MLS-C01 Exam Dumps - AWS Certified Machine Learning - Specialty

The solution C modifies the HPO configuration to use a logarithmic scale for the learning rate parameter. This means that the values of the learning rate are sampled from a log-uniform distribution, which gives more weight to smaller values. This can help to explore the lower end of the range more evenly and find the optimal learning rate more efficiently. The other solutions either use a linear scale, which may not sample enough values from the lower end, or divide the range into sub-intervals, which may miss some combinations of hyperparameters. References:

How Hyperparameter Tuning Works - Amazon SageMaker

Tuning Hyperparameters - Amazon SageMaker

SageMaker Data Wrangler is a feature of SageMaker Studio that provides an end-to-end solution for importing, preparing, transforming, featurizing, and analyzing data. Data Wrangler includes built-in analyses that help generate visualizations and data insights in a few clicks. One of the built-in analyses is the Quick Model visualization, which can be used to quickly evaluate the data and produce importance scores for each feature. A feature importance score indicates how useful a feature is at predicting a target label. The feature importance score is between [0, 1] and a higher number indicates that the feature is more important to the whole dataset. The Quick Model visualization uses a random forest model to calculate the feature importance for each feature using the Gini importance method. This method measures the total reduction in node impurity (a measure of how well a node separates the classes) that is attributed to splitting on a particular feature. The ML developer can use the Quick Model visualization to obtain the importance scores for each feature of the dataset and use them to feature engineer the dataset. This solution requires the least development effort compared to the other options.

 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

The solution that will meet the requirements with the least development effort is to use Amazon SageMaker Feature Store to set feature groups for the current features that the ML models use, assign the required metadata for each feature, and use Amazon QuickSight to analyze the metadata. This solution can leverage the existing AWS services and features to perform feature-level metadata analysis and reporting.

Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, update, search, and share machine learning (ML) features. The service provides feature management capabilities such as enabling easy feature reuse, low latency serving, time travel, and ensuring consistency between features used in training and inference workflows. A feature group is a logical grouping of ML features whose organization and structure is defined by a feature group schema. A feature group schema consists of a list of feature definitions, each of which specifies the name, type, and metadata of a feature. The metadata can include information such as data sensitivity, authorship, description, and parameters. The metadata can help make features discoverable, understandable, and traceable. Amazon SageMaker Feature Store allows users to set feature groups for the current features that the ML models use, and assign the required metadata for each feature using the AWS SDK for Python (Boto3), AWS Command Line Interface (AWS CLI), or Amazon SageMaker Studio1.

Amazon QuickSight is a fully managed, serverless business intelligence service that makes it easy to create and publish interactive dashboards that include ML insights. Amazon QuickSight can connect to various data sources, such as Amazon S3, Amazon Athena, Amazon Redshift, and Amazon SageMaker Feature Store, and analyze the data using standard SQL or built-in ML-powered analytics. Amazon QuickSight can also create rich visualizations and reports that can be accessed from any device, and securely shared with anyone inside or outside an organization. Amazon QuickSight can be used to analyze the metadata of the features stored in Amazon SageMaker Feature Store, and generate a report that summarizes the metadata analysis2.

The other options are either more complex or less effective than the proposed solution. Using Amazon SageMaker Data Wrangler to select the features and create a data flow to perform feature-level metadata analysis would require additional steps and resources, and may not capture all the metadata attributes that the company requires. Creating an Amazon DynamoDB table to store feature-level metadata would introduce redundancy and inconsistency, as the metadata is already stored in Amazon SageMaker Feature Store. Using SageMaker Studio to analyze the metadata would not generate a report that can be easily shared and accessed by the company.

1: Amazon SageMaker Feature Store – Amazon Web Services

2: Amazon QuickSight – Business Intelligence Service - Amazon Web Services

The best solution is to configure Amazon EventBridge to run a predefined SageMaker pipeline to perform the transformations when a new data is detected in the S3 bucket. This solution requires the least development effort because it leverages the native integration between EventBridge and SageMaker Pipelines, which allows you to trigger a pipeline execution based on an event rule. EventBridge can monitor the S3 bucket for new data uploads and invoke the pipeline that contains the same transformations and feature engineering steps that were defined in SageMaker Data Wrangler. The pipeline can then ingest the transformed data into the online feature store for training and inference.

The other solutions are less optimal because they require more development effort and additional services. Using AWS Lambda or AWS Step Functions would require writing custom code to invoke the SageMaker pipeline and handle any errors or retries. Using Apache Airflow would require setting up and maintaining an Airflow server and DAGs, as well as integrating with the SageMaker API.

Amazon EventBridge and Amazon SageMaker Pipelines integration

Create a pipeline using a JSON specification

Ingest data into a feature group

 The best way to split the dataset into a training and test set for this use case is to randomly select 10% of the users and split off all interaction data from these users for the test set. This is because the company relies on a steady stream of new customers, so the test set should reflect the behavior of new customers who have not been seen by the model before. The other options are not suitable because they either mix old and new customers in the test set (A and B), or they bias the test set towards users with less interaction data ©. References:

Amazon SageMaker Developer Guide: Train and Test Datasets

Amazon Personalize Developer Guide: Preparing and Importing Data

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 combination of actions that will meet the requirements with the least operational overhead is to use SageMaker Clarify to automatically detect data bias and to configure SageMaker Data Wrangler to generate a bias report. SageMaker Clarify is a feature of Amazon SageMaker that provides machine learning (ML) developers with tools to gain greater insights into their ML training data and models. SageMaker Clarify can detect potential bias during data preparation, after model training, and in your deployed model. For instance, you can check for bias related to age in your dataset or in your trained model and receive a detailed report that quantifies different types of potential bias1. SageMaker Data Wrangler is another feature of Amazon SageMaker that enables you to prepare data for machine learning (ML) quickly and easily. You can use SageMaker Data Wrangler to identify potential bias during data preparation without having to write your own code. You specify input features, such as gender or age, and SageMaker Data Wrangler runs an analysis job to detect potential bias in those features. SageMaker Data Wrangler then provides a visual report with a description of the metrics and measurements of potential bias so that you can identify steps to remediate the bias2. The other actions either require more customization (such as using SageMaker Model Monitor or SageMaker Experiments) or do not meet the requirement of detecting data bias (such as using SageMaker Ground Truth). References:

1: Bias Detection and Model Explainability – Amazon Web Services

2: Amazon SageMaker Data Wrangler – Amazon Web Services