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Free Professional Data Engineer Exam Braindumps
Google Cloud Data Engineer Professional Questions 176–180 (page: 37)

Updated On: 10-Feb-2026
Page 37 of 78
PDF with 401 Questions

You have a requirement to insert minute-resolution data from 50,000 sensors into a BigQuery table. You expect significant growth in data volume and need the data to be available within 1 minute of ingestion for real-time analysis of aggregated trends.
What should you do?

  1. Use bq load to load a batch of sensor data every 60 seconds.
  2. Use a Cloud Dataflow pipeline to stream data into the BigQuery table.
  3. Use the INSERT statement to insert a batch of data every 60 seconds.
  4. Use the MERGE statement to apply updates in batch every 60 seconds.

Answer(s): B



You need to copy millions of sensitive patient records from a relational database to BigQuery. The total size of the database is 10 TB. You need to design a solution that is secure and time-efficient.
What should you do?

  1. Export the records from the database as an Avro file. Upload the file to GCS using gsutil, and then load the Avro file into BigQuery using the BigQuery web UI in the GCP Console.
  2. Export the records from the database as an Avro file. Copy the file onto a Transfer Appliance and send it to Google, and then load the Avro file into BigQuery using the BigQuery web UI in the GCP Console.
  3. Export the records from the database into a CSV file. Create a public URL for the CSV file, and then use Storage Transfer Service to move the file to Cloud Storage. Load the CSV file into BigQuery using the BigQuery web UI in the GCP Console.
  4. Export the records from the database as an Avro file. Create a public URL for the Avro file, and then use Storage Transfer Service to move the file to Cloud Storage. Load the Avro file into BigQuery using the BigQuery web UI in the GCP Console.

Answer(s): A



You need to create a near real-time inventory dashboard that reads the main inventory tables in your BigQuery data warehouse. Historical inventory data is stored as inventory balances by item and location. You have several thousand updates to inventory every hour. You want to maximize performance of the dashboard and ensure that the data is accurate.
What should you do?

  1. Leverage BigQuery UPDATE statements to update the inventory balances as they are changing.
  2. Partition the inventory balance table by item to reduce the amount of data scanned with each inventory update.
  3. Use the BigQuery streaming the stream changes into a daily inventory movement table. Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.
  4. Use the BigQuery bulk loader to batch load inventory changes into a daily inventory movement table. Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.

Answer(s): A



You have a data stored in BigQuery. The data in the BigQuery dataset must be highly available. You need to define a storage, backup, and recovery strategy of this data that minimizes cost. How should you configure the BigQuery table?

  1. Set the BigQuery dataset to be regional. In the event of an emergency, use a point-in-time snapshot to recover the data.
  2. Set the BigQuery dataset to be regional. Create a scheduled query to make copies of the data to tables suffixed with the time of the backup. In the event of an emergency, use the backup copy of the table.
  3. Set the BigQuery dataset to be multi-regional. In the event of an emergency, use a point-in-time snapshot to recover the data.
  4. Set the BigQuery dataset to be multi-regional. Create a scheduled query to make copies of the data to tables suffixed with the time of the backup. In the event of an emergency, use the backup copy of the table.

Answer(s): B



You used Cloud Dataprep to create a recipe on a sample of data in a BigQuery table. You want to reuse this recipe on a daily upload of data with the same schema, after the load job with variable execution time completes.
What should you do?

  1. Create a cron schedule in Cloud Dataprep.
  2. Create an App Engine cron job to schedule the execution of the Cloud Dataprep job.
  3. Export the recipe as a Cloud Dataprep template, and create a job in Cloud Scheduler.
  4. Export the Cloud Dataprep job as a Cloud Dataflow template, and incorporate it into a Cloud Composer job.

Answer(s): D



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