Free Professional Data Engineer Exam Braindumps
Google Cloud Data Engineer Professional Questions 196–200 (page: 41)

Updated On: 10-Feb-2026

You are operating a Cloud Dataflow streaming pipeline. The pipeline aggregates events from a Cloud Pub/Sub subscription source, within a window, and sinks the resulting aggregation to a Cloud Storage bucket. The source has consistent throughput. You want to monitor an alert on behavior of the pipeline with Cloud Stackdriver to ensure that it is processing dat

  1. Which Stackdriver alerts should you create?
  2. An alert based on a decrease of subscription/num_undelivered_messages for the source and a rate of change increase of instance/storage/used_bytes for the destination
  3. An alert based on an increase of subscription/num_undelivered_messages for the source and a rate of change decrease of instance/storage/used_bytes for the destination
  4. An alert based on a decrease of instance/storage/used_bytes for the source and a rate of change increase of subscription/num_undelivered_messages for the destination
  5. An alert based on an increase of instance/storage/used_bytes for the source and a rate of change decrease of subscription/num_undelivered_messages for the destination

Answer(s): B



You currently have a single on-premises Kafka cluster in a data center in the us-east region that is responsible for ingesting messages from IoT devices globally. Because large parts of globe have poor internet connectivity, messages sometimes batch at the edge, come in all at once, and cause a spike in load on your Kafka cluster. This is becoming difficult to manage and prohibitively expensive.
What is the Google-recommended cloud native architecture for this scenario?

  1. Edge TPUs as sensor devices for storing and transmitting the messages.
  2. Cloud Dataflow connected to the Kafka cluster to scale the processing of incoming messages.
  3. An IoT gateway connected to Cloud Pub/Sub, with Cloud Dataflow to read and process the messages from Cloud Pub/Sub.
  4. A Kafka cluster virtualized on Compute Engine in us-east with Cloud Load Balancing to connect to the devices around the world.

Answer(s): C



You decided to use Cloud Datastore to ingest vehicle telemetry data in real time. You want to build a storage system that will account for the long-term data growth, while keeping the costs low. You also want to create snapshots of the data periodically, so that you can make a point-in-time (PIT) recovery, or clone a copy of the data for Cloud Datastore in a different environment. You want to archive these snapshots for a long time.
Which two methods can accomplish this? Choose 2 answers.

  1. Use managed export, and store the data in a Cloud Storage bucket using Nearline or Coldline class.
  2. Use managed exportm, and then import to Cloud Datastore in a separate project under a unique namespace reserved for that export.
  3. Use managed export, and then import the data into a BigQuery table created just for that export, and delete temporary export files.
  4. Write an application that uses Cloud Datastore client libraries to read all the entities. Treat each entity as a BigQuery table row via BigQuery streaming insert. Assign an export timestamp for each export, and attach it as an extra column for each row. Make sure that the BigQuery table is partitioned using the export timestamp column.
  5. Write an application that uses Cloud Datastore client libraries to read all the entities. Format the exported data into a JSON file. Apply compression before storing the data in Cloud Source Repositories.

Answer(s): C,E



You need to create a data pipeline that copies time-series transaction data so that it can be queried from within BigQuery by your data science team for analysis. Every hour, thousands of transactions are updated with a new status. The size of the intitial dataset is 1.5 PB, and it will grow by 3 TB per day. The data is heavily structured, and your data science team will build machine learning models based on this dat

  1. You want to maximize performance and usability for your data science team.
    Which two strategies should you adopt? Choose 2 answers.
  2. Denormalize the data as must as possible.
  3. Preserve the structure of the data as much as possible.
  4. Use BigQuery UPDATE to further reduce the size of the dataset.
  5. Develop a data pipeline where status updates are appended to BigQuery instead of updated.
  6. Copy a daily snapshot of transaction data to Cloud Storage and store it as an Avro file. Use BigQuery's support for external data sources to query.

Answer(s): A,E



You are designing a cloud-native historical data processing system to meet the following conditions:

The data being analyzed is in CSV, Avro, and PDF formats and will be accessed by multiple analysis tools including Cloud Dataproc, BigQuery, and Compute Engine.

A streaming data pipeline stores new data daily.

Peformance is not a factor in the solution.

The solution design should maximize availability.

How should you design data storage for this solution?

  1. Create a Cloud Dataproc cluster with high availability. Store the data in HDFS, and peform analysis as needed.
  2. Store the data in BigQuery. Access the data using the BigQuery Connector or Cloud Dataproc and Compute Engine.
  3. Store the data in a regional Cloud Storage bucket. Aceess the bucket directly using Cloud Dataproc, BigQuery, and Compute Engine.
  4. Store the data in a multi-regional Cloud Storage bucket. Access the data directly using Cloud Dataproc, BigQuery, and Compute Engine.

Answer(s): D



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