Free Google Associate Cloud Engineer Exam Dumps

Object lifecycle management is a feature of Cloud Storage that allows you to automatically manage the storage class and retention of your objects. You can use object lifecycle management to reduce the cost of storing your data by transitioning objects to lower-cost storage classes, such as Archive Storage, which is designed for long-term storage of data that is rarely accessed. You can also use object lifecycle management to delete objects that are no longer needed after a certain period of time. To use object lifecycle management, you need to create a lifecycle configuration, which is a set of rules that specify the actions to take on objects that match certain conditions. You can apply a lifecycle configuration to a storage bucket, and it will affect all objects in that bucket.

In this scenario, you want to automatically save costs on files that are no longer accessed by the application after 30 days. Therefore, you should create an object lifecycle on the storage bucket to change the storage class to Archive Storage for objects with an age over 30 days. This way, you can keep the files in the bucket, but pay less for storing them. Archive Storage has the lowest cost per GB among all storage classes, but also has the highest retrieval and early deletion fees. Therefore, it is suitable for data that is rarely accessed and has a long retention period.

The other options are not correct because they either do not reduce the cost of storage, or they delete the files that you may still need. Option A is not correct because enabling object versioning on the storage bucket and adding lifecycle rules to expire non-current versions after 30 days will only affect the old versions of the files, not the current ones. Therefore, you will still pay the same amount for storing the current files in the Standard Storage class. Option C is not correct because creating a retention policy on the storage bucket of 30 days, and locking the bucket by using a retention policy lock will prevent you from deleting or modifying any object in the bucket for 30 days. This will not reduce the cost of storage, and it will also limit your flexibility to manage your data. Option D is not correct because creating a cron job in Cloud Scheduler to call a Cloud Functions instance every day to delete files older than 30 days will permanently remove the files from the bucket. This may not be what you want, as you may still need to access the files for backup, audit, or compliance purposes.

Object Lifecycle Management

Storage Classes

Archive Storage

Cloud Spanner, Pub/Sub, and BigQuery are Google-recommended solutions for migrating your on-premises data management solutions to Google Cloud. They provide global scalability and require minimal operational and infrastructure management. Here is why:

Cloud Spanner is a fully managed, relational database service that offers strong consistency, high availability, and horizontal scalability across regions and continents. It is compatible with MySQL and can handle high-throughput OLTP workloads. It is a good choice for migrating your main database from MySQL to Google Cloud, as it can support your application's performance, availability, and growth requirements.

Pub/Sub is a fully managed, real-time messaging service that allows you to send and receive messages between independent applications. It is compatible with Apache Kafka and can handle high-volume event streaming and data ingestion. It is a good choice for migrating your event streaming platform from Kafka to Google Cloud, as it can support your application's reliability, scalability, and flexibility requirements.

BigQuery is a fully managed, serverless data warehouse that offers fast SQL queries, high scalability, and low cost. It is compatible with Cloud SQL for PostgreSQL and can handle complex analytical and reporting needs. It is a good choice for migrating your analytical and reporting database from Cloud SQL for PostgreSQL to Google Cloud, as it can support your application's speed, efficiency, and innovation requirements.

The other options are not correct because they either do not provide the required scalability, performance, or functionality. Option A is not correct because Cloud SQL is a regional, managed database service that does not offer the same level of scalability, availability, and consistency as Cloud Spanner. Option C is not correct because Memorystore is a regional, managed in-memory data store that does not offer the same level of functionality, durability, and integration as Pub/Sub. Option D is not correct because Cloud SQL is a regional, managed database service that does not offer the same level of scalability, performance, and cost-effectiveness as BigQuery.

Migrating from MySQL to Cloud Spanner

Migrating from Apache Kafka to Cloud Pub/Sub

Migrating from Cloud SQL for PostgreSQL to BigQuery

Cloud Run is a fully managed platform that allows you to deploy and run containerized applications without worrying about the underlying infrastructure. Cloud Run automatically scales your application based on the incoming requests, and you only pay for the resources you use. Cloud Run also supports deploying multiple revisions of your service, and splitting the customer traffic between them. This allows you to gradually roll out new features, test them in production, and monitor their performance and reliability. If you encounter any problems with the new revision, you can easily roll back to a previous revision by adjusting the traffic split. This is a Google-recommended practice for managing revisions to a service, as it minimizes the risk associated with a new revision, reduces the number of customers who might be affected by an outage, and does not introduce any development or operational costs to your customers.

The other options are not correct because they either do not follow the Google-recommended practice, or they do not reduce the risk or cost associated with a new revision. Option B is not correct because deploying your application to a second Cloud Run service, and asking your customers to use the second Cloud Run service, is not a gradual roll out, but a complete switch. This may cause confusion and inconvenience for your customers, and it does not allow you to monitor the performance and reliability of the new revision in comparison with the old one. Option C is not correct because asking your customers to retry access to your service with exponential backoff to mitigate any potential problems after the new revision is deployed, is not a good user experience, and it does not address the root cause of the problems. Option D is not correct because sending all customer traffic to the new revision, and rolling back to a previous revision if you witness any problems in production, is a risky and costly strategy, as it exposes all your customers to the potential problems, and it does not allow you to test the new revision before deploying it to all customers.

Cloud Run documentation

Managing revisions

Splitting traffic

The Google Cloud Pricing Calculator is a tool that allows you to estimate the cost of using Google Cloud products and services based on your specific needs and preferences. You can add various Google Cloud resources to your estimate, such as Compute Engine instances, Cloud SQL instances, network services, storage options, and more. You can also adjust the parameters of each resource, such as the region, zone, machine type, disk size, network egress, and usage hours. The Google Cloud Pricing Calculator will show you the monthly and hourly cost of each resource, as well as the total cost of your estimate. You can also save, share, or export your estimate for future reference.

To create an estimated total cost of cloud infrastructure to run your three-tier web application on Google Cloud instances and Cloud SQL, you should use the Google Cloud Pricing Calculator to determine the cost of every Google Cloud resource you expect to use. You should use similar size instances for the web server, such as n1-standard-1 or n1-standard-2, depending on your application's CPU and memory requirements. You should also use your current on-premises machines as a comparison for Cloud SQL, such as MySQL 5.7 or MySQL 8.0, depending on your database version. You should also consider the network and storage costs associated with your application, such as the network egress, persistent disk, and Cloud Storage.

The other options are not correct because they either do not use the Google Cloud Pricing Calculator, or they do not provide a reliable or accurate estimate of the cloud infrastructure cost. Option B is not correct because creating a Google spreadsheet with multiple Google Cloud resource combinations is a manual and tedious process that may not reflect the current Google Cloud prices or discounts. Option C is not correct because implementing a similar architecture on Google Cloud, and running a reasonable load test on a smaller scale, is a time-consuming and costly process that may not represent the actual load or performance of your application. Option D is not correct because using the Google Cloud Pricing Calculator and selecting the Cloud Operations template will not define your web application with as much detail as possible, as the Cloud Operations template is designed for estimating the cost of monitoring and logging services, not the compute, database, network, and storage services.

Google Cloud Pricing Calculator

Compute Engine pricing

Cloud SQL pricing

https://console.cloud.google.com/marketplace/details/gc-launcher-for-mongodb-atlas/mongodb-atlas