Free Google Professional-Data-Engineer Exam Dumps Questions & Answers

You are designing a real-time system for a ride hailing app that identifies areas with high demand for rides to effectively reroute available drivers to meet the demand. The system ingests data from multiple sources to Pub/Sub, processes the data, and stores the results for visualization and analysis in real-time dashboards. The data sources include driver location updates every 5 seconds and app-based booking events from riders. The data processing involves real-time aggregation of supply and demand data for the last 30 seconds, every 2 seconds, and storing the results in a low-latency system for visualization. What should you do?

• A.Group the data by using a session window in a Dataflow pipeline, and write the aggregated data to BigQuery.
• B.Group the data by using a hopping window in a Dataflow pipeline, and write the aggregated data to Memorystore.
• C.Group the data by using a hopping window in a Dataflow pipeline, and write the aggregated data to BigQuery.
• D.Group the data by using a tumbling window in a Dataflow pipeline, and write the aggregated data to Memorystore.

If a dataset contains rows with individual people and columns for year of birth, country, and income, how many of the columns are continuous and how many are categorical?

• A.1 continuous and 2 categorical
• B.3 categorical
• C.2 continuous and 1 categorical
• D.3 continuous

You maintain ETL pipelines. You notice that a streaming pipeline running on Dataflow is taking a long time to process incoming data, which causes output delays. You also noticed that the pipeline graph was automatically optimized by Dataflow and merged into one step. You want to identify where the potential bottleneck is occurring. What should you do?

• A.Verify that the Dataflow service accounts have appropriate permissions to write the processed data to the output sinks.
• B.Insert output sinks after each key processing step, and observe the writing throughput of each block.
• C.Log debug information in each ParDo function, and analyze the logs at execution time.
• D.Insert a Reshuffle operation after each processing step, and monitor the execution details in the Dataflow console.

You are updating the code for a subscriber to a Pub/Sub feed. You are concerned that upon deployment the subscriber may erroneously acknowledge messages, leading to message loss.
Your subscriber is not set up to retain acknowledged messages. What should you do to ensure that you can recover from errors after deployment?

• A.Create a Pub/Sub snapshot before deploying new subscriber code. Use a Seek operation to re- deliver messages that became available after the snapshot was created.
• B.Set up the Pub/Sub emulator on your local machine. Validate the behavior of your new subscriber logic before deploying it to production.
• C.Enable dead-lettering on the Pub/Sub topic to capture messages that aren't successfully acknowledged. If an error occurs after deployment, re-deliver any messages captured by the dead-letter queue.
• D.Use Cloud Build for your deployment. If an error occurs after deployment, use a Seek operation to locate a timestamp logged by Cloud Build at the start of the deployment.

Your company is migrating their 30-node Apache Hadoop cluster to the cloud. They want to re- use Hadoop jobs they have already created and minimize the management of the cluster as much as possible. They also want to be able to persist data beyond the life of the cluster. What should you do?

• A.Create a Hadoop cluster on Google Compute Engine that uses persistent disks.
• B.Create a Google Cloud Dataflow job to process the data.
• C.Create a Google Cloud Dataproc cluster that uses persistent disks for HDFS.
• D.Create a Hadoop cluster on Google Compute Engine that uses Local SSD disks.
• E.Create a Cloud Dataproc cluster that uses the Google Cloud Storage connector.