Professional Cloud Architect · Free Practice Question Hard
Question 6
A company is looking to process real-time data from multiple sources in order to provide real-time analytics to their clients. The company is expecting to process a large volume of data with an estimated peak of up to 1 million requests per second. The solution must be able to scale elastically to handle varying levels of incoming data, ensure low latency processing, and support the processing of data in real-time. Which of the following options would be the most effective approach to meet these requirements?
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A
Use Cloud Pub/Sub to ingest the data and feed it into Cloud Dataflow for real-time processing.
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B
Use Cloud Dataflow with Apache Beam to process the data in batch mode and store the results in Cloud Bigtable.
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C
Use Cloud Dataflow to ingest the data and feed it into Cloud Bigtable for real-time processing.
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D
Use Cloud Dataproc with Apache Spark Streaming to process the data in real-time.
Reveal correct answer
Correct answer: A
A.
It is the most effective approach for this scenario. Cloud Pub/Sub is a fully-managed real-time messaging service that can handle high throughput and automatically scales to meet the incoming data demands. Cloud Dataflow can be used to process the data in real-time and provides low-latency processing capabilities. Additionally, Cloud Dataflow can easily scale up or down to handle varying volumes of incoming data.
B.
It is not an effective approach for processing real-time data. Cloud Dataflow is designed for batch processing and cannot provide low-latency processing capabilities.
C.
It is not an effective approach for processing real-time data. Cloud Dataflow is designed for batch processing and cannot provide low-latency processing capabilities. Additionally, Cloud Bigtable is a NoSQL database designed for high-throughput and high-scalability of large amounts of data, but may not be an ideal choice for real-time processing of data.
D.
It is a valid option for processing real-time data, but may not be the most effective approach for this particular scenario. While Cloud Dataproc is highly scalable and can support processing of real-time data, the company is expecting to process a large volume of data, which may not be efficiently processed using Apache Spark Streaming in a single cluster.
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