1. You have a dataset containing information about customer complaints and their respective product categories. You want to use Amazon SageMaker to train a model to predict the category of a new complaint based on its text description. Which algorithm is suitable for this use case?

A) BlazingText
B) K-Means Clustering
C) Principal Component Analysis (PCA)
D) Linear Regression



2. You are building a machine learning model to predict customer churn for a subscription-based service. Which of the following regularization techniques can be used to prevent overfitting in the model?

A) Lasso regularization
B) Batch normalization
C) Max pooling
D) Dropout
E) AdaBoost


3. Which of the following AWS services can be used for collecting and ingesting data for machine learning purposes while providing automatic schema inference and data cataloging? (Select 2)(Select 2answers)

A) Amazon Kinesis Data Analytics
B) AWS Glue
C) Amazon QuickSight
D) AWS Lake Formation
E) AWS Step Functions


4. Which of the following AWS services can be used to optimize the operational efficiency of a deployed machine learning model by automating the scaling of compute resources based on demand?

A) AWS Batch
B) Amazon EC2 Auto Scaling
C) Amazon Elastic Kubernetes Service (EKS)
D) AWS Lambda



5. A healthcare organization wants to develop an ML model that can predict patient readmission within 30 days of discharge, given a patient's demographic information, medical history, and treatment received during the hospital stay. Which AWS ML application service would be most appropriate for this use case, considering the need for data security and compliance with HIPAA regulations?

A) Amazon Textract
B) Amazon Comprehend Medical
C) Amazon SageMaker
D) Amazon Rekognition
E) Amazon HealthLake