You are a machine learning specialist working for a state government water safety department. The state needs to monitor water quality across all of its counties to ensure water contamination levels remain within acceptable thresholds. Your machine learning team is responsible for producing a forecasting report of water contaminants in parts per million for the next month, every month, across the state. Your team has daily data from the last year available as a starting point for your model.
Which SageMaker model will give you the best results for your monthly forecasting report?

Answer options:

A.Use multiple time series of the full previous year of data as your input to a SageMaker Linear Learner built-in algorithm with a predictor_type of the classifier.
B.Use a single time series of the full previous year of data as your input to a SageMaker Linear Learner built-in algorithm with a predictor_type of the regressor.
C.Use a single time series of the full previous year of data as your input to a SageMaker Random Cut Forest (RCF) built-in algorithm.
D.Use multiple time series of the full previous year of data as your input to a SageMaker k-Nearest-Neighbors (kNN) built-in algorithm with a predictor_type of the classifier.

Correct Answer: B
Option A is incorrect. This problem requires a regression algorithm since we solve a real number (continuous) prediction value or label: water contaminants in parts per million. We are not producing a classification of unacceptable versus acceptable. Also, we should use a single time series for this type of regression, not multiple time series.
Option B is correct. This option is correct since the problem requires a regression algorithm because we solve a real number (continuous) prediction value or label: water contaminants in parts per million. The SageMaker Linear Learner algorithm is one of the go-to algorithms for regression on the Machine Learning exam.
Option C is incorrect. This option is incorrect because the Random Cut Forest algorithm is primarily used as an unsupervised algorithm for detecting anomalous data points within a data set. You would not use an RCF algorithm to solve a regression problem.
Option D is incorrect. This option is incorrect because while you can use the kNN algorithm for regression problems, this option states the use of a predictor_type of the classifier. This problem requires a regression algorithm. So you would need to use a predictor_type of the regressor. Also, we should use a single time series for this type of regression, not multiple time series.
References:
Please see the Amazon SageMaker developer guide titled K-Nearest Neighbors (k-NN) Algorithm (https://docs.aws.amazon.com/sagemaker/latest/dg/k-nearest-neighbors.html),
The Amazon SageMaker developer guide titled Linear Learner Algorithm (https://docs.aws.amazon.com/sagemaker/latest/dg/linear-learner.html),
The Amazon SageMaker developer guide titled Random Cut Forest (RCF) Algorithm (https://docs.aws.amazon.com/sagemaker/latest/dg/randomcutforest.html)