Become Google Certified with updated Professional-Data-Engineer exam questions and correct answers
Your company maintains a hybrid deployment with GCP, where analytics are performed on your anonymized customer data. The data are imported to Cloud Storage from your data center through parallel uploads to a data transfer server running on GCP. Management informs you that the daily transfers take too long and have asked you to fix the problem. You want to maximize transfer speeds. Which action should you take?
Which methods can be used to reduce the number of rows processed by BigQuery?
You have a data processing application that runs on Google Kubernetes Engine (GKE). Containers need to be
launched with their latest available configurations from a container registry. Your GKE nodes need to have
GPUs. local SSDs, and 8 Gbps bandwidth. You want to efficiently provision the data processing infrastructure
and manage the deployment process. What should you do?
A data analyst currently has the bigquery.dataViewer role and can successfully query a materialized view. They also want to be able to refresh the materialized view. You want to use a predefined role but not grant them any more permissions than needed to refresh the materialized view. What predefined role would you grant to the user?
You want to schedule a number of sequential load and transformation jobs Data files will be added to a Cloud
Storage bucket by an upstream process There is no fixed schedule for when the new data arrives Next, a
Dataproc job is triggered to perform some transformations and write the data to BigQuery. You then need to
run additional transformation jobs in BigQuery The transformation jobs are different for every table These
jobs might take hours to complete You need to determine the most efficient and maintainable workflow to
process hundreds of tables and provide the freshest data to your end users. What should you do?
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