Amazon Web Services Updated MLS-C01 Exam Questions and Answers by chaim

Feature selection is the process of selecting a subset of extracted features that are relevant and contribute to minimizing the error rate of a trained model. Some techniques for feature selection are:

Correlation plot with heat maps: This technique visualizes the correlation between features using a color-coded matrix. Features that are highly correlated with each other or with the target variable can be identified and removed to reduce redundancy and noise.

Univariate selection: This technique evaluates each feature individually based on a statistical test, such as chi-square, ANOVA, or mutual information, and selects the features that have the highest scores or p-values. This technique is simple and fast, but it does not consider the interactions between features.

Feature importance with a tree-based classifier: This technique uses a tree-based classifier, such as random forest or gradient boosting, to rank the features based on their importance in splitting the nodes. Features that have low importance scores can be dropped from the model. This technique can capture the non-linear relationships and interactions between features.

The other options are not techniques for feature selection, but rather for feature engineering, which is the process of creating, transforming, or extracting features from the original data. Feature engineering can improve the performance and interpretability of the model, but it does not reduce the number of features.

Data scaling with standardization and normalization: This technique transforms the features to have a common scale, such as zero mean and unit variance, or a range between 0 and 1. This technique can help some algorithms, such as k-means or logistic regression, to converge faster and avoid numerical instability, but it does not change the number of features.

Data binning: This technique groups the continuous features into discrete bins or categories based on some criteria, such as equal width, equal frequency, or clustering. This technique can reduce the noise and outliers in the data, and also create ordinal or nominal features that can be used for some algorithms, such as decision trees or naive Bayes, but it does not reduce the number of features.

Data augmentation: This technique generates new data from the existing data by applying some transformations, such as rotation, flipping, cropping, or noise addition. This technique can increase the size and diversity of the data, and help prevent overfitting, but it does not reduce the number of features.

Feature engineering - Machine Learning Lens

Amazon SageMaker Autopilot now provides feature selection and the ability to change data types while creating an AutoML experiment

Feature Selection in Machine Learning | Baeldung on Computer Science

Feature Selection in Machine Learning: An easy Introduction

In linear least squares regression, the design matrix (often denoted as XXX) must have full rank to ensure a unique solution. When two or more features are perfectly linearly dependent, it leads to multicollinearity, causing the matrix XTXX^TXXTX to become singular (non-invertible). This singularity prevents the computation of a unique solution for the regression coefficients.​

From the documentation:​

Therefore, perfect linear dependence among features is problematic for linear regression models as it undermines the mathematical foundation required for deriving unique solutions.

Option A is correct because SageMaker Pipelines is a service that enables you to create and manage automated workflows for your machine learning projects. You can use SageMaker Pipelines to orchestrate the steps of data extraction, model training, and model deployment in a repeatable and scalable way1.

Option B is correct because SageMaker Model Monitor is a service that monitors the quality of your models in production and alerts you when there are deviations in the model quality. You can use SageMaker Model Monitor to set an accuracy threshold for your model and configure a CloudWatch alarm that triggers when the threshold is exceeded. You can then connect the alarm to the workflow in SageMaker Pipelines to automatically initiate retraining and deployment of a new version of the model2.

Option C is incorrect because it is not the most operationally efficient way to maintain the model’s accuracy. Creating a daily SageMaker Processing job that reads the predictions from Amazon S3 and checks for changes in model prediction accuracy is a manual and time-consuming process. It also requires you to write custom code to perform the data analysis and send the email notification. Moreover, it does not automatically retrain and deploy the model when the accuracy drops.

Option D is incorrect because it is not the most operationally efficient way to maintain the model’s accuracy. Rerunning the steps in the Jupyter notebook that is hosted on SageMaker Studio notebooks to retrain the model and redeploy a new version of the model is a manual and error-prone process. It also requires you to monitor the model’s performance and initiate the retraining and deployment steps yourself. Moreover, it does not leverage the benefits of SageMaker Pipelines and SageMaker Model Monitor to automate and streamline the workflow.

Option E is incorrect because it is not the most operationally efficient way to maintain the model’s accuracy. Exporting the training and deployment code from the SageMaker Studio notebooks into a Python script and packaging the script into an Amazon ECS task that an AWS Lambda function can initiate is a complex and cumbersome process. It also requires you to manage the infrastructure and resources for the Amazon ECS task and the AWS Lambda function. Moreover, it does not leverage the benefits of SageMaker Pipelines and SageMaker Model Monitor to automate and streamline the workflow.

1: SageMaker Pipelines - Amazon SageMaker

2: Monitor data and model quality - Amazon SageMaker

The task involves periodic inference (hourly batches of telemetry data) that can be processed in bulk, with the total processing time under 5 minutes. The most cost-effective solution for this batch prediction scenario is SageMaker Batch Transform, as it avoids the need for always-on endpoints.

“Use Batch Transform when you: … Don’t need a persistent endpoint and want to process batches of data at once. This is typically the most cost-efficient way to process large volumes of input data periodically.”

Since the use case doesn't require low latency or asynchronous real-time response, and processing takes only minutes, Batch Transform is the optimal fit.