AWS Certified Machine Learning Engineer – Associate (MLA-C01) — Question 30

A financial company receives a high volume of real-time market data streams from an external provider. The streams consist of thousands of JSON records every second.
The company needs to implement a scalable solution on AWS to identify anomalous data points.
Which solution will meet these requirements with the LEAST operational overhead?

Answer options

• A. Ingest real-time data into Amazon Kinesis data streams. Use the built-in RANDOM_CUT_FOREST function in Amazon Managed Service for Apache Flink to process the data streams and to detect data anomalies.
• B. Ingest real-time data into Amazon Kinesis data streams. Deploy an Amazon SageMaker endpoint for real-time outlier detection. Create an AWS Lambda function to detect anomalies. Use the data streams to invoke the Lambda function.
• C. Ingest real-time data into Apache Kafka on Amazon EC2 instances. Deploy an Amazon SageMaker endpoint for real-time outlier detection. Create an AWS Lambda function to detect anomalies. Use the data streams to invoke the Lambda function.
• D. Send real-time data to an Amazon Simple Queue Service (Amazon SQS) FIFO queue. Create an AWS Lambda function to consume the queue messages. Program the Lambda function to start an AWS Glue extract, transform, and load (ETL) job for batch processing and anomaly detection.

Correct answer: A

Explanation

Option A is the best choice because it leverages Amazon Kinesis data streams and the RANDOM_CUT_FOREST function in Amazon Managed Service for Apache Flink, which is specifically designed for real-time anomaly detection with minimal operational management. Options B and C involve additional components like SageMaker and Lambda, increasing complexity, while option D relies on batch processing with AWS Glue, which is not suited for real-time requirements.