Topic 1 Question 277
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?
Group the data by using a tumbling window in a Dataflow pipeline, and write the aggregated data to Memorystore.
Group the data by using a hopping window in a Dataflow pipeline, and write the aggregated data to Memorystore.
Group the data by using a session window in a Dataflow pipeline, and write the aggregated data to BigQuery.
Group the data by using a hopping window in a Dataflow pipeline, and write the aggregated data to BigQuery.
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- 正解だと思う選択肢: B
B. Group the data by using a hopping window in a Dataflow pipeline, and write the aggregated data to Memorystore.
👍 2scaenruy2024/01/03 - 正解だと思う選択肢: B
- Hopping Window: Hopping windows are fixed-sized, overlapping intervals.
- Aggregate data over the last 30 seconds, every 2 seconds, as hopping windows allow for overlapping data analysis.
- Memorystore: Ideal for low-latency access required for real-time visualization and analysis.
👍 2raaad2024/01/09
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