Google Professional-Cloud-Architect Exam With Confidence Using Practice Dumps

Set up a streaming Cloud Dataflow job, receiving data by the ingestion process. Clean the data in a Cloud Dataflow pipeline.

Create a Cloud Function that reads data from BigQuery and cleans it. Trigger it. Trigger the Cloud Function from a Compute Engine instance.

Create a SQL statement on the data in BigQuery, and save it as a view. Run the view daily, and save the result to a new table.

Use Cloud Dataprep and configure the BigQuery tables as the source. Schedule a daily job to clean the data.

Have the vehicle’ computer compress the data in hourly snapshots, and store it in a Google Cloud storage (GCS) Nearline bucket.

Push the telemetry data in Real-time to a streaming dataflow job that compresses the data, and store it in Google BigQuery.

Push the telemetry data in real-time to a streaming dataflow job that compresses the data, and store it in Cloud Bigtable.

Have the vehicle's computer compress the data in hourly snapshots, a Store it in a GCS Coldline bucket.

Use regional managed instance groups and a global load balancer to increase performance because the

regional managed instance group can grow instances in each region separately based on traffic.

Use a global load balancer with a set of virtual machines that forward the requests to a closer group of

virtual machines managed by your operations team.

Use regional managed instance groups and a global load balancer to increase reliability by providing

automatic failover between zones in different regions.

Use a global load balancer with a set of virtual machines that forward the requests to a closer group of

virtual machines as part of a separate managed instance groups.