MLA-C01 exam dumps

MLA-C01 practice question 71 of 458

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

MLA-C01 Question 71

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A data engineering team is building a real-time recommendation system for an e-commerce platform. The system needs to process streaming clickstream data from a Kinesis Data Stream, enrich it with pre-computed user preferences stored in DynamoDB, and then send the results to another Kinesis Data Stream for downstream processing. Which combination of AWS services would best meet this requirement?

  1. A

    AWS Lambda

  2. B

    Amazon Kinesis Data Analytics

  3. C

    Apache Spark on Amazon EMR

  4. D

    Amazon SageMaker

  5. E

    Amazon Comprehend

Show answer and explanation

Correct answers: A, B, C

Explanation

To process and enrich streaming data from a Kinesis Data Stream, AWS Lambda, Amazon Kinesis Data Analytics, and Apache Spark on Amazon EMR are all suitable options. Lambda can handle real-time event-based processing and integrate with DynamoDB for enrichment. Kinesis Data Analytics is purpose-built for streaming data transformations, while Spark on EMR provides advanced capabilities for large-scale data processing. Amazon SageMaker and Amazon Comprehend are not relevant for this specific use case.

  • A. Correct.

    AWS Lambda is a great choice for processing streaming data from Amazon Kinesis Data Streams. It can be used to enrich the data with DynamoDB and send the results to another Kinesis Data Stream.

  • B. Correct.

    Amazon Kinesis Data Analytics is specifically designed for real-time analytics on streaming data. It can be used to process and transform data without requiring complex infrastructure management.

  • C. Correct.

    Apache Spark on Amazon EMR provides a scalable framework for processing large-scale streaming data. It can be integrated with Kinesis Data Streams for advanced data processing and transformations.

  • D. Incorrect.

    Amazon SageMaker is primarily designed for building, training, and deploying machine learning models, not for streaming data enrichment or transformation.

  • E. Incorrect.

    Amazon Comprehend is a natural language processing service and is not suitable for processing and enriching streaming data in this scenario.

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