A Machine Learning Specialist built an image classification deep learning model. However the Specialist ran into an overfitting problem in which the training and testing accuracies were 99% and 75%r respectively.
How should the Specialist address this issue and what is the reason behind it?

1. A. The learning rate should be increased because the optimization process was trapped at a local minimum.
2. B. The dropout rate at the flatten layer should be increased because the model is not generalized enough.
3. C. The dimensionality of dense layer next to the flatten layer should be increased because the model is not complex enough.
4. D. The epoch number should be increased because the optimization process was terminated before it reached the global minimum.

Correct Answer: A

A company that promotes healthy sleep patterns by providing cloud-connected devices currently hosts a sleep tracking application on AWS. The application collects device usage information from device users. The company's Data Science team is building a machine learning model to predict if and when a user will stop utilizing the company's devices. Predictions from this model are used by a downstream application that determines the best approach for contacting users.
The Data Science team is building multiple versions of the machine learning model to evaluate each version against the company’s business goals. To measure long-term effectiveness, the team wants to run multiple versions of the model in parallel for long periods of time, with the ability to control the portion of inferences served by the models.
Which solution satisfies these requirements with MINIMAL effort?

1. A. Build and host multiple models in Amazon SageMake
2. B. Create multiple Amazon SageMaker endpoints, one for each mode
3. C. Programmatically control invoking different models for inference at the applicationlayer.
4. D. Build and host multiple models in Amazon SageMake
5. E. Create an Amazon SageMaker endpoint configuration with multiple production variant
6. F. Programmatically control the portion of the inferences served by the multiple models by updating the endpoint configuration.
7. G. Build and host multiple models in Amazon SageMaker Neo to take into account different types of medical device
8. H. Programmatically control which model is invoked for inference based on the medical device type.
9. I. Build and host multiple models in Amazon SageMake
10. J. Create a single endpoint that accesses multiple model
11. K. Use Amazon SageMaker batch transform to control invoking the different models through the single endpoint.

Correct Answer: B
A/B testing with Amazon SageMaker is required in the Exam. In A/B testing, you test different variants of your models and compare how each variant performs. Amazon SageMaker enables you to test multiple models or model versions behind the `same endpoint` using `production variants`. Each production variant identifies a machine learning (ML) model and the resources deployed for hosting the model. To test multiple models by `distributing traffic` between them, specify the `percentage of the traffic` that gets routed to each model by specifying the `weight` for each `production variant` in the endpoint configuration.
https://docs.aws.amazon.com/sagemaker/latest/dg/model-ab-testing.html#model-testing-target-variant

A Machine Learning Specialist is developing a daily ETL workflow containing multiple ETL jobs The workflow consists of the following processes
* Start the workflow as soon as data is uploaded to Amazon S3
* When all the datasets are available in Amazon S3, start an ETL job to join the uploaded datasets with multiple terabyte-sized datasets already stored in Amazon S3
* Store the results of joining datasets in Amazon S3
* If one of the jobs fails, send a notification to the Administrator Which configuration will meet these requirements?

1. A. Use AWS Lambda to trigger an AWS Step Functions workflow to wait for dataset uploads to complete in Amazon S3. Use AWS Glue to join the datasets Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure
2. B. Develop the ETL workflow using AWS Lambda to start an Amazon SageMaker notebook instance Use a lifecycle configuration script to join the datasets and persist the results in Amazon S3 Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure
3. C. Develop the ETL workflow using AWS Batch to trigger the start of ETL jobs when data is uploaded to Amazon S3 Use AWS Glue to join the datasets in Amazon S3 Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure
4. D. Use AWS Lambda to chain other Lambda functions to read and join the datasets in Amazon S3 as soon as the data is uploaded to Amazon S3 Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure

Correct Answer: A