You work as a machine learning specialist for a political candidate that is mounting a campaign to get reelected in her US senate district. Your job is to build a machine learning model that allows the campaign to understand how to reach groups of similar counties by highlighting messages that resonate with those groups. The senate candidate has a limited budget. So, you need to build a cost-effective solution. Which machine learning services and features should you use to solve this problem?

Answer options:

A.Gather US anonymized census data from the US census on demographics by different US counties using the US Census Bureau Data API and stream it to Kinesis Data Streams. Write a Kinesis Client Library application to perform feature engineering on the data and write it to S3. Then use the Factorization Machine SageMaker built-in algorithm to produce the similar counties analysis to be used in the advertising for the grouped counties.
B.Gather US anonymized census data from the US census on demographics by different US counties using the US Census Bureau Data API and stream it to Kinesis Data Firehose. Use the Kinesis Data Firehose Lambda blueprints to create your Lambda function to use as transformations to perform feature engineering on the data and write it to S3. Then use the K-Means SageMaker built-in algorithm to produce the similar counties analysis to be used in the advertising for the grouped counties.
C.Gather US anonymized census data from the US census on demographics by different US counties using the US Census Bureau Data API and process the data using a Glue ETL job. Have the Glue ETL job transformation the data to perform feature engineering on the data and write it to S3. Then use the K-Nearest Neighbors SageMaker built-in algorithm to produce the similar counties analysis to be used in the advertising for the grouped counties.
D.Gather US anonymized census data from the US census on demographics by different US counties using the US Census Bureau Data API and stream it to Kinesis Data Firehose. Use the Kinesis Data Firehose Lambda blueprints to create your Lambda function to use as transformations to perform feature engineering on the data and write it to S3. Then use the K-Nearest Neighbors SageMaker built-in algorithm to produce the similar counties analysis to be used in the advertising for the grouped counties.

Correct Answer: B
Option A is incorrect. Using Kinesis Data Streams and writing a Kinesis Data Streams Client Library application will be more costly than using Kinesis Data Firehose and its Lambda blueprint capability. Also, the Factorization Machines algorithm is not used for clustering groups as this scenario requires.
Option B is correct. Kinesis Data Firehose and its Lambda blueprints capability allow you to create the data gathering part of your machine learning solution at a lower cost than the other options. Also, the K-Means algorithm is the best algorithm to use for clustering of groups, as this scenario requires.
Option C is incorrect. You could use a Glue ETL job to gather the census data from the API. However, the K-Nearest Neighbors algorithm is not a good choice for the clustering of groups as this scenario requires.
Option D is incorrect. Kinesis Data Firehose and its Lambda blueprints capability allow you to create the data gathering part of your machine learning solution at a lower cost than the other options. However, the K-Nearest Neighbors algorithm is not a good choice for the clustering of groups as this scenario requires.
References:
Please see the Amazon SageMaker developer guide titled K-Means Algorithm (https://docs.aws.amazon.com/sagemaker/latest/dg/k-means.html),
The Amazon SageMaker developer guide titled K-Nearest Neighbors (k-NN) Algorithm (https://docs.aws.amazon.com/sagemaker/latest/dg/k-nearest-neighbors.html),
Amazon SageMaker Examples GitHub repository titled Analyze US census data for population segmentation using Amazon SageMaker (https://github.com/aws/amazon-sagemaker-examples/blob/master/introduction_to_applying_machine_learning/US-census_population_segmentation_PCA_Kmeans/sagemaker-countycensusclustering.ipynb),
The Amazon SageMaker developer guide titled Factorization Machines Algorithm (https://docs.aws.amazon.com/sagemaker/latest/dg/fact-machines.html),
The Amazon Kinesis Data Firehose developer guide titled Amazon Kinesis Data Firehose Data Transformation (https://docs.aws.amazon.com/firehose/latest/dev/data-transformation.html)