GCP-DE 試験問題を無料オンラインアクセス

You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?

• A.Store the data in Google Cloud Datastor
• B.Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery
• C.Store the data in a file in a regional Google Cloud Storage bucke
• D.Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore
• E.Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.
• F.Load the data every 30 minutes into a new partitioned table in BigQuery.

You are planning to migrate your current on-premises Apache Hadoop deployment to the cloud. You need to ensure that the deployment is as fault-tolerant and cost-effective as possible for long-running batch jobs. You want to use a managed service. What should you do?

• A.Change references in scripts from hdfs:// to gs://
• B.Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
• C.Deploy a Cloud Dataproc cluste
• D.Use an SSD persistent disk and 50% preemptible worker
• E.Deploy a Cloud Dataproc cluste
• F.Install the Cloud Storage connector, and store the data in Cloud Storag
• G.Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
• H.Install Hadoop and Spark on a 10-node Compute Engine instance group with standard instance
• I.Change references in scripts from hdfs:// to gs://
• J.Install Hadoop and Spark on a 10-node Compute Engine instance group with preemptible instances.Store data in HDF
• K.Use a standard persistent disk and 50% preemptible worker

What Dataflow concept determines when a Window's contents should be output based on certain criteria being met?

• A.Triggers
• B.Sessions
• C.Windows
• D.OutputCriteria

You receive data files in CSV format monthly from a third party. You need to cleanse this data, but every third month the schema of the files changes. Your requirements for implementing these transformations include:
Executing the transformations on a schedule
Enabling non-developer analysts to modify transformations
Providing a graphical tool for designing transformations
What should you do?

• A.Merge the transformed tables together with a SQL query
• B.Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformatio
• C.Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard scheme
• D.Use Apache Spark on Cloud Dataproc to infer the schema of the CSV file before creating a Dataframe.Then implement the transformations in Spark SQL before writing the data out to Cloud Storage and loading into BigQuery
• E.The Python code should be stored in a revision control system and modified as the incoming data's schema changes
• F.Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis

For the best possible performance, what is the recommended zone for your Compute Engine instance and Cloud Bigtable instance?

• A.Have the Compute Engine instance in the furthest zone from the Cloud Bigtable instance.
• B.Have both the Compute Engine instance and the Cloud Bigtable instance to be in different zones.
• C.Have the Cloud Bigtable instance to be in the same zone as all of the consumers of your data.
• D.Have both the Compute Engine instance and the Cloud Bigtable instance to be in the same zone.