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Answer: Utilizing Amazon Kinesis Data Streams for data ingestion and AWS Lambda for real-time data analysis.
The correct answer is A: Utilizing Amazon Kinesis Data Streams for data ingestion and AWS Lambda for real-time data analysis. This combination provides the most operational efficiency for real-time analysis because Amazon Kinesis Data Streams is designed for high-throughput data ingestion, and AWS Lambda can process the data in real time without managing servers. Options B and C involve Amazon Kinesis Data Analytics, which can be used for real-time analysis but might not be as operationally efficient as using AWS Lambda directly with Kinesis Data Streams. Option D involves Amazon API Gateway, which is more suited for exposing APIs rather than efficiently ingesting and processing high-frequency data streams.
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How can a company achieve operational efficiency in real-time analysis and ingestion of customer payment data into an Amazon S3 data lake, given an average data reception rate of once per minute?
A
Utilizing Amazon Kinesis Data Streams for data ingestion and AWS Lambda for real-time data analysis.
B
Employing AWS Glue for data ingestion and Amazon Kinesis Data Analytics for real-time analysis.
C
Leveraging Amazon Kinesis Data Firehose for data ingestion and Amazon Kinesis Data Analytics for real-time analysis.
D
Using Amazon API Gateway for data ingestion and AWS Lambda for real-time data analysis.