ML Model Deployment and Feature Engineering PDF
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Uploaded by ProvenImagery4187
Rutgers University
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This document contains questions and answers about deploying machine learning models in Amazon SageMaker with a CI/CD pipeline and performing feature engineering. The questions cover topics such as model retraining and data preparation.
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Question 7 HOTSPOT - A company wants to host an ML model on Amazon SageMaker. An ML engineer is configuring a continuous integration and continuous delivery (Cl/CD) pipeline in AWS CodePipeline to deploy the model. The pipeline must run automatically when new training data for the model is uploaded...
Question 7 HOTSPOT - A company wants to host an ML model on Amazon SageMaker. An ML engineer is configuring a continuous integration and continuous delivery (Cl/CD) pipeline in AWS CodePipeline to deploy the model. The pipeline must run automatically when new training data for the model is uploaded to an Amazon S3 bucket. Select and order the pipeline's correct steps from the following list. Each step should be selected one time or not at all. (Select and order three.) An S3 event notification invokes the pipeline when new data is uploaded. S3 Lifecycle rule invokes the pipeline when new data is uploaded. SageMaker retrains the model by using the data in the S3 bucket. The pipeline deploys the model to a SageMaker endpoint. The pipeline deploys the model to SageMaker Model Registry. Answer: Question 8 A company stores historical data in.csv files in Amazon S3. Only some of the rows and columns in the.csv files are populated. The columns are not labeled. An ML engineer needs to prepare and store the data so that the company can use the data to train ML models. Select and order the correct steps from the following list to perform this task. Each step should be selected one time or not at all. (Select and order three.) * Create an Amazon SageMaker batch transform job for data cleaning and feature engineering. * Store the resulting data back in Amazon S3. * Use Amazon Athena to infer the schemas and available columns. * Use AWS Glue crawlers to infer the schemas and available columns. * Use AWS Glue DataBrew for data cleaning and feature engineering. Answer: Question 9 HOTSPOT - An ML engineer is working on an ML model to predict the prices of similarly sized homes. The model will base predictions on several features The ML engineer will use the following feature engineering techniques to estimate the prices of the homes: Feature splitting Logarithmic transformation One-hot encoding Standardized distribution Select the correct feature engineering techniques for the following list of features. Each feature engineering technique should be selected one time or not at all (Select three.) Answer:
