Create an egress rule with priority 1000 to deny all traffic for all instances. Create another egress rule with priority 100 to allow the Active Directory traffic for all instances.

Create an egress rule with priority 100 to deny all traffic for all instances. Create another egress rule with priority 1000 to allow the Active Directory traffic for all instances.

Create an egress rule with priority 1000 to allow the Active Directory traffic. Rely on the implied deny egress rule with priority 100 to block all traffic for all instances.

Create an egress rule with priority 100 to allow the Active Directory traffic. Rely on the implied deny egress rule with priority 1000 to block all traffic for all instances.

Export Cloud Machine Learning Engine performance metrics from Stackdriver to BigQuery, to be used to analyze the efficiency of the model.

Build a roadmap to move the machine learning model training from Cloud GPUs to Cloud TPUs, which offer better results.

Monitor Compute Engine announcements for availability of newer CPU architectures, and deploy the model to them as soon as they are available for additional performance.

Save a history of recommendations and results of the recommendations in BigQuery, to be used as training data. 

Use the Horizontal Pod Autoscaler and enable cluster autoscaling. Use an Ingress resource to load-balance the HTTPS traffic.

Use the Horizontal Pod Autoscaler and enable cluster autoscaling on the Kubernetes cluster. Use a Service resource of type LoadBalancer to load-balance the HTTPS traffic.

Enable autoscaling on the Compute Engine instance group. Use an Ingress resource to load-balance the HTTPS traffic.

Enable autoscaling on the Compute Engine instance group. Use a Service resource of type LoadBalancer to load-balance the HTTPS traffic.

Create a cross-region load balancer with URL Maps.

Create an HTTPS load balancer with URL Maps.

Create appropriate instance groups and instances. Configure SSL proxy load balancing.

Create a global forwarding rule. Configure SSL proxy load balancing.

Use gRPC instead of HTTP for better performance.

Implement retry logic using a truncated exponential backoff strategy.

Make sure the Cloud Storage bucket is multi-regional for geo-redundancy.

Monitor https://status.cloud.google.com/feed.atom and only make requests if Cloud Storage is not reporting an incident.

Use Deployment Manager to automate service provisioning. Use Activity Logs to monitor and debug your tests.

Use Deployment Manager to automate service provisioning. Use Stackdriver to monitor and debug your tests.

Use gcloud scripts to automate service provisioning. Use Activity Logs to monitor and debug your tests.

Use gcloud scripts to automate service provisioning. Use Stackdriver to monitor and debug your tests.

Save the files in a Multi-Regional Cloud Storage bucket.

Save the files in a Regional Cloud Storage bucket, one bucket per zone of the region.

Save the files in multiple Regional Cloud Storage buckets, one bucket per zone per region.

Save the files in multiple Multi-Regional Cloud Storage buckets, one bucket per multi-region. 

Store the data in Google Drive and manually delete records as they expire.

Anonymize the data using the Cloud Data Loss Prevention API and store it indefinitely.

Store the data in Cloud Storage and use lifecycle management to delete files when they expire.

Store the data in Cloud Storage and run a nightly batch script that deletes all expired data.

Set the memcache service level to dedicated. Create a key from the hash of the query, and return database values from memcache before issuing a query to Cloud SQL.

Set the memcache service level to dedicated. Create a cron task that runs every minute to populate the cache with keys containing query results.

Set the memcache service level to shared. Create a cron task that runs every minute to save all expected queries to a key called ג€cached_queriesג€.

Set the memcache service level to shared. Create a key called ג€cached_queriesג€, and return database values from the key before using a query to Cloud SQL.

Using the Cron service provided by App Engine, publish messages directly to a message-processing utility service running on Compute Engine instances.

Using the Cron service provided by App Engine, publish messages to a Cloud Pub/Sub topic. Subscribe to that topic using a message-processing utility service running on Compute Engine instances.

Using the Cron service provided by Google Kubernetes Engine (GKE), publish messages directly to a message-processing utility service running on Compute Engine instances.

Using the Cron service provided by GKE, publish messages to a Cloud Pub/Sub topic. Subscribe to that topic using a message-processing utility service running on Compute Engine instances.