Topic 1 Question 270
A company has a podcast platform that has thousands of users. The company implemented an algorithm to detect low podcast engagement based on a 10-minute running window of user events such as listening to, pausing, and closing the podcast. A machine learning (ML) specialist is designing the ingestion process for these events. The ML specialist needs to transform the data to prepare the data for inference.
How should the ML specialist design the transformation step to meet these requirements with the LEAST operational effort?
Use an Amazon Managed Streaming for Apache Kafka (Amazon MSK) cluster to ingest event data. Use Amazon Kinesis Data Analytics to transform the most recent 10 minutes of data before inference.
Use Amazon Kinesis Data Streams to ingest event data. Store the data in Amazon S3 by using Amazon Kinesis Data Firehose. Use AWS Lambda to transform the most recent 10 minutes of data before inference.
Use Amazon Kinesis Data Streams to ingest event data. Use Amazon Kinesis Data Analytics to transform the most recent 10 minutes of data before inference.
Use an Amazon Managed Streaming for Apache Kafka (Amazon MSK) cluster to ingest event data. Use AWS Lambda to transform the most recent 10 minutes of data before inference.
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コメント(6)
I think it's C as we are using only 2 services and it's less operational effort.
👍 1ADVIT2023/07/07- 正解だと思う選択肢: C
It’s real-time and less operational overhead
👍 1awsarchitect52023/07/24 - 正解だと思う選択肢: C
Option C also allows the ML specialist to use Amazon Kinesis Data Analytics to transform the most recent 10 minutes of data before inference. Kinesis Data Analytics is a fully managed service that enables users to analyze streaming data using SQL or Apache Flink. Kinesis Data Analytics can process streaming data in real time and generate insights, metrics, and alerts. Kinesis Data Analytics can also integrate with other AWS services, such as Lambda, S3, or SageMaker. The ML specialist can use Kinesis Data Analytics to apply SQL queries or Flink applications to transform the event data based on the 10-minute running window and prepare it for inference.
👍 1Mickey3212023/08/04
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