Google freetechexam full Access Professional-data-engineer Tutorials by Antonio q268 vce pdf

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Exam Name:	Google Professional Data Engineer Exam
Exam Code:	Professional-Data-Engineer Dumps
Vendor:	Google	Certification:	Google Cloud Certified
Questions:	330 Q&A's	Shared By:	antonio

Question 8

You created an analytics environment on Google Cloud so that your data scientist team can explore data without impacting the on-premises Apache Hadoop solution. The data in the on-premises Hadoop Distributed File System (HDFS) cluster is in Optimized Row Columnar (ORC) formatted files with multiple columns of Hive partitioning. The data scientist team needs to be able to explore the data in a similar way as they used the on-premises HDFS cluster with SQL on the Hive query engine. You need to choose the most cost-effective storage and processing solution. What should you do?

Options:

Import the ORC files lo Bigtable tables for the data scientist team.

Import the ORC files to BigOuery tables for the data scientist team.

Copy the ORC files on Cloud Storage, then deploy a Dataproc cluster for the data scientist team.

Copy the ORC files on Cloud Storage, then create external BigQuery tables for the data scientist team.

Discussion

Question 9

You use BigQuery as your centralized analytics platform. New data is loaded every day, and an ETL pipeline modifies the original data and prepares it for the final users. This ETL pipeline is regularly modified and can generate errors, but sometimes the errors are detected only after 2 weeks. You need to provide a method to recover from these errors, and your backups should be optimized for storage costs. How should you organize your data in BigQuery and store your backups?

Options:

Organize your data in a single table, export, and compress and store the BigQuery data in Cloud Storage.

Organize your data in separate tables for each month, and export, compress, and store the data in Cloud Storage.

Organize your data in separate tables for each month, and duplicate your data on a separate dataset in BigQuery.

Organize your data in separate tables for each month, and use snapshot decorators to restore the table to a time prior to the corruption.

Discussion

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Question 10

You are creating a new pipeline in Google Cloud to stream IoT data from Cloud Pub/Sub through Cloud Dataflow to BigQuery. While previewing the data, you notice that roughly 2% of the data appears to be corrupt. You need to modify the Cloud Dataflow pipeline to filter out this corrupt data. What should you do?

Options:

Add a SideInput that returns a Boolean if the element is corrupt.

Add a ParDo transform in Cloud Dataflow to discard corrupt elements.

Add a Partition transform in Cloud Dataflow to separate valid data from corrupt data.

Add a GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.

Discussion

Question 11

You have a data stored in BigQuery. The data in the BigQuery dataset must be highly available. You need to define a storage, backup, and recovery strategy of this data that minimizes cost. How should you configure the BigQuery table?

Options:

Set the BigQuery dataset to be regional. In the event of an emergency, use a point-in-time snapshot to recover the data.

Set the BigQuery dataset to be regional. Create a scheduled query to make copies of the data to tables suffixed with the time of the backup. In the event of an emergency, use the backup copy of the table.

Set the BigQuery dataset to be multi-regional. In the event of an emergency, use a point-in-time snapshot to recover the data.

Set the BigQuery dataset to be multi-regional. Create a scheduled query to make copies of the data to tables suffixed with the time of the backup. In the event of an emergency, use the backup copy of the table.

Discussion

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