AWS Certified Machine Learning – Specialty — Question 58

A city wants to monitor its air quality to address the consequences of air pollution. A Machine Learning Specialist needs to forecast the air quality in parts per million of contaminates for the next 2 days in the city. As this is a prototype, only daily data from the last year is available.
Which model is MOST likely to provide the best results in Amazon SageMaker?

Answer options

• A. Use the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the single time series consisting of the full year of data with a predictor_type of regressor.
• B. Use Amazon SageMaker Random Cut Forest (RCF) on the single time series consisting of the full year of data.
• C. Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_type of regressor.
• D. Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_type of classifier.

Correct answer: C

Explanation

The correct answer is C because the Linear Learner algorithm is suitable for regression tasks, which is necessary for forecasting air quality in parts per million. Option A, kNN, is less effective for time series data, while Option B, RCF, is typically used for anomaly detection rather than forecasting. Option D incorrectly uses a classifier for a regression problem.