You work as a machine learning specialist for a hedge fund firm.
Your traders trade in highly volatile securities and derivatives.
In real-time, their trading activity must be monitored for an anomalous activity to keep the firm from entering into potentially very large high-risk transactions that could jeopardize the firm's valuation and collateral obligations with the Securities and Exchange Commission (SEC)
Which of the following options best describes your optimal machine learning solution to this problem?
Click on the arrows to vote for the correct answer
A. B. C. D.Correct Answer: C.
Option A is incorrect.
Kinesis Data Streams will satisfy your real-time data gathering requirement.
The SageMaker Random Cut Forest algorithm will satisfy your anomaly detection requirement.
However, using SQL in your Kinesis Data Analytics application will require a Lambda function to write your data to S3.
Option B is incorrect.
Kinesis Data Firehose will deliver your data in near real-time, not real-time like Kinesis Data Streams.
Also, using SQL in your Kinesis Data Analytics application will require a Lambda function to write your data to S3
Finally, the k-means algorithm is not the best choice for anomaly detection.
Random Cut Forest is the best algorithm for anomaly detection.
Option C is correct.
Kinesis Data Streams will satisfy your real-time data gathering requirement.
Kinesis Data Analytics running an Apache Flink application will allow you to transform your data and directly write it to S3
Finally, the Random Cut Forest algorithm is the best choice for anomaly detection.
Option D is incorrect.
A Glue ETL job will not satisfy your real-time data delivery requirement.
Glue ETL is used in batch applications.
References:
Please see the US Securities and Exchange Commission compliance alert dated July, 2008 (https://www.sec.gov/about/offices/ocie/complialert0708.htm),
The Amazon Kinesis Data Analytics developer guide titled Example: Writing to an Amazon S3 Bucket (https://docs.aws.amazon.com/kinesisanalytics/latest/java/examples-s3.html),
The Amazon SageMaker developer guide titled Random Cut Forest (RCF) Algorithm (https://docs.aws.amazon.com/sagemaker/latest/dg/randomcutforest.html)
The optimal machine learning solution for this problem would be to use Kinesis Data Streams to gather the trading, valuation, and collateral data from the investment management systems, source the data from Kinesis Data Streams to Kinesis Data Analytics, use Apache Flink to transform the data and write it to S3, and use the SageMaker Random Cut Forest built-in algorithm to detect anomalous trading activity.
Option A is close to the correct answer, but it uses SQL to transform the data and write it to S3, which may not be optimal for real-time data processing. SQL is good for querying and analyzing data, but it may not be the best option for real-time data transformation.
Option B uses Kinesis Data Firehose instead of Kinesis Data Streams, which is not ideal for real-time data processing. Kinesis Data Firehose is designed to load data into destinations like S3, Redshift, or Elasticsearch. It is not suitable for real-time data transformation or analysis. Additionally, the SageMaker k-means algorithm is not well suited for detecting anomalous trading activity.
Option C is almost identical to option A, but it replaces SQL with Apache Flink. Apache Flink is a stream processing framework that can handle real-time data processing and complex data transformations. It is more suitable than SQL for real-time data processing.
Option D uses Glue ETL job to transform the data and write it to S3. Glue ETL is a batch processing service and is not ideal for real-time data processing. Additionally, using batch processing may not be appropriate for monitoring trading activity in real-time.
The SageMaker Random Cut Forest algorithm is well suited for detecting anomalous trading activity. It is a scalable unsupervised learning algorithm that can detect anomalies in real-time data streams. It can handle high-dimensional data and can be trained and deployed using Amazon SageMaker.
In summary, the optimal solution is to use Kinesis Data Streams to gather real-time data, source the data to Kinesis Data Analytics, use Apache Flink to transform the data and write it to S3, and use the SageMaker Random Cut Forest built-in algorithm to detect anomalous trading activity.