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Free Professional Data Engineer Exam Braindumps
Google Cloud Data Engineer Professional Questions 191–195 (page: 40)

Updated On: 10-Feb-2026
Page 40 of 78
PDF with 401 Questions

You use a dataset in BigQuery for analysis. You want to provide third-party companies with access to the same dataset. You need to keep the costs of data sharing low and ensure that the data is current.
Which solution should you choose?

  1. Create an authorized view on the BigQuery table to control data access, and provide third-party companies with access to that view.
  2. Use Cloud Scheduler to export the data on a regular basis to Cloud Storage, and provide third- party companies with access to the bucket.
  3. Create a separate dataset in BigQuery that contains the relevant data to share, and provide third- party companies with access to the new dataset.
  4. Create a Cloud Dataflow job that reads the data in frequent time intervals, and writes it to the relevant BigQuery dataset or Cloud Storage bucket for third-party companies to use.

Answer(s): B



A shipping company has live package-tracking data that is sent to an Apache Kafka stream in real time. This is then loaded into BigQuery. Analysts in your company want to query the tracking data in BigQuery to analyze geospatial trends in the lifecycle of a package. The table was originally created with ingest-date partitioning. Over time, the query processing time has increased. You need to implement a change that would improve query performance in BigQuery.
What should you do?

  1. Implement clustering in BigQuery on the ingest date column.
  2. Implement clustering in BigQuery on the package-tracking ID column.
  3. Tier older data onto Cloud Storage files, and leverage extended tables.
  4. Re-create the table using data partitioning on the package delivery date.

Answer(s): A



You are designing a data processing pipeline. The pipeline must be able to scale automatically as load increases. Messages must be processed at least once, and must be ordered within windows of 1 hour. How should you design the solution?

  1. Use Apache Kafka for message ingestion and use Cloud Dataproc for streaming analysis.
  2. Use Apache Kafka for message ingestion and use Cloud Dataflow for streaming analysis.
  3. Use Cloud Pub/Sub for message ingestion and Cloud Dataproc for streaming analysis.
  4. Use Cloud Pub/Sub for message ingestion and Cloud Dataflow for streaming analysis.

Answer(s): D



You need to set access to BigQuery for different departments within your company. Your solution should comply with the following requirements:

Each department should have access only to their data.

Each department will have one or more leads who need to be able to create and update tables and provide them to their team.

Each department has data analysts who need to be able to query but not modify data.

How should you set access to the data in BigQuery?

  1. Create a dataset for each department. Assign the department leads the role of OWNER, and assign the data analysts the role of WRITER on their dataset.
  2. Create a dataset for each department. Assign the department leads the role of WRITER, and assign the data analysts the role of READER on their dataset.
  3. Create a table for each department. Assign the department leads the role of Owner, and assign the data analysts the role of Editor on the project the table is in.
  4. Create a table for each department. Assign the department leads the role of Editor, and assign the data analysts the role of Viewer on the project the table is in.

Answer(s): D



You operate a database that stores stock trades and an application that retrieves average stock price for a given company over an adjustable window of time. The data is stored in Cloud Bigtable where the datetime of the stock trade is the beginning of the row key. Your application has thousands of concurrent users, and you notice that performance is starting to degrade as more stocks are added.
What should you do to improve the performance of your application?

  1. Change the row key syntax in your Cloud Bigtable table to begin with the stock symbol.
  2. Change the row key syntax in your Cloud Bigtable table to begin with a random number per second.
  3. Change the data pipeline to use BigQuery for storing stock trades, and update your application.
  4. Use Cloud Dataflow to write summary of each day's stock trades to an Avro file on Cloud Storage.
    Update your application to read from Cloud Storage and Cloud Bigtable to compute the responses.

Answer(s): A



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