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MLS-C01 · Question #225

A company has a podcast platform that has thousands of users. The company has implemented an anomaly detection algorithm to detect low podcast engagement based on a 10-minute running window of user…

The correct answer is C. Ingest event data by using Amazon Kinesis Data Streams. Use an Amazon Kinesis Data. For real-time ingestion of high-volume user event data, applying small transformations, and performing anomaly detection within a running window with minimal operational overhead, Amazon Kinesis Data Streams and Kinesis Data Analytics are the best fit.

Data Engineering

Question

A company has a podcast platform that has thousands of users. The company has implemented an anomaly detection algorithm to detect low podcast engagement based on a 10-minute running window of user events such as listening, pausing, and exiting the podcast. A machine learning (ML) specialist is designing the data ingestion of these events with the knowledge that the event payload needs some small transformations before inference. How should the ML specialist design the data ingestion to meet these requirements with the LEAST operational overhead?

Options

  • AIngest event data by using a GraphQLAPI in AWS AppSync. Store the data in an Amazon
  • BIngest event data by using Amazon Kinesis Data Streams. Store the data in Amazon S3 by using
  • CIngest event data by using Amazon Kinesis Data Streams. Use an Amazon Kinesis Data
  • DIngest event data by using Amazon Managed Streaming for Apache Kafka (Amazon MSK). Use

How the community answered

(38 responses)
  • A
    13% (5)
  • B
    3% (1)
  • C
    76% (29)
  • D
    8% (3)

Why each option

For real-time ingestion of high-volume user event data, applying small transformations, and performing anomaly detection within a running window with minimal operational overhead, Amazon Kinesis Data Streams and Kinesis Data Analytics are the best fit.

AIngest event data by using a GraphQLAPI in AWS AppSync. Store the data in an Amazon

AWS AppSync with GraphQL is for building flexible APIs, but storing data in Amazon S3 would require additional services for real-time running window analysis, adding complexity not directly provided by AppSync.

BIngest event data by using Amazon Kinesis Data Streams. Store the data in Amazon S3 by using

While Kinesis Data Streams can ingest data, storing it in Amazon S3 via Kinesis Data Firehose captures raw data but does not inherently provide the real-time running window transformations and anomaly detection capabilities without additional processing services.

CIngest event data by using Amazon Kinesis Data Streams. Use an Amazon Kinesis DataCorrect

Amazon Kinesis Data Streams effectively ingests high-velocity streaming data, and Amazon Kinesis Data Analytics can then consume this stream in a serverless manner. Kinesis Data Analytics allows performing necessary transformations and aggregations over a running window (like 10 minutes) using SQL or Apache Flink, providing the required real-time analysis with minimal operational overhead.

DIngest event data by using Amazon Managed Streaming for Apache Kafka (Amazon MSK). Use

Amazon Managed Streaming for Apache Kafka (Amazon MSK) is a managed Kafka service, but performing running window transformations would still require a separate processing application (e.g., Flink, Spark Streaming), potentially adding more operational overhead than serverless Kinesis Data Analytics.

Concept tested: Real-time streaming data processing and analytics

Source: https://docs.aws.amazon.com/kinesisanalytics/latest/dg/what-is.html

Topics

#Data Ingestion#Streaming Data#Kinesis#Data Transformation

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