Free Google Professional-Data-Engineer Exam Questions

Question 1

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 : Specify a worker region by using the —region flag.

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 : Create an Eventarc trigger to resubmit the job in case of zonal failure when submitting the job.

Answer: A

Question 2

You are designing the architecture of your application to store data in Cloud Storage. Your application consists of pipelines that read data from a Cloud Storage bucket that contains raw data, and write the data to a second bucket after processing. You want to design an architecture with Cloud Storage resources that are capable of being resilient if a Google Cloud regional failure occurs. You want to minimize the recovery point objective (RPO) if a failure occurs, with no impact on applications that use the stored data. What should you do?

A : Adopt two regional Cloud Storage buckets, and update your application to write the output on both buckets.

B : Adopt multi-regional Cloud Storage buckets in your architecture.

C : Adopt two regional Cloud Storage buckets, and create a daily task to copy from one bucket to the other.

D : Adopt a dual-region Cloud Storage bucket, and enable turbo replication in your architecture.

Answer: D

Question 3

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

Question 4

You have a BigQuery dataset named "customers". All tables will be tagged by using a Data Catalog tag

template named "gdpr". The template contains one mandatory field, "has sensitive data~. with a boolean value.

All employees must be able to do a simple search and find tables in the dataset that have either true or false in

the "has sensitive data" field. However, only the Human Resources (HR) group should be able to see the data

inside the tables for which "hass-ensitive-data" is true. You give the all employees group the

bigquery.metadataViewer and bigquery.connectionUser roles on the dataset. You want to minimize

configuration overhead. What should you do next?

A : Create the "gdpr" tag template with private visibility. Assign the bigquery -dataViewer role to the HRgroup on the tables that contain sensitive data.

B : Create the ~gdpr" tag template with private visibility. Assign the datacatalog. tagTemplateViewer roleon this tag to the all employeesgroup, and assign the bigquery.dataViewer role to the HR group on the tables that contain sensitive data.

C : Create the "gdpr" tag template with public visibility. Assign the bigquery. dataViewer role to the HRgroup on the tables that containsensitive data.

D : Create the "gdpr" tag template with public visibility. Assign the datacatalog. tagTemplateViewer role onthis tag to the all employees.group, and assign the bijquery.dataViewer role to the HR group on the tables that contain sensitive data.

Answer: D

Question 5

You have a data pipeline that writes data to Cloud Bigtable using well-designed row keys. You want to monitor your pipeline to determine when to increase the size of you Cloud Bigtable cluster. Which two actions can you take to accomplish this? Choose 2 answers.

A : view Key Visualizer metrics. Increase the size of the Cloud Bigtable cluster when the Read pressure index is above 100

B : view Key Visualizer metrics. Increase the size of the Cloud Bigtable cluster when the Write pressure index is above 100.

C : nitor the latency of write operations. Increase the size of the Cloud Bigtable cluster when there is a sustained increase in write latency.

D : nitor storage utilization. Increase the size of the Cloud Bigtable cluster when utilization increases above 70% of max capacity

E : nitor latency of read operations. Increase the size of the Cloud Bigtable cluster of read operations take longer than 100 ms

Answer: A,C