1. A healthcare provider wants to build an ML model that can predict patient outcomes based on their medical history and demographic information. Which AWS ML application service would be the best fit for this use case, given the need for interpretability, explainability, and compliance with regulatory requirements?

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


2. Which of the following services on AWS provides a fully-managed ETL solution for processing large datasets at scale?

A) AWS Data Pipeline
B) AWS Glue
C) Amazon Kinesis Data Firehose
D) Amazon Redshift
E) Amazon EMR


3. Which of the following AWS services can be used for data ingestion/collection in a machine learning pipeline?

A) Amazon Kinesis Data Firehose
B) Amazon EC2
C) Amazon S3 Glacier
D) Amazon RDS



4. Which of the following techniques can help address the problem of overfitting when training a machine learning model?

A) Using a larger batch size during training
B) Increasing the number of epochs during training
C) Adding more layers to the neural network
D) Implementing early stopping during training
E) Decreasing the number of features used as input


5. An AWS DeepLens device has been deployed in a factory for object detection and recognition purposes. The factory produces a variety of goods with different shapes and sizes, and the objects need to be identified in real-time. The factory has a mix of fluorescent and incandescent lighting, which can cause variations in the images captured by the camera.What AWS DeepLens feature could be used to adjust the image quality and improve the accuracy of the object detection models in this scenario?

A) Image preprocessing using AWS Lambda functions
B) Adjusting the camera focus manually in the AWS DeepLens console
C) Applying filters in the AWS DeepLens console
D) Changing the image resolution and frame rate in the AWS DeepLens console