Amazon MLS-C01 Practice Test - Questions Answers, Page 26

The Hyperband tuning strategy is a multi-fidelity based tuning strategy that dynamically reallocates resources to the most promising hyperparameter configurations. Hyperband uses both intermediate and final results of training jobs to stop under-performing jobs and reallocate epochs to well-utilized hyperparameter configurations. Hyperband can provide up to three times faster hyperparameter tuning compared to other strategies1. Setting a lower value for the MaxNumberOfTrainingJobs parameter can also reduce the computation time for the hyperparameter tuning job by limiting the number of training jobs that the tuning job can launch. This can help avoid unnecessary or redundant training jobs that do not improve the objective metric.

The other options are not effective ways to reduce the computation time for the hyperparameter tuning job. Increasing the number of hyperparameters will increase the complexity and dimensionality of the search space, which can result in longer computation time and lower performance. Using the grid search tuning strategy will also increase the computation time, as grid search methodically searches through every combination of hyperparameter values, which can be very expensive and inefficient for large search spaces. Setting a lower value for the MaxParallelTrainingJobs parameter will reduce the number of training jobs that can run in parallel, which can slow down the tuning process and increase the waiting time.

References:

* How Hyperparameter Tuning Works

* Best Practices for Hyperparameter Tuning

* HyperparameterTuner

* Amazon SageMaker Automatic Model Tuning now provides up to three times faster hyperparameter tuning with Hyperband

The SageMaker CreatePresignedDomainUrl API is the best option to meet the requirements of the company. This API creates a URL for a specified UserProfile in a Domain. When accessed in a web browser, the user will be automatically signed in to the domain, and granted access to all of the Apps and files associated with the Domain's Amazon Elastic File System (EFS) volume. This API can only be called when the authentication mode equals IAM, which means the company can use its existing IdP to authenticate users. The company can use the DynamoDB table to store the domain IDs and user profile names for each department, and use the proxy application to query the table and generate the presigned URL for the appropriate domain according to the user's credentials. The presigned URL is valid only for a specified duration, which can be set by the SessionExpirationDurationInSeconds parameter. This can help enhance the security and prevent unauthorized access to the domains.

The other options are not suitable for the company's requirements. The SageMaker CreateHumanTaskUi API is used to define the settings for the human review workflow user interface, which is not related to accessing the SageMaker Studio domains. The SageMaker ListHumanTaskUis API is used to return information about the human task user interfaces in the account, which is also not relevant to the company's use case. The SageMaker CreatePresignedNotebookInstanceUrl API is used to create a URL to connect to the Jupyter server from a notebook instance, which is different from accessing the SageMaker Studio domain.

References:

* CreatePresignedDomainUrl

* CreatePresignedNotebookInstanceUrl

* CreateHumanTaskUi

* ListHumanTaskUis

The best solution to meet the requirements is to apply Synthetic Minority Oversampling Technique (SMOTE) to the training dataset before training. SMOTE is a technique that generates synthetic samples for the minority class by interpolating between existing samples. This can help balance the class distribution and provide more information to the model. SMOTE can improve the performance of the model on the minority class, which is the class of interest in churn prediction. SMOTE can be applied using the SageMaker Data Wrangler, which provides a built-in analysis for oversampling the minority class1.

The other options are not effective solutions for the problem. Applying anomaly detection to remove outliers from the training dataset before training may not improve the model's accuracy, as outliers may not be the main cause of the false results. Moreover, removing outliers may reduce the diversity of the data and make the model less robust. Applying normalization to the features of the training dataset before training may improve the model's convergence and stability, but it does not address the class imbalance issue. Normalization can also be applied using the SageMaker Data Wrangler, which provides a built-in transformation for scaling the features2. Applying undersampling to the training dataset before training may reduce the class imbalance, but it also discards potentially useful information from the majority class. Undersampling can also result in underfitting and high bias for the model.

References:

* Analyze and Visualize

* Transform and Export

* SMOTE for Imbalanced Classification with Python

* Churn prediction using Amazon SageMaker built-in tabular algorithms LightGBM, CatBoost, TabTransformer, and AutoGluon-Tabular

The best solution to verify the suitability of an ARIMA approach is to use Amazon SageMaker Data Wrangler. 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 Seasonal-Trend decomposition, which can be used to decompose a time series into its trend, seasonal, and residual components. This analysis can help the developer understand the patterns and characteristics of the time series, such as stationarity, seasonality, and autocorrelation, which are important for choosing an appropriate ARIMA model. Data Wrangler also provides built-in transformations that can help the developer handle missing data, such as imputing with mean, median, mode, or constant values, or dropping rows with missing values. Imputing missing data can help avoid gaps and irregularities in the time series, which can affect the ARIMA model performance. Data Wrangler also allows the developer to export the prepared data and the analysis code to various destinations, such as SageMaker Processing, SageMaker Pipelines, or SageMaker Feature Store, for further processing and modeling.

The other options are not suitable for verifying the suitability of an ARIMA approach. Amazon SageMaker Autopilot is a feature-set that automates key tasks of an automatic machine learning (AutoML) process. It explores the data, selects the algorithms relevant to the problem type, and prepares the data to facilitate model training and tuning. However, Autopilot does not support ARIMA as a machine learning problem type, and it does not provide any visualization or analysis of the time series data. Resampling data by using the aggregate daily total can reduce the granularity and resolution of the time series, which can affect the ARIMA model accuracy and applicability.

References:

* Analyze and Visualize

* Transform and Export

* Amazon SageMaker Autopilot

* ARIMA Model -- Complete Guide to Time Series Forecasting in Python

The AnalyzeDocument API action is the best option to generate a confidence score for each page of each contract. This API action analyzes an input document for relationships between detected items. The input document can be an image file in JPEG or PNG format, or a PDF file. The output is a JSON structure that contains the extracted data from the document. The FeatureTypes parameter specifies the types of analysis to perform on the document. The available feature types are TABLES, FORMS, and SIGNATURES. By setting the FeatureTypes parameter to SIGNATURES, the API action will detect and extract information about signatures from the document. The output will include a list of SignatureDetection objects, each containing information about a detected signature, such as its location and confidence score. The confidence score is a value between 0 and 100 that indicates the probability that the detected signature is correct. The output will also include a list of Block objects, each representing a document page. Each Block object will have a Page attribute that contains the page number and a Confidence attribute that contains the confidence score for the page. The confidence score for the page is the average of the confidence scores of the blocks that are detected on the page. The law firm can use the AnalyzeDocument API action to generate a confidence score for each page of each contract by using the SIGNATURES feature type and returning the confidence scores from the SignatureDetection and Block objects.

The other options are not suitable for generating a confidence score for each page of each contract. The Prediction API call is not an Amazon Textract API action, but a generic term for making inference requests to a machine learning model. The StartDocumentAnalysis API action is used to start an asynchronous job to analyze a document. The output is a job identifier (JobId) that is used to get the results of the analysis with the GetDocumentAnalysis API action. The GetDocumentAnalysis API action is used to get the results of a document analysis started by the StartDocumentAnalysis API action. The output is a JSON structure that contains the extracted data from the document. However, both the StartDocumentAnalysis and the GetDocumentAnalysis API actions do not support the SIGNATURES feature type, and therefore cannot detect signatures or provide confidence scores for them.

References:

* AnalyzeDocument

* SignatureDetection

* Block

* Amazon Textract launches the ability to detect signatures on any document

The best solution to meet the requirements is to tune the csv_weight hyperparameter and the scale_pos_weight hyperparameter by using automatic model tuning (AMT). Optimize on {''HyperParameterTuningJobObjective'': {''MetricName'': ''validation:f1'', ''Type'': ''Maximize''}}.

The csv_weight hyperparameter is used to specify the instance weights for the training data in CSV format. This can help handle imbalanced data by assigning higher weights to the minority class examples and lower weights to the majority class examples. The scale_pos_weight hyperparameter is used to control the balance of positive and negative weights. It is the ratio of the number of negative class examples to the number of positive class examples. Setting a higher value for this hyperparameter can increase the importance of the positive class and improve the recall. Both of these hyperparameters can help the XGBoost model capture as many instances of returned items as possible.

Automatic model tuning (AMT) is a feature of Amazon SageMaker that automates the process of finding the best hyperparameter values for a machine learning model. AMT uses Bayesian optimization to search the hyperparameter space and evaluate the model performance based on a predefined objective metric. The objective metric is the metric that AMT tries to optimize by adjusting the hyperparameter values. For imbalanced classification problems, accuracy is not a good objective metric, as it can be misleading and biased towards the majority class. A better objective metric is the F1 score, which is the harmonic mean of precision and recall. The F1 score can reflect the balance between precision and recall and is more suitable for imbalanced data. The F1 score ranges from 0 to 1, where 1 is the best possible value. Therefore, the type of the objective should be ''Maximize'' to achieve the highest F1 score.

By tuning the csv_weight and scale_pos_weight hyperparameters and optimizing on the F1 score, the data scientist can meet the requirements most cost-effectively. This solution requires tuning only two hyperparameters, which can reduce the computation time and cost compared to tuning all possible hyperparameters. This solution also uses the appropriate objective metric for imbalanced classification, which can improve the model performance and capture more instances of returned items.

References:

* XGBoost Hyperparameters

* Automatic Model Tuning

* How to Configure XGBoost for Imbalanced Classification

* Imbalanced Data

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.

References:

Amazon EventBridge and Amazon SageMaker Pipelines integration

Create a pipeline using a JSON specification

Ingest data into a feature group

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.

References:

1:SageMaker Pipelines - Amazon SageMaker

2:Monitor data and model quality - Amazon SageMaker

The best solution for this scenario is to use shadow deployment, which is a technique that allows the company to run the new experimental model in parallel with the existing model, without exposing it to the end users. In shadow deployment, the company can route the same user requests to both models, but only return the responses from the existing model to the users.The responses from the new experimental model are logged and analyzed for quality and performance metrics, such as accuracy, latency, and resource consumption12. This way, the company can validate the new experimental model in a production environment, without affecting the current live traffic or user experience.

The other solutions are not suitable, because they have the following drawbacks:

A: A/B testing is a technique that involves splitting the user traffic between two or more models, and comparing their outcomes based on predefined metrics.However, this technique exposes the new experimental model to a portion of the end users, which might affect their experience if the model is not reliable or consistent with the existing model3.

B: Canary release is a technique that involves gradually rolling out the new experimental model to a small subset of users, and monitoring its performance and feedback.However, this technique also exposes the new experimental model to some end users, and requires careful selection and segmentation of the user groups4.

D: Blue/green deployment is a technique that involves switching the user traffic from the existing model (blue) to the new experimental model (green) at once, after testing and verifying the new model in a separate environment.However, this technique does not allow the company to validate the new experimental model in a production environment, and might cause service disruption or inconsistency if the new model is not compatible or stable5.

References:

1:Shadow Deployment: A Safe Way to Test in Production | LaunchDarkly Blog

2:Shadow Deployment: A Safe Way to Test in Production | LaunchDarkly Blog

3:A/B Testing for Machine Learning Models | AWS Machine Learning Blog

4:Canary Releases for Machine Learning Models | AWS Machine Learning Blog

5:Blue-Green Deployments for Machine Learning Models | AWS Machine Learning Blog