You work as a machine learning specialist for a city that wants to monitor air quality to address air pollution in their environment. You and your machine learning specialist team need to forecast the city air quality in parts per million of contaminants over the next week, taking into account weather, traffic conditions, and other pollutant contributors. You are building your model using daily data from the last year as your data source. Your team has decided to use SageMaker Studio to leverage its collaborative notebooks feature.
Which model and SageMaker Studio image will provide the best results for your team in the most efficient manner?

Answer options:

A.Use the SageMaker Studio Base TensorFlow [tensorflow-2.3.0] image and the k-Nearest-Neighbors algorithm on the single time series consisting of the full year of data with a predictor_type of regressor.
B.Use SageMaker Studio Base R [r-4.0.3] image and the Random Cut Forest algorithm on the single time series consisting of the full year of data.
C.Use the SageMaker Studio Base Python [python-3.6] image and the Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_type of regressor.
D.Use the SageMaker Studio Base Scala [scala-2.13.3] image and the Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_type of classifier.

Answer: C
Option A is incorrect. The problem of forecasting the air quality is a regression problem best solved by the Linear Learner built-in algorithm, not the k-Nearest Neighbors SageMaker built-in algorithm. Also, there is no Base TensorFlow image available in SageMaker Studio. You would have to create a custom image to use TensorFlow in SageMaker Studio. This would be far less efficient than using an image that is already available with SageMaker Studio.
Option B is incorrect. The problem of forecasting the air quality is a regression problem best solved by the Linear Learner built-in algorithm, not the Random Cut Forest SageMaker built-in algorithm. Also, there is no Base R image available in SageMaker Studio. You would have to create a custom image to use R in SageMaker Studio. This would be far less efficient than using an image that is already available with SageMaker Studio.
Option C is CORRECT. The Linear Learner SageMaker built-in algorithm is the best choice from the options given to solve this regression problem. Also, the SageMaker Studio Base Python [python-3.6] image is a valid choice of an available SageMaker Studio image.
Option D is incorrect. While the Linear Learner SageMaker built-in algorithm is the best choice from the options given to solve this regression problem, there is no Base Scala image available in SageMaker Studio. You would have to create a custom image to use Scala in SageMaker Studio. This would be far less efficient than using an image that is already available with SageMaker Studio.
Reference:
Please see the AWS SageMaker developer guide titled Available Amazon SageMaker Images.
Please refer to the AWS SageMaker developer guide titled Bring your own SageMaker image.