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Latest Professional-Machine-Learning-Engineer Test Objectives -

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    NEW QUESTION 54
    A Machine Learning Specialist working for an online fashion company wants to build a data ingestion solution for the company's Amazon S3-based data lake.
    The Specialist wants to create a set of ingestion mechanisms that will enable future capabilities comprised of:
    * Real-time analytics
    * Interactive analytics of historical data
    * Clickstream analytics
    * Product recommendations
    Which services should the Specialist use?

    • A. Amazon Athena as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon DynamoDB streams for clickstream analytics; AWS Glue to generate personalized product recommendations
    • B. Amazon Athena as the data catalog: Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for near-real-time data insights; Amazon Kinesis Data Firehose for clickstream analytics; AWS Glue to generate personalized product recommendations
    • C. AWS Glue as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations
    • D. AWS Glue as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for real- time data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations

    Answer: D

     

    NEW QUESTION 55
    You are an ML engineer at a large grocery retailer with stores in multiple regions. You have been asked to create an inventory prediction model. Your models features include region, location, historical demand, and seasonal popularity. You want the algorithm to learn from new inventory data on a daily basis. Which algorithms should you use to build the model?

    • A. Recurrent Neural Networks (RNN)
    • B. Convolutional Neural Networks (CNN)
    • C. Classification
    • D. Reinforcement Learning

    Answer: A

    Explanation:
    "algorithm to learn from new inventory data on a daily basis" = time series model , best option to deal with time series is forsure RNN
    https://builtin.com/data-science/recurrent-neural-networks-and-lstm

     

    NEW QUESTION 56
    A financial services company is building a robust serverless data lake on Amazon S3. The data lake should be flexible and meet the following requirements:
    * Support querying old and new data on Amazon S3 through Amazon Athena and Amazon Redshift Spectrum.
    * Support event-driven ETL pipelines
    * Provide a quick and easy way to understand metadata
    Which approach meets these requirements?

    • A. Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Batch job, and an AWS Glue Data Catalog to search and discover metadata.
    • B. Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Glue ETL job, and an AWS Glue Data catalog to search and discover metadata.
    • C. Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Glue ETL job, and an external Apache Hive metastore to search and discover metadata.
    • D. Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Batch job, and an external Apache Hive metastore to search and discover metadata.

    Answer: B

     

    NEW QUESTION 57
    ......