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Google Professional-Data-Engineer Exam Dumps - Actual Questions Answers

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  • Last Update: Jul 19, 2024
  • Questions: 330 questions with Expert Explanation
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Professional-Data-Engineer Questions and Answers

Question # 1

You are collecting loT sensor data from millions of devices across the world and storing the data in BigQuery. Your access pattern is based on recent data tittered by location_id and device_version with the following query:


You want to optimize your queries for cost and performance. How should you structure your data?


Partition table data by create_date, location_id and device_version


Partition table data by create_date cluster table data by tocation_id and device_version


Cluster table data by create_date location_id and device_version


Cluster table data by create_date, partition by location and device_version

Question # 2

You are designing a data warehouse in BigQuery to analyze sales data for a telecommunication service provider. You need to create a data model for customers, products, and subscriptions All customers, products, and subscriptions can be updated monthly, but you must maintain a historical record of all data. You plan to use the visualization layer for current and historical reporting. You need to ensure that the data model is simple, easy-to-use. and cost-effective. What should you do?


Create a normalized model with tables for each entity. Use snapshots before updates to track historical data


Create a normalized model with tables for each entity. Keep all input files in a Cloud Storage bucket to track historical data


Create a denormalized model with nested and repeated fields Update the table and use snapshots to track historical data


Create a denormalized, append-only model with nested and repeated fields Use the ingestion timestamp to track historical data.

Question # 3

You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally. You also want to optimize data for range queries on nonkey columns. What should you do?


Use Cloud SQL for storage. Add secondary indexes to support query patterns.


Use Cloud SQL for storage. Use Cloud Dataflow to transform data to support query patterns.


Use Cloud Spanner for storage. Add secondary indexes to support query patterns.


Use Cloud Spanner for storage. Use Cloud Dataflow to transform data to support query patterns.

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