DEA-C01 exam dumps

DEA-C01 practice question 22 of 550

AWS Certified Data Engineer - Associate. Associate level, Amazon Web Services. Free question with the correct answer and a full explanation.

DEA-C01 Question 22

Select 3

You are tasked with designing a real-time data ingestion pipeline for a stock trading application that processes millions of transactions per second. The application requires low-latency processing and the ability to replay data in case of failures. Which combination of services should you use to achieve this?

  1. A

    Amazon Kinesis Data Streams for ingestion and AWS Lambda for processing

  2. B

    Amazon S3 for ingestion and Amazon Athena for querying

  3. C

    Amazon Kinesis Data Streams for ingestion and Amazon Kinesis Data Analytics for real-time processing

  4. D

    Amazon DynamoDB Streams for ingestion and AWS Glue for processing

  5. E

    Amazon Managed Streaming for Apache Kafka (Amazon MSK) for ingestion and Apache Flink for processing

Show answer and explanation

Correct answers: A, C, E

Explanation

For real-time, low-latency data ingestion and processing, services like Amazon Kinesis Data Streams and Amazon MSK are ideal for handling high-throughput streaming data. They also support replaying records, which meets the application's requirements. AWS Lambda, Amazon Kinesis Data Analytics, and Apache Flink are well-suited for real-time processing tasks. Other options, such as Amazon S3 and DynamoDB Streams, are not designed for low-latency, real-time scenarios, making them unsuitable for this use case.

  • A. Correct.

    Amazon Kinesis Data Streams is designed for high-throughput, low-latency streaming data ingestion, and AWS Lambda can process the data in real-time. This combination is suitable for real-time processing and replaying data.

  • B. Incorrect.

    Amazon S3 and Amazon Athena are not suitable for low-latency, real-time use cases as they are geared towards batch processing and querying historical data.

  • C. Correct.

    Amazon Kinesis Data Streams provides low-latency ingestion, and Amazon Kinesis Data Analytics can directly process streaming data in real-time. This is an effective combination for the given use case.

  • D. Incorrect.

    Amazon DynamoDB Streams can capture item-level changes in DynamoDB, but it is not designed for high-throughput streaming ingestion from external sources, making it unsuitable for this use case.

  • E. Correct.

    Amazon MSK is a managed service for Apache Kafka, which is highly effective for low-latency, high-throughput streaming ingestion. Apache Flink is a robust framework for real-time stream processing, making this combination suitable for the scenario.

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