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:	374 Q&A's	Shared By:	antonio

Question 8

After migrating ETL jobs to run on BigQuery, you need to verify that the output of the migrated jobs is the same as the output of the original. You’ve loaded a table containing the output of the original job and want to compare the contents with output from the migrated job to show that they are identical. The tables do not contain a primary key column that would enable you to join them together for comparison.

What should you do?

Options:

Select random samples from the tables using the RAND() function and compare the samples.

Select random samples from the tables using the HASH() function and compare the samples.

Use a Dataproc cluster and the BigQuery Hadoop connector to read the data from each table and calculate a hash from non-timestamp columns of the table after sorting. Compare the hashes of each table.

Create stratified random samples using the OVER() function and compare equivalent samples from each table.

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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

Question 10

You are on the data governance team and are implementing security requirements to deploy resources. You need to ensure that resources are limited to only the europe-west 3 region You want to follow Google-recommended practices What should you do?

Options:

Deploy resources with Terraform and implement a variable validation rule to ensure that the region is set to the europe-west3 region for all resources.

Set the constraints/gcp. resourceLocations organization policy constraint to in:eu-locations.

Create a Cloud Function to monitor all resources created and automatically destroy the ones created outside the europe-west3 region.

Set the constraints/gcp. resourceLocations organization policy constraint to in: europe-west3-locations.

Discussion

Question 11

You have a query that filters a BigQuery table using a WHERE clause on timestamp and ID columns. By using bq query – -dry_run you learn that the query triggers a full scan of the table, even though the filter on timestamp and ID select a tiny fraction of the overall data. You want to reduce the amount of data scanned by BigQuery with minimal changes to existing SQL queries. What should you do?

Options:

Create a separate table for each ID.

Use the LIMIT keyword to reduce the number of rows returned.

Recreate the table with a partitioning column and clustering column.

Use the bq query - -maximum_bytes_billed flag to restrict the number of bytes billed.

Discussion

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