Question 290 of 307
Single answerYou 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?
AGroup the data by using a tumbling window in a Dataflow pipeline, and write the aggregated data to Memorystore.
✓BGroup the data by using a hopping window in a Dataflow pipeline, and write the aggregated data to Memorystore.
CGroup the data by using a session window in a Dataflow pipeline, and write the aggregated data to BigQuery.
DGroup the data by using a hopping window in a Dataflow pipeline, and write the aggregated data to BigQuery.
✓
Correct Answer: B
Group the data by using a hopping window in a Dataflow pipeline, and write the aggregated data to Memorystore.
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Explanation
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