Free Professional Data Engineer Exam Braindumps
Google Cloud Data Engineer Professional Questions 106–110 (page: 23)

Updated On: 10-Feb-2026

When using Cloud Dataproc clusters, you can access the YARN web interface by configuring a browser to connect through a ____ proxy.

  1. HTTPS
  2. VPN
  3. SOCKS
  4. HTTP

Answer(s): C

Explanation:

When using Cloud Dataproc clusters, configure your browser to use the SOCKS proxy. The SOCKS proxy routes data intended for the Cloud Dataproc cluster through an SSH tunnel.


Reference:

https://cloud.google.com/dataproc/docs/concepts/cluster-web-interfaces#interfaces



Cloud Dataproc is a managed Apache Hadoop and Apache _____ service.

  1. Blaze
  2. Spark
  3. Fire
  4. Ignite

Answer(s): B

Explanation:

Cloud Dataproc is a managed Apache Spark and Apache Hadoop service that lets you use open source data tools for batch processing, querying, streaming, and machine learning.


Reference:

https://cloud.google.com/dataproc/docs/



Which action can a Cloud Dataproc Viewer perform?

  1. Submit a job.
  2. Create a cluster.
  3. Delete a cluster.
  4. List the jobs.

Answer(s): D

Explanation:

A Cloud Dataproc Viewer is limited in its actions based on its role. A viewer can only list clusters, get cluster details, list jobs, get job details, list operations, and get operation details.


Reference:

https://cloud.google.com/dataproc/docs/concepts/iam#iam_roles_and_cloud_dataproc_operations _summary



Dataproc clusters contain many configuration files. To update these files, you will need to use the -- properties option. The format for the option is: file_prefix:property=_____.

  1. details
  2. value
  3. null
  4. id

Answer(s): B

Explanation:

To make updating files and properties easy, the --properties command uses a special format to specify the configuration file and the property and value within the file that should be updated. The formatting is as follows: file_prefix:property=value.


Reference:

https://cloud.google.com/dataproc/docs/concepts/cluster-properties#formatting



Scaling a Cloud Dataproc cluster typically involves ____.

  1. increasing or decreasing the number of worker nodes
  2. increasing or decreasing the number of master nodes
  3. moving memory to run more applications on a single node
  4. deleting applications from unused nodes periodically

Answer(s): A

Explanation:

After creating a Cloud Dataproc cluster, you can scale the cluster by increasing or decreasing the number of worker nodes in the cluster at any time, even when jobs are running on the cluster. Cloud Dataproc clusters are typically scaled to:

1) increase the number of workers to make a job run faster

2) decrease the number of workers to save money

3) increase the number of nodes to expand available Hadoop Distributed Filesystem (HDFS) storage


Reference:

https://cloud.google.com/dataproc/docs/concepts/scaling-clusters



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