Free Google Professional-Data-Engineer Exam Dumps Questions & Answers

You are building a data pipeline on Google Cloud. You need to prepare data using a casual method for a machine-learning process. You want to support a logistic regression model. You also need to monitor and adjust for null values, which must remain real-valued and cannot be removed. What should you do?

• A.  Use Cloud Dataflow to find null values in sample source data. Convert all nulls to 0 using a custom script.
• B.  Use Cloud Dataprep to find null values in sample source data. Convert all nulls to 0 using a Cloud Dataprep job.
• C.  Use Cloud Dataprep to find null values in sample source data. Convert all nulls to `none' using a Cloud Dataproc job.
• D.  Use Cloud Dataflow to find null values in sample source data. Convert all nulls to `none' using a Cloud Dataprep job.

How can you get a neural network to learn about relationships between categories in a categorical feature?

• A.  Create a hash bucket
• B.  Create a one-hot column
• C.  Create a multi-hot column
• D.  Create an embedding column

You are designing a Dataflow pipeline for a batch processing job. You want to mitigate multiple zonal failures at job submission time. What should you do?

• A.  Create an Eventarc trigger to resubmit the job in case of zonal failure when submitting the job.
• B.  Set the pipeline staging location as a regional Cloud Storage bucket.
• C.  Submit duplicate pipelines in two different zones by using the --zone flag.
• D.  Specify a worker region by using the --region flag.

You are creating a data model in BigQuery that will hold retail transaction data. Your two largest tables, sales_transaction_header and sales_transaction_line, have a tightly coupled immutable relationship. These tables are rarely modified after load and are frequently joined when queried.
You need to model the sales_transaction_header and sales_transaction_line tables to improve the performance of data analytics queries. What should you do?

• A.  Create a sales_transaction table that stores the sales_transaction_header and sales_transaction_line data as a JSON data type.
• B.  Create separate sales_transaction_header and sales_transaction_line tables and, when querying, specify the sales_transaction_line first in the WHERE clause.
• C.  Create a sales_transaction table that holds the sales_transaction_header information as rows and the sales_transaction_line rows as nested and repeated fields.
• D.  Create a sales_transaction table that holds the sales_transaction_header and sales_transaction_line information as rows, duplicating the sales_transaction_header data for each line.

Your company produces 20,000 files every hour. Each data file is formatted as a comma separated values (CSV) file that is less than 4 KB. All files must be ingested on Google Cloud Platform before they can be processed. Your company site has a 200 ms latency to Google Cloud, and your Internet connection bandwidth is limited as 50 Mbps. You currently deploy a secure FTP (SFTP) server on a virtual machine in Google Compute Engine as the data ingestion point. A local SFTP client runs on a dedicated machine to transmit the CSV files as is. The goal is to make reports with data from the previous day available to the executives by 10:00 a.m. each day. This design is barely able to keep up with the current volume, even though the bandwidth utilization is rather low. You are told that due to seasonality, your company expects the number of files to double for the next three months. Which two actions should you take? (choose two.)

• A.  Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.
• B.  Introduce data compression for each file to increase the rate file of file transfer.
• C.  Create an S3-compatible storage endpoint in your network, and use Google Cloud Storage Transfer Service to transfer on-premices data to the designated storage bucket.
• D.  Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.
• E.  Assemble 1,000 files into a tape archive (TAR) file. Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.