NEW QUESTION 67 You are building a pipeline to process time-series data. Which Google Cloud Platform services should you put in boxes 1,2,3, and 4?
Cloud Pub/Sub, Cloud Dataflow, Cloud Datastore, BigQuery
Firebase Messages, Cloud Pub/Sub, Cloud Spanner, BigQuery
Cloud Pub/Sub, Cloud Storage, BigQuery, Cloud Bigtable
Cloud Pub/Sub, Cloud Dataflow, Cloud Bigtable, BigQuery
Reference: https://cloud.google.com/solutions/correlating-time-series-dataflow https://cloud.google.com/blog/products/data-analytics/handling-duplicate-data-in-streaming-pipeline-using- pubsub-dataflow https://cloud.google.com/bigtable/docs/schema-design-time-series
NEW QUESTION 71 You want to configure 10 Compute Engine instances for availability when maintenance occurs. Your requirements state that these instances should attempt to automatically restart if they crash. Also, the instances should be highly available including during system maintenance. What should you do?
Create an instance template for the instances so VMs have same specs. Set the “‘Automatic Restart’ to on to VM automatically restarts upon crash. Set the “On-host maintenance’ to Migrate VM instance. This will take care of VM during maintenance window. It will migrate VM instance making it highly available Add the instance template to an instance group so instances can be managed. * onHostMaintenance: Determines the behavior when a maintenance event occurs that might cause your instance to reboot. * [Default] MIGRATE, which causes Compute Engine to live migrate an instance when there is a maintenance event. * TERMINATE, which stops an instance instead of migrating it. * automaticRestart: Determines the behavior when an instance crashes or is stopped by the system. * [Default] true, so Compute Engine restarts an instance if the instance crashes or is stopped. * false, so Compute Engine does not restart an instance if the instance crashes or is stopped. Enabling automatic restart ensures that compute engine instances are automatically restarted when they crash. And Enabling Migrate VM Instance enables live migrates i.e. compute instances are migrated during system maintenance and remain running during the migration. Automatic Restart If your instance is set to terminate when there is a maintenance event, or if your instance crashes because of an underlying hardware issue, you can set up Compute Engine to automatically restart the instance by setting the automaticRestart field to true. This setting does not apply if the instance is taken offline through a user action, such as calling sudo shutdown, or during a zone outage. Ref: https://cloud.google.com/compute/docs/instances/setting-instance-scheduling-options#autorestart Enabling the Migrate VM Instance option migrates your instance away from an infrastructure maintenance event, and your instance remains running during the migration. Your instance might experience a short period of decreased performance, although generally, most instances should not notice any difference. This is ideal for instances that require constant uptime and can tolerate a short period of decreased performance. Ref: https://cloud.google.com/compute/docs/instances/setting-instance-scheduling-options#live_migrate
NEW QUESTION 82 You are managing several Google Cloud Platform (GCP) projects and need access to all logs for the past 60 days. You want to be able to explore and quickly analyze the log contents. You want to follow Google- recommended practices to obtain the combined logs for all projects. What should you do?
* Navigate to Stackdriver Logging and select resource.labels.project_id=*. is not right. Log entries are held in Stackdriver Logging for a limited time known as the retention period which is 30 days (default configuration). After that, the entries are deleted. To keep log entries longer, you need to export them outside of Stackdriver Logging by configuring log sinks. Ref: https://cloud.google.com/blog/products/gcp/best-practices-for-working-with-google-cloud-audit-logging * Configure a Cloud Scheduler job to read from Stackdriver and store the logs in BigQuery. Configure the table expiration to 60 days. is not right. While this works, it makes no sense to use Cloud Scheduler job to read from Stackdriver and store the logs in BigQuery when Google provides a feature (export sinks) that does exactly the same thing and works out of the box.Ref: https://cloud.google.com/logging/docs/export/configure_export_v2 * Create a Stackdriver Logging Export with a Sink destination to Cloud Storage. Create a lifecycle rule to delete objects after 60 days. is not right. You can export logs by creating one or more sinks that include a logs query and an export destination. Supported destinations for exported log entries are Cloud Storage, BigQuery, and Pub/Sub.Ref: https://cloud. google.com/logging/docs/export/configure_export_v2 Sinks are limited to exporting log entries from the exact resource in which the sink was created: a Google Cloud project, organization, folder, or billing account. If it makes it easier to exporting from all projects of an organication, you can create an aggregated sink that can export log entries from all the projects, folders, and billing accounts of a Google Cloud organization.Ref: https://cloud.google.com/logging/docs/export /aggregated_sinks Either way, we now have the data in Cloud Storage, but querying logs information from Cloud Storage is harder than Querying information from BigQuery dataset. For this reason, we should prefer Big Query over Cloud Storage. * Create a Stackdriver Logging Export with a Sink destination to a BigQuery dataset. Configure the table expiration to 60 days. is the right answer. You can export logs by creating one or more sinks that include a logs query and an export destination. Supported destinations for exported log entries are Cloud Storage, BigQuery, and Pub/Sub.Ref: https://cloud. google.com/logging/docs/export/configure_export_v2 Sinks are limited to exporting log entries from the exact resource in which the sink was created: a Google Cloud project, organization, folder, or billing account. If it makes it easier to exporting from all projects of an organication, you can create an aggregated sink that can export log entries from all the projects, folders, and billing accounts of a Google Cloud organization.Ref: https://cloud.google.com/logging/docs/export /aggregated_sinks Either way, we now have the data in a BigQuery Dataset. Querying information from a Big Query dataset is easier and quicker than analyzing contents in Cloud Storage bucket. As our requirement is to Quickly analyze the log contents, we should prefer Big Query over Cloud Storage. Also, You can control storage costs and optimize storage usage by setting the default table expiration for newly created tables in a dataset. If you set the property when the dataset is created, any table created in the dataset is deleted after the expiration period. If you set the property after the dataset is created, only new tables are deleted after the expiration period.For example, if you set the default table expiration to 7 days, older data is automatically deleted after 1 week.Ref: https://cloud.google.com/bigquery/docs/best-practices- storage