8/28/25, 7:00 PM GCP DATA ENGINEER CERTIFICATION
GCP Professional Data Engineer Certification Exam Newest
2025/2026 With Complete Questions And Correct Answers
|Already Graded A+||Brand New Version !! Actual exam GCP
Professional Data Engineer
You are creating a data Tumbling windows (fixed windows in Apache Beam)
pipeline for
streaming data on Dataflow Correct. A tumbling window (or fixed window in Apache
for Cymbal Retail's point of Beam) is fixed duration and non-overlapping, which is
sales data. You want to the right option for this requirement.
calculate the total sales
per hour on a continuous
basis. Which of these
windowing options should
you use?
Global window
Tumbling windows
(fixed windows in
Apache Beam)
Hopping windows (sliding
windows in Apache Beam)
Session windows
You have a data pipeline Cloud Composer
that requires you to
monitor a Cloud Storage Correct. Cloud Composer, a managed version of
bucket for a Apache Airflow, can orchestrate a series of data
file, start a Dataflow job to pipeline tasks.
process data in the file, run
a shell script to validate
the
processed data in BigQuery,
/ 1/94
,8/28/25, 7:00 PM GCP DATA ENGINEER CERTIFICATION
and then delete the
original file. You need to
orchestrate this pipeline by
using
recommended tools. Which
product should you choose?
-Cloud Scheduler
-Cloud Composer
-Cloud Run
-Cloud Tasks
Your data engineering team Store the data in Cloud Storage and create an
receives data in JSON extract, transform, and load (ETL) pipeline.
format from external sources
at the end of each day. You Correct. The recommended approach for batch data pipelines is
to store data in
need to design the
data pipeline. What should Cloud Storage. Then, create an ETL (or ELT, depending
you do? on the use case) pipeline to move the data into a data
warehouse.
-Create a public API to
allow external
applications to add the
data to your
warehouse.
-Make your BigQuery
data warehouse public
and ask the external
sources to insert the
data.
-Store the data in
persistent disks and
create an ETL pipeline.
-Store the data in Cloud
Storage and
create an extract, transform,
and load (ETL) pipeline.
/ 2/94
,8/28/25, 7:00 PM GCP DATA ENGINEER CERTIFICATION
You want to build a streaming Pub/Sub, Dataflow, BigQuery
data
analytics pipeline in Correct. Pub/Sub, Dataflow, and BigQuery support
Google Cloud. You need streaming data and form the recommended pipeline
to choose the right for continuous data processing.
products that support
streaming data. Which of
these would you
choose?
-Cloud Storage, Dataprep,
AlloyDB
-Pub/Sub, Dataprep, BigQuery
-Cloud Storage, Dataflow,
Cloud SQL
-Pub/Sub, Dataflow, BigQuery
You are processing large Use a federated query to get data from Cloud SQL.
amounts of input data in
BigQuery. You need to Correct. Because the data is frequently changing, you
combine this data with a can query the data in-place by using federated queries
small amount of frequently from BigQuery.
changing data that is
available in Cloud SQL.
What should you do?
-Use a federated query to
get data from Cloud SQL.
-Create a Dataflow pipeline to
combine the BigQuery and
Cloud SQL data when the
Cloud SQL data changes.
-Copy the data from
Cloud SQL to a new
BigQuery table hourly.
/ 3/94
, 8/28/25, 7:00 PM GCP DATA ENGINEER CERTIFICATION
-Copy the data from Cloud
SQL and create a
combined, normalized
table hourly.
The first stage of your data Bigtable
pipeline
processes tens of Correct. Bigtable is ideal for applications that need high
terabytes of financial data throughput and scalability for key/value data, where
and creates a sparse, each value is typically no larger than 10 MB: Bigtable is
time-series suitable for applications that work on time-series data, such as
financial applications.
dataset as a key-value pair.
Which of these is a suitable
sink for the pipeline's first
stage?
-Cloud Storage
-Bigtable
-Cloud SQL
-AlloyDB
You need to run batch Run the jobs on Batch.
jobs, which could take
many days to complete. Correct. Batch is a fully managed service that schedules,
You do not want to queues, and executes batch processing workloads on
manage the Google Cloud. Resources and capacity are
infrastructure provisioned
provisioning. What should you and managed for you based on your requirements.
do?
-Use Cloud Scheduler to run
the jobs.
-Use Cloud Run to run the
jobs.
/ 4/94
GCP Professional Data Engineer Certification Exam Newest
2025/2026 With Complete Questions And Correct Answers
|Already Graded A+||Brand New Version !! Actual exam GCP
Professional Data Engineer
You are creating a data Tumbling windows (fixed windows in Apache Beam)
pipeline for
streaming data on Dataflow Correct. A tumbling window (or fixed window in Apache
for Cymbal Retail's point of Beam) is fixed duration and non-overlapping, which is
sales data. You want to the right option for this requirement.
calculate the total sales
per hour on a continuous
basis. Which of these
windowing options should
you use?
Global window
Tumbling windows
(fixed windows in
Apache Beam)
Hopping windows (sliding
windows in Apache Beam)
Session windows
You have a data pipeline Cloud Composer
that requires you to
monitor a Cloud Storage Correct. Cloud Composer, a managed version of
bucket for a Apache Airflow, can orchestrate a series of data
file, start a Dataflow job to pipeline tasks.
process data in the file, run
a shell script to validate
the
processed data in BigQuery,
/ 1/94
,8/28/25, 7:00 PM GCP DATA ENGINEER CERTIFICATION
and then delete the
original file. You need to
orchestrate this pipeline by
using
recommended tools. Which
product should you choose?
-Cloud Scheduler
-Cloud Composer
-Cloud Run
-Cloud Tasks
Your data engineering team Store the data in Cloud Storage and create an
receives data in JSON extract, transform, and load (ETL) pipeline.
format from external sources
at the end of each day. You Correct. The recommended approach for batch data pipelines is
to store data in
need to design the
data pipeline. What should Cloud Storage. Then, create an ETL (or ELT, depending
you do? on the use case) pipeline to move the data into a data
warehouse.
-Create a public API to
allow external
applications to add the
data to your
warehouse.
-Make your BigQuery
data warehouse public
and ask the external
sources to insert the
data.
-Store the data in
persistent disks and
create an ETL pipeline.
-Store the data in Cloud
Storage and
create an extract, transform,
and load (ETL) pipeline.
/ 2/94
,8/28/25, 7:00 PM GCP DATA ENGINEER CERTIFICATION
You want to build a streaming Pub/Sub, Dataflow, BigQuery
data
analytics pipeline in Correct. Pub/Sub, Dataflow, and BigQuery support
Google Cloud. You need streaming data and form the recommended pipeline
to choose the right for continuous data processing.
products that support
streaming data. Which of
these would you
choose?
-Cloud Storage, Dataprep,
AlloyDB
-Pub/Sub, Dataprep, BigQuery
-Cloud Storage, Dataflow,
Cloud SQL
-Pub/Sub, Dataflow, BigQuery
You are processing large Use a federated query to get data from Cloud SQL.
amounts of input data in
BigQuery. You need to Correct. Because the data is frequently changing, you
combine this data with a can query the data in-place by using federated queries
small amount of frequently from BigQuery.
changing data that is
available in Cloud SQL.
What should you do?
-Use a federated query to
get data from Cloud SQL.
-Create a Dataflow pipeline to
combine the BigQuery and
Cloud SQL data when the
Cloud SQL data changes.
-Copy the data from
Cloud SQL to a new
BigQuery table hourly.
/ 3/94
, 8/28/25, 7:00 PM GCP DATA ENGINEER CERTIFICATION
-Copy the data from Cloud
SQL and create a
combined, normalized
table hourly.
The first stage of your data Bigtable
pipeline
processes tens of Correct. Bigtable is ideal for applications that need high
terabytes of financial data throughput and scalability for key/value data, where
and creates a sparse, each value is typically no larger than 10 MB: Bigtable is
time-series suitable for applications that work on time-series data, such as
financial applications.
dataset as a key-value pair.
Which of these is a suitable
sink for the pipeline's first
stage?
-Cloud Storage
-Bigtable
-Cloud SQL
-AlloyDB
You need to run batch Run the jobs on Batch.
jobs, which could take
many days to complete. Correct. Batch is a fully managed service that schedules,
You do not want to queues, and executes batch processing workloads on
manage the Google Cloud. Resources and capacity are
infrastructure provisioned
provisioning. What should you and managed for you based on your requirements.
do?
-Use Cloud Scheduler to run
the jobs.
-Use Cloud Run to run the
jobs.
/ 4/94
