You work as a machine learning specialist for the sales department of a large web retailer that needs to gain insight into their sales patterns. They need a way to use a visualization to show their sales data in near-real time so that they can quickly recognize higher-than-expected sales of specific products. This will help your product operations quickly meet high demands. Which option is a viable, efficient solution to your problem?

Answer options:

A.Use Kinesis Data Streams to stream your data to S3. Then run a Random Cut Forest SagaMaker model on the data continuously, using the output as source data to visualization in QuickSight.
B.Use Kinesis Data Firehose to stream your data to S3. Then run a Random Cut Forest SagaMaker model on the data continuously, using the output as source data to visualization in QuickSight.
C.Use Kinesis Data Firehose to stream your data to S3. Use QuickSight ML Insights to use the output as source data to visualization in QuickSight.
D.Use Kinesis Data Firehose to stream your data to a Kinesis Data Analytics application that runs a Random Cut Forest SagaMaker model on the data continuously, writing the output to S3 which is then used as source data to visualization in QuickSight.

Correct Answer: C
Option A is incorrect. Kinesis Data Streams cannot stream your data directly to S3. Also, running your own SageMaker Random Cut Forest model against your data is much less efficient than using the QuickSight ML Insights integrated Random Cut Forest capability.
Option B is incorrect. While Kinesis Data Firehose can stream your data directly to S3, running your own SageMaker Random Cut Forest model against your data is much less efficient than using the QuickSight ML Insights integrated Random Cut Forest capability.
Option C is correct. Streaming your data directly to S3 using Kinesis Data Firehose is very efficient. Also, using QuickSight’s integrated ML Insights Random Cut Forest capability requires far less development and coding effort than the other options.
Option D is incorrect. Kinesis Data Analytics has a Random Cut Forest capability that you can use to detect your sales outliers. However, you would still have to build your visualization in QuickSight. The option of using ML Insights directly within QuickSight allows you to run your anomaly detection and visualize your data more quickly.
References:
Please see the Amazon QuickSight user guide titled Working with ML Insights (https://docs.aws.amazon.com/quicksight/latest/user/making-data-driven-decisions-with-ml-in-quicksight.html),
The Amazon QuickSight user guide titled Detecting Outliers with ML-Powered Anomaly Detection (https://docs.aws.amazon.com/quicksight/latest/user/anomaly-detection.html),
The AWS Machine Learning blog titled Visualizing Amazon SageMaker machine learning predictions with Amazon QuickSight (https://aws.amazon.com/blogs/machine-learning/making-machine-learning-predictions-in-amazon-quicksight-and-amazon-sagemaker/),
The Amazon Kinesis Data Analytics SQL reference titled RANDOM_CUT_FOREST (https://docs.aws.amazon.com/kinesisanalytics/latest/sqlref/sqlrf-random-cut-forest.html)