Free Professional-Data-Engineer Questions Attempt

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Question 36

You are migrating your on-premises data warehouse to BigQuery. One of the upstream data sources resides on a MySQL database that runs in your on-premises data center with no public IP addresses. You want to ensure that the data ingestion into BigQuery is done securely and does not go through the public internet. What should you do?

Options:

Update your existing on-premises ETL tool to write to BigQuery by using the BigQuery Open Database Connectivity (ODBC) driver. Set up the proxy parameter in the Simba. googlebigqueryodbc. ini tile to point to your data center's NAT gateway.

Use Datastream to replicate data from your on-premises MySQL database to BigQuery. Gather Datastream public IP addresses of the Google Cloud region that will be used to set up the stream. Add those IP addresses to the firewall allowlist of your on-premises data center.

Use IP Allovlisting as the connectivity method and Server-only as the encryption type when setting up the connection profile in Datastream.

Use Datastream to replicate data from your on-premises MySQL database to BigQuery. Use Forward-SSH tunnel as the connectivity method to establish a secure tunnel between Datastream and your on-premises MySQL database through a tunnel server in your on-premises data center. Use None as the encryption type when setting up the connection profile in Datastream.

Use Datastream to replicate data from your on-premises MySQL database to BigQuery. Set up Cloud Interconnect between your on- premises data center and Google Cloud. Use Private connectivity as the connectivity method and allocate an IP address range within your VPC network to the Datastream connectivity configuration. Use Server-only as the encryption type when setting up the connection profile in Datastream.

Question 37

You are running a pipeline in Cloud Dataflow that receives messages from a Cloud Pub/Sub topic and writes the results to a BigQuery dataset in the EU. Currently, your pipeline is located in europe-west4 and has a maximum of 3 workers, instance type n1-standard-1. You notice that during peak periods, your pipeline is struggling to process records in a timely fashion, when all 3 workers are at maximum CPU utilization. Which two actions can you take to increase performance of your pipeline? (Choose two.)

Options:

Increase the number of max workers

Use a larger instance type for your Cloud Dataflow workers

Change the zone of your Cloud Dataflow pipeline to run in us-central1

Create a temporary table in Cloud Bigtable that will act as a buffer for new data. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Bigtable to BigQuery

Create a temporary table in Cloud Spanner that will act as a buffer for new data. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Spanner to BigQuery

Question 38

You store and analyze your relational data in BigQuery on Google Cloud with all data that resides in US regions. You also have a variety of object stores across Microsoft Azure and Amazon Web Services (AWS), also in US regions. You want to query all your data in BigQuery daily with as little movement of data as possible. What should you do?

Options:

Load files from AWS and Azure to Cloud Storage with Cloud Shell gautil rsync arguments.

Create a Dataflow pipeline to ingest files from Azure and AWS to BigQuery.

Use the BigQuery Omni functionality and BigLake tables to query files in Azure and AWS.

Use BigQuery Data Transfer Service to load files from Azure and AWS into BigQuery.

Question 39

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.)

Options:

Introduce data compression for each file to increase the rate file of file transfer.

Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.

Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.

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.

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.

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