Google Cloud Architect Professional Exam

This option can help create an organization structure that allows developers to create projects, but prevents them from modifying production projects. Folders are containers for projects and other folders within Google Cloud organizations. Folders allow resources to be structured hierarchically and inherit policies from their parent resources. By creating folders under the organization resource named ''Development'' and ''Production'', you can organize your projects by environment and apply different policies to them. By granting all developers the Project Creator IAM role on the ''Development'' folder, you can allow them to create projects under that folder, but not under the ''Production'' folder. By moving the developer projects into the ''Development'' folder, you can ensure that they are subject to the policies set on that folder. By setting the policies for all projects on the organization, you can manage policies centrally and efficiently. By additionally setting the production policies on the ''Production'' folder, you can enforce more restrictive policies for production projects and prevent developers from modifying them. The other options are not optimal for this scenario, because they either create a second Google Workspace account and organization, which increases complexity and cost (A), or do not use folders to organize projects by environment, which makes it harder to manage policies and permissions (B, D). Reference:

https://cloud.google.com/resource-manager/docs/creating-managing-folders

https://cloud.google.com/architecture/framework/system-design

The practice for managing logs generated on Compute Engine on Google Cloud is to install the Cloud Logging agent and send them to Cloud Logging.

The sent logs will be aggregated into a Cloud Logging sink and exported to Cloud Storage.

The reason for using Cloud Storage as the destination for the logs is that the requirement in question requires setting up a lifecycle based on the storage period.

In this case, the log will be used for active queries for 30 days after it is saved, but after that, it needs to be stored for a longer period of time for auditing purposes.

If the data is to be used for active queries, we can use BigQuery's Cloud Storage data query feature and move the data past 30 days to Coldline to build a cost-optimal solution.

Therefore, the correct answer is as follows

1. Install the Cloud Logging agent on all instances.

Create a sync that exports the logs to the region's Cloud Storage bucket.

3. Create an Object Lifecycle rule to move the files to the Coldline Cloud Storage bucket after one month. 4.

4. set up a bucket-level retention policy using bucket locking.'

The Data Transfer appliance is a Google-provided hardware device that can be used to transfer large amounts of data from on-premises environments to Cloud Storage. It is suitable for scenarios where the bandwidth between the on-premises environment and Google Cloud is low or insufficient, and the data size is large. The Data Transfer appliance can minimize the time it takes to complete the migration, the overall cost and database load, by avoiding network bottlenecks and reducing bandwidth consumption. The Data Transfer appliance also encrypts the data at rest and in transit, ensuring data security and privacy. The other options are not optimal for this scenario, because they either require a high-bandwidth network connection (B, C, D), or incur additional costs and complexity (B, C). Reference:

https://cloud.google.com/data-transfer-appliance/docs/overview

https://cloud.google.com/blog/products/storage-data-transfer/introducing-storage-transfer-service-for-on-premises-data

The practice for managing logs generated on Compute Engine on Google Cloud is to install the Cloud Logging agent and send them to Cloud Logging.

The sent logs will be aggregated into a Cloud Logging sink and exported to Cloud Storage.

The reason for using Cloud Storage as the destination for the logs is that the requirement in question requires setting up a lifecycle based on the storage period.

In this case, the log will be used for active queries for 30 days after it is saved, but after that, it needs to be stored for a longer period of time for auditing purposes.

If the data is to be used for active queries, we can use BigQuery's Cloud Storage data query feature and move the data past 30 days to Coldline to build a cost-optimal solution.

Therefore, the correct answer is as follows

1. Install the Cloud Logging agent on all instances.

Create a sync that exports the logs to the region's Cloud Storage bucket.

3. Create an Object Lifecycle rule to move the files to the Coldline Cloud Storage bucket after one month. 4.

4. set up a bucket-level retention policy using bucket locking.'

When an application becomes slow, the first step you should take is to gather information about the underlying cause of the problem. One way to do this is by inspecting the logs and metrics from the instances where the application is deployed. Google Cloud Platform (GCP) provides tools such as Cloud Logging and Cloud Monitoring that can help you to collect and analyze this information. By reviewing the logs and metrics from the instances, you may be able to identify issues such as resource shortages (e.g. CPU, memory, or disk), network problems, or application errors that are causing the performance issues. Once you have identified the underlying cause of the problem, you can take steps to resolve it.