and provide cost visibility, the following steps should be taken: First, use persistent disks with the --no-auto-delete flag. This ensures that the state persists even when VMs are stopped and avoids data loss by not automatically deleting the disks when the instance is stopped. Second, use Google BigQuery billing export and labels to associate costs to groups. This allows the finance department to have visibility into cost allocation and analysis by associating resource consumption with specific labels, enabling better cost management and accountability.
Your company wants to track whether someone is present in a meeting room reserved for a scheduled meeting. There are 1000 meeting rooms across 5 offices on 3 continents. Each room is equipped with a motion sensor that reports its status every second. The data from the motion detector includes only a sensor ID and several different discrete items of information. Analysts will use this data, together with information about account owners and office locations.
Which database type should you use?
Given the scenario where there are 1000 meeting rooms equipped with motion sensors reporting their status every second, the most appropriate database type would be one that can handle massive volumes of rapidly changing, potentially unstructured data. A NoSQL database fits this requirement well. NoSQL databases are designed to manage large amounts of data and can be scaled horizontally across many servers, which is crucial for handling data from multiple sources in real-time. Additionally, NoSQL databases are flexible, allowing the storage of various types of data without a fixed schema, making it easier to adapt to different kinds of sensor data. Other options, such as flat files, relational databases, and blobstores, do not offer the same level of scalability, flexibility, and ease of handling such large, rapidly evolving datasets.
You set up an autoscaling instance group to serve web traffic for an upcoming launch. After configuring the instance group as a backend service to an HTTP(S) load balancer, you notice that virtual machine (VM) instances are being terminated and re-launched every minute. The instances do not have a public IP address.
You have verified the appropriate web response is coming from each instance using the curl command. You want to ensure the backend is configured correctly.
What should you do?
If virtual machine instances are being terminated and re-launched every minute, it suggests that the instances are failing their health checks. Ensuring that a firewall rule exists to allow load balancer health checks to reach the instances in the instance group would address this issue, as it would allow the load balancer to communicate with the instances to verify their health. When these health checks fail, the instances are often terminated and recreated by the autoscaling group to maintain a healthy service. Therefore, configuring the firewall to permit these health checks is crucial for the stable operation of the instances.
You write a Python script to connect to Google BigQuery from a Google Compute Engine virtual machine. The script is printing errors that it cannot connect to
BigQuery.
What should you do to fix the script?
To fix the script, create a new service account with BigQuery access and execute your script using that user. Service accounts are used for server-to-server interactions, such as those between a virtual machine and BigQuery, and provide a way to authenticate applications to Google Cloud services. By assigning the appropriate BigQuery access role to the service account and using that account to run your script, the script will be able to connect to BigQuery and access the data it needs.