Examtopics

AWS Certified Machine Learning - Specialty

  • Topic 1 Question 277

    An exercise analytics company wants to predict running speeds for its customers by using a dataset that contains multiple health-related features for each customer. Some of the features originate from sensors that provide extremely noisy values.

    The company is training a regression model by using the built-in Amazon SageMaker linear learner algorithm to predict the running speeds. While the company is training the model, a data scientist observes that the training loss decreases to almost zero, but validation loss increases.

    Which technique should the data scientist use to optimally fit the model?

    • Add L1 regularization to the linear learner regression model.

    • Perform a principal component analysis (PCA) on the dataset. Use the linear learner regression model.

    • Perform feature engineering by including quadratic and cubic terms. Train the linear learner regression model.

    • Add L2 regularization to the linear learner regression model.

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