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Google Data Engineer Sample Questions:

1. 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) Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.
B) Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.
C) Introduce data compression for each file to increase the rate file of file transfer.
D) Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.
E) 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.
F) Assemble 1,000 files into a tape archive (TAR) fil

2. MJTelco is building a custom interface to share dat
a. They have these requirements:
They need to do aggregations over their petabyte-scale datasets.
They need to scan specific time range rows with a very fast response time (milliseconds). Which combination of Google Cloud Platform products should you recommend?

A) BigQuery and Cloud Bigtable
B) Cloud Bigtable and Cloud SQL
C) BigQuery and Cloud Storage
D) Cloud Datastore and Cloud Bigtable

3. You are integrating one of your internal IT applications and Google BigQuery, so users can query BigQuery from the application's interface. You do not want individual users to authenticate to BigQuery and you do not want to give them access to the dataset. You need to securely access BigQuery from your IT application.
What should you do?

A) Create a dummy user and grant dataset access to that use
B) Integrate with a single sign-on (SSO) platform, and pass each user's credentials along with the query request
C) Create a service account and grant dataset access to that accoun
D) Use the service account's private key to access the dataset
E) Create groups for your users and give those groups access to the dataset
F) Store the username and password for that user in a file on the files system, and use those credentials to access the BigQuery dataset

4. The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster .

A) master node
B) worker node
C) conditional node
D) application node

5. You are planning to use Google's Dataflow SDK to analyze customer data such as displayed below. Your project requirement is to extract only the customer name Passing Certification Exams Made Easy visit - https://www.2PassEasy.com from the data source and then write to an output PCollection.
Tom,555 X street Tim,553 Y street Sam, 111 Z street
Which operation is best suited for the above data processing requirement?

A) ParDo
B) Source API
C) Sink API
D) Data extraction

Solutions: