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

2. Suppose you have a table that includes a nested column called "city" inside a column called "person", but when you try to submit the following query in BigQuery, it gives you an error.
SELECT person FROM `project1.example.table1` WHERE city = "London"
How would you correct the error?

A) Add ", UNNEST(person)" before the WHERE clause.
B) Change "person" to "city.person".
C) Change "person" to "person.city".
D) Add ", UNNEST(city)" before the WHERE clause.

3. All Google Cloud Bigtable client requests go through a front-end server they are sent to a Cloud Bigtable node.

A) after
B) only if
C) before
D) once

4. You are implementing several batch jobs that must be executed on a schedule. These jobs have many interdependent steps that must be executed in a specific order. Portions of the jobs involve executing shell scripts, running Hadoop jobs, and running queries in BigQuery. The jobs are expected to run for many minutes up to several hours. If the steps fail, they must be retried a fixed number of times. Which service should you use to manage the execution of these jobs?

A) Cloud Functions
B) Cloud Scheduler
C) Cloud Composer
D) Cloud Dataflow

5. You are planning to migrate your current on-premises Apache Hadoop deployment to the cloud. You need to ensure that the deployment is as fault-tolerant and cost-effective as possible for long-running batch jobs. You want to use a managed service. What should you do?

A) Use an SSD persistent disk and 50% preemptible worker
B) Use a standard persistent disk and 50% preemptible worker
C) Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
D) Change references in scripts from hdfs:// to gs://
E) Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
F) Install Hadoop and Spark on a 10-node Compute Engine instance group with standard instance
G) Deploy a Cloud Dataproc cluste
H) Install the Cloud Storage connector, and store the data in Cloud Storag
I) Install Hadoop and Spark on a 10-node Compute Engine instance group with preemptible instances.Store data in HDF
J) Change references in scripts from hdfs:// to gs://
K) Deploy a Cloud Dataproc cluste

Solutions: