You work as a machine learning specialist for a scientific research lab that analyzes fossils found in geological research digs worldwide. You are currently working on a project that is analyzing bones of ancient mammals found during archaeological excavations in Africa. The data that the archaeologists provide to you for each specimen is exact for measurements, density, skeleton structural component, etc. Your SageMaker Linear Learner model predicts the age of the specimen based on the collected data. This data needs to be sanitized and categorized before you can feed it into your inference engine to get the estimated age. You are using the SageMaker built-in Scikit-learn library to do your data preprocessing. You need to transform categorical values such as skeletal components (femur, skull, rib cage, etc.) into numerical values. You also need to replace missing values with meaningful estimates. Which Scikit-learn library methods should you use to perform these data preprocessing tasks? (Select TWO)?

Answer options:

A.Normalizer
B.StandardScaler
C.SimpleImputer
D.Binarizer
E.OneHotEncoder

Correct Answers: C and E
Option A is incorrect. The Scikit-learn Normalizer normalizes values to a unit norm. You need to transform categorical values into numerical representations, and you need to replace missing values.
Option B is incorrect. The Scikit-learn Standardizer standardizes values to a unit norm. You need to transform categorical values into numerical representations, and you need to replace missing values.
Option C is correct. The SimpleImputer completes or estimates missing values. This is one of the two sanitation tasks you need to perform.
Option D is incorrect. The Scikit-learn Binarizer sets feature values to 0 or 1 according to a threshold. You need to transform categorical values into numerical values that can represent many different categories, and you need to replace missing values.
Option E is correct. The OneHotEncoder encodes categorical features into a one-hot numeric array with each entry in the array representing a category. There are as many entries in the array as there are categories in the feature. The ‘one’ in a given array element represents a categorical value numerically.
References:
Please see the AWS Machine Learning blog titled Preprocess input data before making predictions using Amazon SageMaker inference pipelines and Scikit-learn (https://aws.amazon.com/blogs/machine-learning/preprocess-input-data-before-making-predictions-using-amazon-sagemaker-inference-pipelines-and-scikit-learn/),
The Amazon SageMaker Examples titled Inference Pipeline with Scikit-learn and Linear Learner (https://github.com/aws/amazon-sagemaker-examples/blob/master/sagemaker-python-sdk/scikit_learn_inference_pipeline/Inference%20Pipeline%20with%20Scikit-learn%20and%20Linear%20Learner.ipynb),
Amazon SageMaker developer guide titled Use Scikit-learn with Amazon SageMaker (https://docs.aws.amazon.com/sagemaker/latest/dg/sklearn.html),
Scikit-learn API page titled sklearn.preprocessing.OneHotEncoder (https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html),
Scikit-learn API page titled sklearn.impute.SimpleImputer (https://scikit-learn.org/stable/modules/generated/sklearn.impute.SimpleImputer.html),
Scikit-learn API page titled API Reference (https://scikit-learn.org/stable/modules/classes.html)