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Professional-Data-Engineer Dumps With Exact Questions and Answers

Exact2pass Provides 100% Valid Google Professional Data Engineer Exam Professional-Data-Engineer Exam dumps Questions and answers which can helps you to Pass Your Certification Exam in First Attempt.

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Professional-Data-Engineer Questions and Answers

Question # 1

You are working on a linear regression model on BigQuery ML to predict a customer's likelihood of purchasing your company's products. Your model uses a city name variable as a key predictive component in order to train and serve the model your data must be organized in columns. You want to prepare your data using the least amount of coding while maintaining the predictable variables. What should you do?


Use SQL in BigQuery to transform the stale column using a one-hot encoding method, and make each city a column with binary values.


Create a new view with BigQuery that does not include a column which city information.


Cloud Data Fusion to assign each city to a region that is labeled as 1, 2 3, 4, or 5, and then use that number to represent the city in the model.


Use TensorFlow to create a categorical variable with a vocabulary list. Create the vocabulary file and upload that as part of your model to BigQuery ML.

Question # 2

You have a requirement to insert minute-resolution data from 50,000 sensors into a BigQuery table. You expect significant growth in data volume and need the data to be available within 1 minute of ingestion for real-time analysis of aggregated trends. What should you do?


Use bq load to load a batch of sensor data every 60 seconds.


Use a Cloud Dataflow pipeline to stream data into the BigQuery table.


Use the INSERT statement to insert a batch of data every 60 seconds.


Use the MERGE statement to apply updates in batch every 60 seconds.

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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