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Answer: Use Amazon Kinesis for data ingestion and processing, and Amazon SageMaker for real-time data analysis
In this scenario, using Amazon Kinesis for data ingestion and processing, and Amazon SageMaker for real-time data analysis would be the most appropriate choice. Amazon Kinesis can handle real-time data ingestion and processing, while Amazon SageMaker provides a fully managed machine learning service that can perform real-time data analysis. By combining these services, you can build a data ingestion pipeline that supports real-time data processing and analysis for machine learning applications.
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Your company is building a data ingestion pipeline for a machine learning application that requires real-time data processing and analysis. Which AWS service would you recommend for this scenario, and how would you design the pipeline to support real-time data processing and analysis?
A
Use Amazon S3 for data storage and Amazon SageMaker for real-time data processing and analysis
B
Use Amazon Kinesis for data ingestion and processing, and Amazon SageMaker for real-time data analysis
C
Use Amazon DynamoDB for data storage and AWS Lambda for real-time data processing and analysis
D
Use Amazon Redshift for data storage and Amazon SageMaker for real-time data analysis
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