Download Full Version MLS-C01 Amazon Web Services Exam

 The question is about improving the testing accuracy of a gradient boosting regression model. The testing accuracy is much lower than the training accuracy, which indicates that the model is overfitting the training data. To reduce overfitting, the following steps are recommended:

Use a one-hot encoder for the categorical fields in the dataset. This will create binary features for each category and avoid imposing an ordinal relationship among them. This can help the model learn the patterns better and generalize to unseen data.

Perform standardization on the financial fields in the dataset. This will scale the features to have zero mean and unit variance, which can improve the convergence and performance of the model. This can also help the model handle features with different units and ranges.

Apply L1 regularization to the data. This will add a penalty term to the loss function that is proportional to the absolute value of the coefficients. This can help the model reduce the complexity and select the most relevant features by shrinking the coefficients of less important features to zero.

1: AWS Machine Learning Specialty Exam Guide

2: AWS Machine Learning Specialty Course

3: AWS Machine Learning Blog

 Quantile binning is a data transformation technique that can be used to handle skewed and non-linear numerical features. It divides the range of a feature into equal-sized bins based on the percentiles of the data. Each bin is assigned a numerical value that represents the midpoint of the bin. This way, the feature values are transformed into a more uniform distribution that can improve the performance of linear models. Quantile binning can also reduce the impact of outliers and noise in the data.

One-hot encoding, Cartesian product transformation, and normalization are not suitable for this scenario. One-hot encoding is used to transform categorical features into binary features. Cartesian product transformation is used to create new features by combining existing features. Normalization is used to scale numerical features to a standard range, but it does not change the shape of the distribution. References:

Data Transformations for Machine Learning

Quantile Binning Transformation

The solution A will create the required transformed records with the least operational overhead because it uses AWS Lambda and Amazon Kinesis Data Firehose, which are fully managed services that can provide the desired functionality. The solution A involves the following steps:

Create an AWS Lambda function that can transform the incoming records. AWS Lambda is a service that can run code without provisioning or managing servers. AWS Lambda can execute the transformation logic on the purchasing records and add the new attributes to the records1.

Enable data transformation on the ingestion Kinesis Data Firehose delivery stream. Use the Lambda function as the invocation target. Amazon Kinesis Data Firehose is a service that can capture, transform, and load streaming data into AWS data stores. Amazon Kinesis Data Firehose can enable data transformation and invoke the Lambda function to process the incoming records before delivering them to Amazon S3. This can reduce the operational overhead of managing the transformation process and the data storage2.

The other options are not suitable because:

Option B: Deploying an Amazon EMR cluster that runs Apache Spark and includes the transformation logic, using Amazon EventBridge (Amazon CloudWatch Events) to schedule an AWS Lambda function to launch the cluster each day and transform the records that accumulate in Amazon S3, and delivering the transformed records to Amazon S3 will incur more operational overhead than using AWS Lambda and Amazon Kinesis Data Firehose. The company will have to manage the Amazon EMR cluster, the Apache Spark application, the AWS Lambda function, and the Amazon EventBridge rule. Moreover, this solution will introduce a delay in the transformation process, as it will run only once a day3.

Option C: Deploying an Amazon S3 File Gateway in the stores, updating the in-store software to deliver data to the S3 File Gateway, and using a scheduled daily AWS Glue job to transform the data that the S3 File Gateway delivers to Amazon S3 will incur more operational overhead than using AWS Lambda and Amazon Kinesis Data Firehose. The company will have to manage the S3 File Gateway, the in-store software, and the AWS Glue job. Moreover, this solution will introduce a delay in the transformation process, as it will run only once a day4.

Option D: Launching a fleet of Amazon EC2 instances that include the transformation logic, configuring the EC2 instances with a daily cron job to transform the records that accumulate in Amazon S3, and delivering the transformed records to Amazon S3 will incur more operational overhead than using AWS Lambda and Amazon Kinesis Data Firehose. The company will have to manage the EC2 instances, the transformation code, and the cron job. Moreover, this solution will introduce a delay in the transformation process, as it will run only once a day5.

1: AWS Lambda

2: Amazon Kinesis Data Firehose

3: Amazon EMR

4: Amazon S3 File Gateway

5: Amazon EC2