1. A retail company wants to build a recommendation system for its e-commerce website that suggests products to customers based on their browsing and purchase history. Which AWS ML application service would be most appropriate for this use case?

A) Amazon Personalize
B) Amazon Kendra
C) Amazon Rekognition
D) Amazon SageMaker
E) Amazon Comprehend


2. You want to monitor the performance of an AWS Lambda function and create alarms to notify you when certain conditions are met. Which of the following statements about Amazon CloudWatch and Lambda functions is true?

A) CloudWatch metrics for Lambda functions are only available for a limited set of metrics.
B) CloudWatch can be used to monitor and collect metrics for Lambda functions, and alarms can be created to notify you when specific conditions are met.
C) CloudWatch alarms can only be created for Lambda functions in the same AWS account as the CloudWatch metrics.
D) Lambda functions can only be triggered by CloudWatch Events, not CloudWatch alarms.



3. You want to deploy a machine learning model for real-time inference on AWS that can handle millions of requests per second with low latency. Which of the following services would be the best fit for your use case?

A) Amazon Elastic Kubernetes Service (EKS)
B) Amazon SageMaker Inference Accelerator (IA)
C) Amazon Elastic Container Service (ECS)
D) AWS Lambda



4. You are building a computer vision application to detect anomalies in medical images, and you need to deploy your machine learning model to process new images in real-time. Which of the following AWS services would be the best fit for this use case?

A) Amazon Elastic Inference
B) Amazon SageMaker Batch Transform
C) Amazon SageMaker Neo
D) Amazon SageMaker Ground Truth



5. Which of the following is NOT a benefit of using Amazon SageMaker Studio over a traditional Jupyter notebook interface?

A) Simplified model deployment and monitoring.
B) Integrated version control and collaboration tools.
C) Increased flexibility for customizing the user interface.
D) Pre-installed machine learning frameworks and libraries.