Amazon Web Services MLS-C01 Exam With Confidence Using Practice Dumps

AWS Glue is a serverless data integration service that can catalogue, clean, enrich, and move data between various data stores. Amazon Athena is an interactive query service that can run SQL queries on data stored in Amazon S3. By using AWS Glue to catalogue the data and Amazon Athena to run queries, the Machine Learning Specialist can leverage the existing data in Amazon S3 without any additional data transformation or loading. This solution requires the least effort compared to the other options, which involve more complex and costly data processing and storage services. References: AWS Glue, Amazon Athena

 The modeling technique that the Machine Learning Specialist should use is binary classification. Binary classification is a type of supervised learning that predicts whether an input belongs to one of two possible classes. In this case, the input is the customer’s recent usage data and the output is whether or not the customer is likely to switch to a competitor in the next 30 days. This is a binary outcome, either yes or no, so binary classification is suitable for this problem. The other options are not appropriate for this problem. Time-series prediction is a type of supervised learning that forecasts future values based on past and present data. Anomaly detection is a type of unsupervised learning that identifies outliers or abnormal patterns in the data. Regression is a type of supervised learning that estimates a continuous numerical value based on the input features. References: Binary Classification, Time Series Prediction, Anomaly Detection, Regression

The approach that will meet the requirements with the least operational overhead is to deploy all the models to a single SageMaker endpoint, treat each model as a production variant, configure an S3 event notification that invokes an AWS Lambda function when new documents are created, and configure the Lambda function to call each production variant and return the results of each model. This approach involves the following steps:

Deploy all the models to a single SageMaker endpoint. Amazon SageMaker is a service that can build, train, and deploy machine learning models. Amazon SageMaker can deploy multiple models to a single endpoint, which is a web service that can serve predictions from the models. Each model can be treated as a production variant, which is a version of the model that runs on one or more instances. Amazon SageMaker can distribute the traffic among the production variants according to the specified weights1.

Treat each model as a production variant. Amazon SageMaker can deploy multiple models to a single endpoint, which is a web service that can serve predictions from the models. Each model can be treated as a production variant, which is a version of the model that runs on one or more instances. Amazon SageMaker can distribute the traffic among the production variants according to the specified weights1.

Configure an S3 event notification that invokes an AWS Lambda function when new documents are created. Amazon S3 is a service that can store and retrieve any amount of data. Amazon S3 can send event notifications when certain actions occur on the objects in a bucket, such as object creation, deletion, or modification. Amazon S3 can invoke an AWS Lambda function as a destination for the event notifications. AWS Lambda is a service that can run code without provisioning or managing servers2.

Configure the Lambda function to call each production variant and return the results of each model. AWS Lambda can execute the code that can call the SageMaker endpoint and specify the production variant to invoke. AWS Lambda can use the AWS SDK or the SageMaker Runtime API to send requests to the endpoint and receive the predictions from the models. AWS Lambda can return the results of each model as a response to the event notification3.

The other options are not suitable because:

Option A: Configuring an S3 event notification that invokes an AWS Lambda function when new documents are created, configuring the Lambda function to create three SageMaker batch transform jobs, one batch transform job for each model for each document, will incur more operational overhead than using a single SageMaker endpoint. Amazon SageMaker batch transform is a service that can process large datasets in batches and store the predictions in Amazon S3. Amazon SageMaker batch transform is not suitable for real-time inference, as it introduces a delay between the request and the response. Moreover, creating three batch transform jobs for each document will increase the complexity and cost of the solution4.

Option C: Deploying each model to its own SageMaker endpoint, configuring an S3 event notification that invokes an AWS Lambda function when new documents are created, configuring the Lambda function to call each endpoint and return the results of each model, will incur more operational overhead than using a single SageMaker endpoint. Deploying each model to its own endpoint will increase the number of resources and endpoints to manage and monitor. Moreover, calling each endpoint separately will increase the latency and network traffic of the solution5.

Option D: Deploying each model to its own SageMaker endpoint, creating three AWS Lambda functions, configuring each Lambda function to call a different endpoint and return the results, configuring three S3 event notifications to invoke the Lambda functions when new documents are created, will incur more operational overhead than using a single SageMaker endpoint and a single Lambda function. Deploying each model to its own endpoint will increase the number of resources and endpoints to manage and monitor. Creating three Lambda functions will increase the complexity and cost of the solution. Configuring three S3 event notifications will increase the number of triggers and destinations to manage and monitor6.

1: Deploying Multiple Models to a Single Endpoint - Amazon SageMaker

2: Configuring Amazon S3 Event Notifications - Amazon Simple Storage Service

3: Invoke an Endpoint - Amazon SageMaker

4: Get Inferences for an Entire Dataset with Batch Transform - Amazon SageMaker

5: Deploy a Model - Amazon SageMaker

6: AWS Lambda