support@Free-Braindumps.com
Register Login
Cisco
CompTIA
ISC
EC-Council
EXIN
Fortinet
ITIL
Juniper
Microsoft
VMware
Avaya
SAP
Google
NVIDIA
Salesforce
Amazon
Palo Alto Networks
ISC2
CrowdStrike
Anthropic
All Vendors
  1. Home
  2. Exams
  3. Google
  4. Professional Data Engineer

Free Professional Data Engineer Exam Braindumps
Google Cloud Data Engineer Professional Questions 146–150 (page: 31)

Updated On: 10-Feb-2026
Page 31 of 78
PDF with 401 Questions

You set up a streaming data insert into a Redis cluster via a Kafka cluster. Both clusters are running on

Compute Engine instances. You need to encrypt data at rest with encryption keys that you can create, rotate, and destroy as needed.
What should you do?

  1. Create a dedicated service account, and use encryption at rest to reference your data stored in your
    Compute Engine cluster instances as part of your API service calls.
  2. Create encryption keys in Cloud Key Management Service. Use those keys to encrypt your data in all of the Compute Engine cluster instances.
  3. Create encryption keys locally. Upload your encryption keys to Cloud Key Management Service.
    Use those keys to encrypt your data in all of the Compute Engine cluster instances.
  4. Create encryption keys in Cloud Key Management Service.
    Reference: those keys in your API service calls when accessing the data in your Compute Engine cluster instances.

Answer(s): C


Reference:

those keys in your API service calls when accessing the data in your Compute Engine cluster instances.

Answer(s): C



You are developing an application that uses a recommendation engine on Google Cloud. Your solution should display new videos to customers based on past views. Your solution needs to generate labels for the entities in videos that the customer has viewed. Your design must be able to provide very fast filtering suggestions based on data from other customer preferences on several TB of dat

  1. What should you do?
  2. Build and train a complex classification model with Spark MLlib to generate labels and filter the results.
    Deploy the models using Cloud Dataproc. Call the model from your application.
  3. Build and train a classification model with Spark MLlib to generate labels. Build and train a second classification model with Spark MLlib to filter results to match customer preferences. Deploy the models using Cloud Dataproc. Call the models from your application.
  4. Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud
    Bigtable, and filter the predicted labels to match the user's viewing history to generate preferences.
  5. Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud
    SQL, and join and filter the predicted labels to match the user's viewing history to generate preferences.

Answer(s): C



You are selecting services to write and transform JSON messages from Cloud Pub/Sub to BigQuery for a data pipeline on Google Cloud. You want to minimize service costs. You also want to monitor and accommodate input data volume that will vary in size with minimal manual intervention.
What should you do?

  1. Use Cloud Dataproc to run your transformations. Monitor CPU utilization for the cluster. Resize the number of worker nodes in your cluster via the command line.
  2. Use Cloud Dataproc to run your transformations. Use the diagnose command to generate an operational output archive. Locate the bottleneck and adjust cluster resources.
  3. Use Cloud Dataflow to run your transformations. Monitor the job system lag with Stackdriver. Use the default autoscaling setting for worker instances.
  4. Use Cloud Dataflow to run your transformations. Monitor the total execution time for a sampling of jobs. Configure the job to use non-default Compute Engine machine types when needed.

Answer(s): B



Your infrastructure includes a set of YouTube channels. You have been tasked with creating a process for sending the YouTube channel data to Google Cloud for analysis. You want to design a solution that allows your world-wide marketing teams to perform ANSI SQL and other types of analysis on up-to- date YouTube channels log dat

  1. How should you set up the log data transfer into Google Cloud?
  2. Use Storage Transfer Service to transfer the offsite backup files to a Cloud Storage Multi-Regional storage bucket as a final destination.
  3. Use Storage Transfer Service to transfer the offsite backup files to a Cloud Storage Regional bucket as a final destination.
  4. Use BigQuery Data Transfer Service to transfer the offsite backup files to a Cloud Storage Multi- Regional storage bucket as a final destination.
  5. Use BigQuery Data Transfer Service to transfer the offsite backup files to a Cloud Storage Regional storage bucket as a final destination.

Answer(s): B



You are designing storage for very large text files for a data pipeline on Google Cloud. You want to support ANSI SQL queries. You also want to support compression and parallel load from the input locations using Google recommended practices.
What should you do?

  1. Transform text files to compressed Avro using Cloud Dataflow. Use BigQuery for storage and query.
  2. Transform text files to compressed Avro using Cloud Dataflow. Use Cloud Storage and BigQuery permanent linked tables for query.
  3. Compress text files to gzip using the Grid Computing Tools. Use BigQuery for storage and query.
  4. Compress text files to gzip using the Grid Computing Tools. Use Cloud Storage, and then import into
    Cloud Bigtable for query.

Answer(s): D



  PDF
Viewing page 31 of 78
Viewing questions 151 - 155 out of 401 questions


Post your Comments and Discuss Google Professional Data Engineer exam prep with other Community members:

AI Tutor AI Tutor 👋 I’m here to help!