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

To log Amazon SageMaker API calls, the team can use AWS CloudTrail, which is a service that provides a record of actions taken by a user, role, or an AWS service in SageMaker1. CloudTrail captures all API calls for SageMaker, with the exception of InvokeEndpoint and InvokeEndpointAsync, as events1. The calls captured include calls from the SageMaker console and code calls to the SageMaker API operations1. The team can create a trail to enable continuous delivery of CloudTrail events to an Amazon S3 bucket, and configure other AWS services to further analyze and act upon the event data collected in CloudTrail logs1. The auditors can view the CloudTrail log activity report in the CloudTrail console or download the log files from the S3 bucket1.

To receive a notification when the model is overfitting, the team can add code to push a custom metric to Amazon CloudWatch, which is a service that provides monitoring and observability for AWS resources and applications2. The team can use the MXNet metric API to define and compute the custom metric, such as the validation accuracy or the validation loss, and use the boto3 CloudWatch client to put the metric data to CloudWatch3 . The team can then create an alarm in CloudWatch with Amazon SNS to receive a notification when the custom metric crosses a threshold that indicates overfitting . For example, the team can set the alarm to trigger when the validation loss increases for a certain number of consecutive periods, which means the model is learning the noise in the training data and not generalizing well to the validation data.

1: Log Amazon SageMaker API Calls with AWS CloudTrail - Amazon SageMaker

2: What Is Amazon CloudWatch? - Amazon CloudWatch

3: Metric API — Apache MXNet documentation

CloudWatch — Boto 3 Docs 1.20.21 documentation

Creating Amazon CloudWatch Alarms - Amazon CloudWatch

What is Amazon Simple Notification Service? - Amazon Simple Notification Service

Overfitting and Underfitting - Machine Learning Crash Course

Recall and precision are two metrics that can be used to evaluate the performance of a classification model. Recall is the ratio of true positives to the total number of actual positives, which measures how well the model can identify all the relevant cases. Precision is the ratio of true positives to the total number of predicted positives, which measures how accurate the model is when it makes a positive prediction. Based on the confusion matrix in the image, we can calculate the recall and precision as follows:

Recall = TP / (TP + FN) = 12 / (12 + 1) = 0.92

Precision = TP / (TP + FP) = 12 / (12 + 3) = 0.8

Where TP is the number of true positives, FN is the number of false negatives, and FP is the number of false positives. Therefore, the recall and precision of the model are 0.92 and 0.8, respectively.

 Regularization and dropouts are techniques that can help reduce overfitting in deep learning models. Overfitting occurs when the model learns too much from the training data and fails to generalize well to new data. Regularization adds a penalty term to the loss function that penalizes the model for having large or complex weights. This prevents the model from memorizing the noise or irrelevant features in the training data. L1 and L2 are two types of regularization that differ in how they calculate the penalty term. L1 regularization uses the absolute value of the weights, while L2 regularization uses the square of the weights. Dropouts are another technique that randomly drops out some units or neurons from the network during training. This creates a thinner network that is less prone to overfitting. Dropouts also act as a form of ensemble learning, where multiple sub-models are combined to produce a better prediction. By applying regularization and dropouts to the training, the web-based company can improve the generalization and accuracy of its deep learning model on the test and validation data. References:

Regularization: A video that explains the concept and benefits of regularization in deep learning.

Dropout: A video that demonstrates how dropout works and why it helps reduce overfitting.

 The best feature engineering technique to speed up the model training time without losing a lot of information from the original dataset is to use an autoencoder or principal component analysis (PCA) to replace original features with new features. An autoencoder is a type of neural network that learns a compressed representation of the input data, called the latent space, by minimizing the reconstruction error between the input and the output. PCA is a statistical technique that reduces the dimensionality of the data by finding a set of orthogonal axes, called the principal components, that capture the maximum variance of the data. Both techniques can help reduce the number of features and remove the noise and redundancy in the data, which can improve the model performance and speed up the training process. References:

AWS Machine Learning Specialty Exam Guide

AWS Machine Learning Training - Dimensionality Reduction for Machine Learning

AWS Machine Learning Training - Deep Learning with Amazon SageMaker