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Exam Code Professional-Data-Engineer
Exam Name Google Cloud Data Engineer Professional
Vendor Google
Total Questions 384
Last Updated 26-Oct, 2026
Free Professional-Data-Engineer Questions Get Premium Access
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Free Professional-Data-Engineer Practice Questions

Explore 10 free practice questions for the Google Professional-Data-Engineer exam and review the answers and explanations to support your preparation.

  1. Question 1

    Multiple answers

    Your company handles data processing for a number of different clients. Each client prefers to use their own suite of analytics tools, with some allowing direct query access via Google BigQuery. You need to secure the data so that clients cannot see each other’s data. You want to ensure appropriate access to the data. Which three steps should you take? (Choose three.)

    • Option A:

      Load data into different partitions.

      Correct
    • Option B:

      Load data into a different dataset for each client.

      Correct
    • Option C:

      Put each client’s BigQuery dataset into a different table.

      Correct
    • Option D:

      Restrict a client’s dataset to approved users.

      Correct
    • Option E:

      Only allow a service account to access the datasets.

      Correct
    • Option F:

      Use the appropriate identity and access management (IAM) roles for each client’s users.

      Correct
    Show Answer & Explanation Hide Answer & Explanation

    Correct Answer: B, D, F

  2. Question 2

    Single answer

    The Development and External teams nave the project viewer Identity and Access Management (1AM) role m a folder named Visualization. You want the Development Team to be able to read data from both Cloud Storage and BigQuery, but the External Team should only be able to read data from BigQuery. What should you do?

    • Option A:

      Remove Cloud Storage IAM permissions to the External Team on the acme-raw-data project

      Correct
    • Option B:

      Create Virtual Private Cloud (VPC) firewall rules on the acme-raw-data protect that deny all Ingress traffic from the External Team CIDR range

      Correct
    • Option C:

      Create a VPC Service Controls perimeter containing both protects and BigQuery as a restricted API Add the External Team users to the perimeter s Access Level

      Correct
    • Option D:

      Create a VPC Service Controls perimeter containing both protects and Cloud Storage as a restricted API. Add the Development Team users to the perimeter ' s Access Level

      Correct
    Show Answer & Explanation Hide Answer & Explanation

    Correct Answer: C

  3. Question 3

    Single answer

    Which of these statements about exporting data from BigQuery is false?

    • Option A:

      To export more than 1 GB of data, you need to put a wildcard in the destination filename.

      Correct
    • Option B:

      The only supported export destination is Google Cloud Storage.

      Correct
    • Option C:

      Data can only be exported in JSON or Avro format.

      Correct
    • Option D:

      The only compression option available is GZIP.

      Correct
    Show Answer & Explanation Hide Answer & Explanation

    Correct Answer: C

    Explanation

    Data can be exported in CSV, JSON, or Avro format. If you are exporting nested or repeated data, then CSV format is not supported.

    [Reference: https://cloud.google.com/bigquery/docs/exporting-data, , ]

  4. Question 4

    Single answer

    You have a job that you want to cancel. It is a streaming pipeline, and you want to ensure that any data that is in-flight is processed and written to the output. Which of the following commands can you use on the Dataflow monitoring console to stop the pipeline job?

    • Option A:

      Cancel

      Correct
    • Option B:

      Drain

      Correct
    • Option C:

      Stop

      Correct
    • Option D:

      Finish

      Correct
    Show Answer & Explanation Hide Answer & Explanation

    Correct Answer: B

    Explanation

    Using the Drain option to stop your job tells the Dataflow service to finish your job in its current state. Your job will immediately stop ingesting new data from input sources, but the Dataflow

    service will preserve any existing resources (such as worker instances) to finish processing and writing any buffered data in your pipeline.

    [Reference: https://cloud.google.com/dataflow/pipelines/stopping-a-pipeline, , ]

  5. Question 5

    Multiple answers

    What are two of the characteristics of using online prediction rather than batch prediction?

    • Option A:

      It is optimized to handle a high volume of data instances in a job and to run more complex models.

      Correct
    • Option B:

      Predictions are returned in the response message.

      Correct
    • Option C:

      Predictions are written to output files in a Cloud Storage location that you specify.

      Correct
    • Option D:

      It is optimized to minimize the latency of serving predictions.

      Correct
    Show Answer & Explanation Hide Answer & Explanation

    Correct Answer: B, D

    Explanation

    Online prediction

    Optimized to minimize the latency of serving predictions.

    Predictions returned in the response message.

    Batch prediction

    Optimized to handle a high volume of instances in a job and to run more complex models.

    Predictions written to output files in a Cloud Storage location that you specify.

    [Reference: https://cloud.google.com/ml-engine/docs/prediction-overview#online_prediction_versus_batch_prediction, , ]

  6. Question 6

    Single answer

    You are planning to use Google ' s Dataflow SDK to analyze customer data such as displayed below. Your project requirement is to extract only the customer name from the data source and then write to an output PCollection.

    Tom,555 X street

    Tim,553 Y street

    Sam, 111 Z street

    Which operation is best suited for the above data processing requirement?

    • Option A:

      ParDo


      Correct
    • Option B:

      Sink API


      Correct
    • Option C:

      Source API


      Correct
    • Option D:

      Data extraction

      Correct
    Show Answer & Explanation Hide Answer & Explanation

    Correct Answer: A

    Explanation

    In Google Cloud dataflow SDK, you can use the ParDo to extract only a customer name of each

    element in your PCollection.

    [Reference: https://cloud.google.com/dataflow/model/par-do, , ]

  7. Question 7

    Single answer

    Which of these statements about exporting data from BigQuery is false?

    • Option A:

      To export more than 1 GB of data, you need to put a wildcard in the destination filename.

      Correct
    • Option B:

      The only supported export destination is Google Cloud Storage.


      Correct
    • Option C:

      Data can only be exported in JSON or Avro format.


      Correct
    • Option D:

      The only compression option available is GZIP.

      Correct
    Show Answer & Explanation Hide Answer & Explanation

    Correct Answer: C

    Explanation

    Data can be exported in CSV, JSON, or Avro format. If you are exporting nested or repeated data, then CSV format is not supported.

    [Reference: https://cloud.google.com/bigquery/docs/exporting-data, , ]

  8. Question 8

    Single answer

    When creating a new Cloud Dataproc cluster with the projects.regions.clusters.create operation, these four values are required: project, region, name, and ____.

    • Option A:

      zone


      Correct
    • Option B:

      node


      Correct
    • Option C:

      label


      Correct
    • Option D:

      type

      Correct
    Show Answer & Explanation Hide Answer & Explanation

    Correct Answer: A

    Explanation

    At a minimum, you must specify four values when creating a new cluster with the projects.regions.clusters.create operation:

    The project in which the cluster will be created

    The region to use

    The name of the cluster

    The zone in which the cluster will be created

    You can specify many more details beyond these minimum requirements. For example, you can

    also specify the number of workers, whether preemptible compute should be used, and the network settings.

    [Reference: https://cloud.google.com/dataproc/docs/tutorials/python-library-example#create_a_new_cloud_dataproc_cluste, , ]

  9. Question 9

    Single answer

    What is the recommended action to do in order to switch between SSD and HDD storage for your Google Cloud Bigtable instance?

    • Option A:

      create a third instance and sync the data from the two storage types via batch jobs


      Correct
    • Option B:

      export the data from the existing instance and import the data into a new instance


      Correct
    • Option C:

      run parallel instances where one is HDD and the other is SDD


      Correct
    • Option D:

      the selection is final and you must resume using the same storage type

      Correct
    Show Answer & Explanation Hide Answer & Explanation

    Correct Answer: B

    Explanation

    When you create a Cloud Bigtable instance and cluster, your choice of SSD or HDD storage for the cluster is permanent. You cannot use the Google Cloud Platform Console to change the type of storage that is used for the cluster.

    If you need to convert an existing HDD cluster to SSD, or vice-versa, you can export the data from the existing instance and import the data into a new instance. Alternatively, you can write

    a Cloud Dataflow or Hadoop MapReduce job that copies the data from one instance to another.

    [Reference: https://cloud.google.com/bigtable/docs/choosing-ssd-hdd–, , ]

  10. Question 10

    Single answer

    You have a job that you want to cancel. It is a streaming pipeline, and you want to ensure that any data that is in-flight is processed and written to the output. Which of the following commands can you use on the Dataflow monitoring console to stop the pipeline job?

    • Option A:

      Cancel


      Correct
    • Option B:

      Drain

      Correct
    • Option C:

      Stop


      Correct
    • Option D:

      Finish

      Correct
    Show Answer & Explanation Hide Answer & Explanation

    Correct Answer: B

    Explanation

    Using the Drain option to stop your job tells the Dataflow service to finish your job in its current state. Your job will immediately stop ingesting new data from input sources, but the Dataflow

    service will preserve any existing resources (such as worker instances) to finish processing and writing any buffered data in your pipeline.

    [Reference: https://cloud.google.com/dataflow/pipelines/stopping-a-pipeline, , ]