100% Free Google Professional-Data-Engineer Practice Test Questions

Question 1

Your company is currently setting up data pipelines for their campaign. For all the Google Cloud Pub/Sub streaming data, one of the important business requirements is to be able to periodically identify the inputs and their timings during their campaign. Engineers have decided to use windowing and transformation in Google Cloud Dataflow for this purpose. However, when testing this feature, they find that the Cloud Dataflow job fails for the all streaming insert. What is the most likely cause of this problem?

A : They have not assigned the timestamp, which causes the job to fail

B : They have not set the triggers to accommodate the data coming in late, which causes the job to fail

C : They have not applied a global windowing function, which causes the job to fail when the pipeline is created

D : They have not applied a non-global windowing function, which causes the job to fail when the pipeline is created

Answer: C

Question 2

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 3

You are developing a data pipeline that will run several data transformation programs on Compute Engine virtual machines. You do not want to use your credentials for authenticating and authorizing these programs. You want to follow Google Cloud recommended practices, how would you authenticate and authorize the data transformation programs?

A : Create a Gmail account and use that account to create an IAM group. Store the password for the group in Secret Manager.

B : Create a service account and assign roles to the service account that are needed to execute the data transformation programs. Use Google managed keys to store both public and private portion of the service account keys.

C : Create a Gmail account and use that account to create an IAM user. Store the password for the account in Secret Manager.

D : Create a service account and assign roles to the service account that are needed to execute the data transformation programs. Use Secret Manager to store service account keys.

Answer: B

Question 4

You want to schedule a number of sequential load and transformation jobs Data files will be added to a Cloud

Storage bucket by an upstream process There is no fixed schedule for when the new data arrives Next, a

Dataproc job is triggered to perform some transformations and write the data to BigQuery. You then need to

run additional transformation jobs in BigQuery The transformation jobs are different for every table These

jobs might take hours to complete You need to determine the most efficient and maintainable workflow to

process hundreds of tables and provide the freshest data to your end users. What should you do?

A : 1Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks byusing the Cloud Storage. Dataproc. and BigQuery operators2 Use a single shared DAG for all tables that need to go through the pipeline3 Schedule the DAG to run hourly

B : 1 Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks byusing the Dataproc and BigQuery operators.2 Create a separate DAG for each table that needs to go through the pipeline3 Use a Cloud Storage object trigger to launch a Cloud Function that triggers the DAG

C : 1 Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks byusing the Cloud Storage, Dataproc. and BigQuery operators2 Create a separate DAG for each table that needs to go through the pipeline3 Schedule the DAGs to run hourly

D : 1 Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks byusing the Dataproc and BigQuery operators2 Use a single shared DAG for all tables that need to go through the pipeline.3 Use a Cloud Storage object trigger to launch a Cloud Function that triggers the DAG

Answer: B

Question 5

How would you query specific partitions in a BigQuery table?

A : Use the DAY column in the WHERE clause

B : Use the EXTRACT(DAY) clause

C : Use the __PARTITIONTIME pseudo-column in the WHERE clause

D : Use DATE BETWEEN in the WHERE clause

Answer: C

Free Practice Google Professional-Data-Engineer Exam Questions 2025