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Google Professional Machine Learning Engineer

Google Professional Machine Learning Engineer

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You are designing a data pipeline for a smart city project that involves aggregating sensor data collected every minute from thousands of IoT devices deployed across the city. The project requires real-time analysis to monitor traffic flow, air quality, and energy usage to make immediate adjustments. Given the need for low latency processing, scalability to handle the volume of data, and the ability to provide real-time insights, which data pipeline type would you choose? (Choose one correct option)

Real Exam

Explanation:

Correct Option: C. Real-time streaming data pipeline

Explanation:
A real-time streaming data pipeline is the most suitable for this scenario due to its ability to:

  • Process data with low latency: Essential for immediate processing and analysis as data is generated by IoT devices.
  • Provide real-time insights: Facilitates quick decision-making by offering timely data analysis, crucial for monitoring and adjusting city operations.
  • Support continuous data ingestion: Allows for uninterrupted data flow from thousands of IoT devices, ensuring scalability.

Why other options are less suitable:

  • A. Data warehousing pipeline: Best for scenarios where historical data analysis is the priority, not for real-time processing.
  • B. Batch and streaming hybrid pipeline: While versatile, may not be the most efficient choice for projects requiring purely real-time data processing.
  • D. Batch data pipeline: Involves processing data in batches, making it unsuitable for projects that rely on real-time data analysis and immediate action.
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