Free Google Professional-Data-Engineer Exam Questions

Question 1

You have several Spark jobs that run on a Cloud Dataproc cluster on a schedule. Some of the jobs run in sequence, and some of the jobs run concurrently. You need to automate this process. What should you do?

A : eate a Cloud Dataproc Workflow Template

B : eate an initialization action to execute the jobs

C : eate a Directed Acyclic Graph in Cloud Composer

D : eate a Bash script that uses the Cloud SDK to create a cluster, execute jobs, and then tear down the cluster

Answer: C

Question 2

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

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

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

D : emble 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.

E : eate 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.

Answer: A

Question 3

Analysts are using Cloud Data Studio for analyzing data sets. They would like to improve the performance of the time required to update tables and charts when working with the data. What would you recommend they try to improve performance?

A : Use an imported data source

B : Use a blended data source

C : Use a live data source

D : Use an extracted data source

Answer: D

Question 4

You want to archive data in Cloud Storage. Because some data is very sensitive, you want to use the “Trust No One” (TNO) approach to encrypt your data to prevent the cloud provider staff from decrypting your data. What should you do?

A : gcloud kms keys create to create a symmetric key. Then use gcloud kms encrypt to encrypt each archival file with the key and unique additional authenticated data (AAD). Use gsutil cp to upload each encrypted file to the Cloud Storage bucket, and keep the AAD outside of Google Cloud.

B : gcloud kms keys create to create a symmetric key. Then use gcloud kms encrypt to encrypt each archival file with the key. Use gsutil cp to upload each encrypted file to the Cloud Storage bucket. Manually destroy the key previously used for encryption, and rotate the key once and rotate the key once.

C : ecify customer-supplied encryption key (CSEK) in the .boto configuration file. Use gsutil cp to upload each archival file to the Cloud Storage bucket. Save the CSEK in Cloud Memorystore as permanent storage of the secret.

D : ecify customer-supplied encryption key (CSEK) in the .boto configuration file. Use gsutil cp to upload each archival file to the Cloud Storage bucket. Save the CSEK in a different project that only the security team can access.

Answer: B

Question 5

An aerospace company uses a proprietary data format to store its night dat

a. You need to connect this new data source to BigQuery and stream the data into BigQuery. You want to efficiency import the data into BigQuery where consuming as few resources as possible. What should you do?

A : a standard Dataflow pipeline to store the raw data in BigQuery and then transform the format later when the data is used.

B : ite a shell script that triggers a Cloud Function that performs periodic ETL batch jobs on the new data source

C : Apache Hive to write a Dataproc job that streams the data into BigQuery in CSV format

D : an Apache Beam custom connector to write a Dataflow pipeline that streams the data into BigQuery in Avro format

Answer: D