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Free Professional Data Engineer Exam Braindumps
Google Cloud Data Engineer Professional Questions 151–155 (page: 32)

Updated On: 10-Feb-2026
Page 32 of 78
PDF with 401 Questions

You are developing an application on Google Cloud that will automatically generate subject labels for users' blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning.
What should you do?

  1. Call the Cloud Natural Language API from your application. Process the generated Entity Analysis as labels.
  2. Call the Cloud Natural Language API from your application. Process the generated Sentiment Analysis as labels.
  3. Build and train a text classification model using TensorFlow. Deploy the model using Cloud Machine
    Learning Engine. Call the model from your application and process the results as labels.
  4. Build and train a text classification model using TensorFlow. Deploy the model using a Kubernetes Engine cluster. Call the model from your application and process the results as labels.

Answer(s): B



You are designing storage for 20 TB of text files as part of deploying a data pipeline on Google Cloud. Your input data is in CSV format. You want to minimize the cost of querying aggregate values for multiple users who will query the data in Cloud Storage with multiple engines.
Which storage service and schema design should you use?

  1. Use Cloud Bigtable for storage. Install the HBase shell on a Compute Engine instance to query the Cloud Bigtable data.
  2. Use Cloud Bigtable for storage. Link as permanent tables in BigQuery for query.
  3. Use Cloud Storage for storage. Link as permanent tables in BigQuery for query.
  4. Use Cloud Storage for storage. Link as temporary tables in BigQuery for query.

Answer(s): A



You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally. You also want to optimize data for range queries on nonkey columns.
What should you do?

  1. Use Cloud SQL for storage. Add secondary indexes to support query patterns.
  2. Use Cloud SQL for storage. Use Cloud Dataflow to transform data to support query patterns.
  3. Use Cloud Spanner for storage. Add secondary indexes to support query patterns.
  4. Use Cloud Spanner for storage. Use Cloud Dataflow to transform data to support query patterns.

Answer(s): D


Reference:

https://cloud.google.com/solutions/data-lifecycle-cloud-platform



Your financial services company is moving to cloud technology and wants to store 50 TB of financial timeseries data in the cloud. This data is updated frequently and new data will be streaming in all the time. Your company also wants to move their existing Apache Hadoop jobs to the cloud to get insights into this data.

Which product should they use to store the data?

  1. Cloud Bigtable
  2. Google BigQuery
  3. Google Cloud Storage
  4. Google Cloud Datastore

Answer(s): A


Reference:

https://cloud.google.com/bigtable/docs/schema-design-time-series



An organization maintains a Google BigQuery dataset that contains tables with user-level dat

  1. They want to expose aggregates of this data to other Google Cloud projects, while still controlling access to the user-level data. Additionally, they need to minimize their overall storage cost and ensure the analysis cost for other projects is assigned to those projects.
    What should they do?
  2. Create and share an authorized view that provides the aggregate results.
  3. Create and share a new dataset and view that provides the aggregate results.
  4. Create and share a new dataset and table that contains the aggregate results.
  5. Create dataViewer Identity and Access Management (IAM) roles on the dataset to enable sharing.

Answer(s): D


Reference:

https://cloud.google.com/bigquery/docs/access-control



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