MLA-C01 Question 32
Select 3A data science team is building a real-time fraud detection system for an e-commerce platform. Transaction data needs to be ingested in real-time, processed with low latency, and stored for further analysis. The platform must handle high throughput and scale dynamically. Which AWS services should the team use to achieve this?
- A
Amazon Kinesis Data Streams for real-time data ingestion
- B
Amazon S3 for real-time data ingestion
- C
AWS Lambda for processing incoming data streams
- D
Amazon Kinesis Data Analytics for real-time analysis
- E
Amazon Athena for streaming data processing
Show answer and explanation
Correct answers: A, C, D
Explanation
To build a real-time fraud detection system, the team needs a scalable and low-latency solution for ingesting, processing, and analyzing streaming data. Amazon Kinesis Data Streams handles high-throughput real-time ingestion. AWS Lambda processes incoming data streams with low latency, and Amazon Kinesis Data Analytics enables real-time analytics. Amazon S3 and Amazon Athena are not suitable for this use case as they do not support real-time data ingestion or processing.
- A. Correct.
Amazon Kinesis Data Streams is specifically designed for real-time, high-throughput data ingestion and is a suitable choice for this scenario.
- B. Incorrect.
Amazon S3 is a storage solution, not a streaming data ingestion service, and is unsuitable for real-time ingestion.
- C. Correct.
AWS Lambda can process the incoming data streams in real-time and is highly scalable, making it a good fit for low-latency processing.
- D. Correct.
Amazon Kinesis Data Analytics allows for real-time analytics on streaming data, which aligns with the team's requirements for low-latency analysis.
- E. Incorrect.
Amazon Athena is not designed for streaming data processing. It is better suited for querying static or semi-static data stored in formats like Parquet in S3.