Professional Data Engineer on Google Cloud Platform Dataflow Practice Questions

You are designing a basket abandonment system for an ecommerce company. The system will send a message to a user based on these rules: No interaction by the user on the site for 1 hour Has added more than $30 worth of products to the basket Has not completed a transaction You use Google Cloud Dataflow to process the data and decide if a message should be sent. How should you design the pipeline?

• AA. Use a fixed-time window with a duration of 60 minutes.
• BB. Use a sliding time window with a duration of 60 minutes.
• CC. Use a session window with a gap time duration of 60 minutes.
• DD. Use a global window with a time based trigger with a delay of 60 minutes.

You have Google Cloud Dataflow streaming pipeline running with a Google Cloud Pub/Sub subscription as the source. You need to make an update to the code that will make the new Cloud Dataflow pipeline incompatible with the current version. You do not want to lose any data when making this update. What should you do?

• AA. Update the current pipeline and use the drain flag.
• BB. Update the current pipeline and provide the transform mapping JSON object.
• CC. Create a new pipeline that has the same Cloud Pub/Sub subscription and cancel the old pipeline.
• DD. Create a new pipeline that has a new Cloud Pub/Sub subscription and cancel the old pipeline.

You are working on a sensitive project involving private user data. You have set up a project on Google Cloud Platform to house your work internally. An external consultant is going to assist with coding a complex transformation in a Google Cloud Dataflow pipeline for your project. How should you maintain users’ privacy?

• AA. Grant the consultant the Viewer role on the project.
• BB. Grant the consultant the Cloud Dataflow Developer role on the project.
• CC. Create a service account and allow the consultant to log on with it.
• DD. Create an anonymized sample of the data for the consultant to work with in a different project.

Your company is running their first dynamic campaign, serving different offers by analyzing real-time data during the holiday season. The data scientists are collecting terabytes of data that rapidly grows every hour during their 30-day campaign. They are using Google Cloud Dataflow to preprocess the data and collect the feature (signals) data that is needed for the machine learning model in Google Cloud Bigtable. The team is observing suboptimal performance with reads and writes of their initial load of 10 TB of data. They want to improve this performance while minimizing cost. What should they do?

• AA. Redefine the schema by evenly distributing reads and writes across the row space of the table.
• BB. The performance issue should be resolved over time as the site of the BigDate cluster is increased.
• CC. Redesign the schema to use a single row key to identify values that need to be updated frequently in the cluster.
• DD. Redesign the schema to use row keys based on numeric IDs that increase sequentially per user viewing the offers.

Your company is performing data preprocessing for a learning algorithm in Google Cloud Dataflow. Numerous data logs are being are being generated during this step, and the team wants to analyze them. Due to the dynamic nature of the campaign, the data is growing exponentially every hour. The data scientists have written the following code to read the data for a new key features in the logs. BigQueryIO.Read .named(“ReadLogData”) .from(“clouddataflow-readonly:samples.log_data”) You want to improve the performance of this data read. What should you do?

• AA. Specify the TableReference object in the code.
• BB. Use .fromQuery operation to read specific fields from the table.
• CC. Use of both the Google BigQuery TableSchema and TableFieldSchema classes.
• DD. Call a transform that returns TableRow objects, where each element in the PCollexction represents a single row in the table.