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 Marketing Manager at a pet insurance company plans to launch a targeted marketing campaign on social media to acquire new customers Currently, the company has the following data in Amazon Aurora
• Profiles for all past and existing customers
• Profiles for all past and existing insured pets
• Policy-level information
• Premiums received
• Claims paid
What steps should be taken to implement a machine learning model to identify potential new customers on social media?

1. A. Use regression on customer profile data to understand key characteristics of consumer segments Find similar profiles on social media.
2. B. Use clustering on customer profile data to understand key characteristics of consumer segments Find similar profiles on social media.
3. C. Use a recommendation engine on customer profile data to understand key characteristics of consumer segment
4. D. Find similar profiles on social media
5. E. Use a decision tree classifier engine on customer profile data to understand key characteristics of consumer segment
6. F. Find similar profiles on social media

Correct Answer: C

A Machine Learning Specialist is building a model that will perform time series forecasting using Amazon SageMaker The Specialist has finished training the model and is now planning to perform load testing on the endpoint so they can configure Auto Scaling for the model variant
Which approach will allow the Specialist to review the latency, memory utilization, and CPU utilization during the load test"?

1. A. Review SageMaker logs that have been written to Amazon S3 by leveraging Amazon Athena and Amazon OuickSight to visualize logs as they are being produced
2. B. Generate an Amazon CloudWatch dashboard to create a single view for the latency, memory utilization, and CPU utilization metrics that are outputted by Amazon SageMaker
3. C. Build custom Amazon CloudWatch Logs and then leverage Amazon ES and Kibana to query and visualize the data as it is generated by Amazon SageMaker
4. D. Send Amazon CloudWatch Logs that were generated by Amazon SageMaker lo Amazon ES and use Kibana to query and visualize the log data.

Correct Answer: B

A manufacturing company uses machine learning (ML) models to detect quality issues. The models use images that are taken of the company's product at the end of each production step. The company has thousands of machines at the production site that generate one image per second on average.
The company ran a successful pilot with a single manufacturing machine. For the pilot, ML specialists used an industrial PC that ran AWS IoT Greengrass with a long-running AWS Lambda function that uploaded the images to Amazon S3. The uploaded images invoked a Lambda function that was written in Python to perform inference by using an Amazon SageMaker endpoint that ran a custom model. The inference results were forwarded back to a web service that was hosted at the production site to prevent faulty products from being shipped.
The company scaled the solution out to all manufacturing machines by installing similarly configured industrial PCs on each production machine. However, latency for predictions increased beyond acceptable limits. Analysis shows that the internet connection is at its capacity limit.
How can the company resolve this issue MOST cost-effectively?

1. A. Set up a 10 Gbps AWS Direct Connect connection between the production site and the nearest AWS Regio
2. B. Use the Direct Connect connection to upload the image
3. C. Increase the size of the instances and the number of instances that are used by the SageMaker endpoint.
4. D. Extend the long-running Lambda function that runs on AWS IoT Greengrass to compress the images and upload the compressed files to Amazon S3. Decompress the files by using a separate Lambda function that invokes the existing Lambda function to run the inference pipeline.
5. E. Use auto scaling for SageMake
6. F. Set up an AWS Direct Connect connection between the production site and the nearest AWS Regio
7. G. Use the Direct Connect connection to upload the images.
8. H. Deploy the Lambda function and the ML models onto the AWS IoT Greengrass core that is running on the industrial PCs that are installed on each machin
9. I. Extend the long-running Lambda function that runs on AWS IoT Greengrass to invoke the Lambda function with the captured images and run the inference on the edge component that forwards the results directly to the web service.

Correct Answer: D

A retail company wants to combine its customer orders with the product description data from its product catalog. The structure and format of the records in each dataset is different. A data analyst tried to use a spreadsheet to combine the datasets, but the effort resulted in duplicate records and records that were not properly combined. The company needs a solution that it can use to combine similar records from the two datasets and remove any duplicates.
Which solution will meet these requirements?

1. A. Use an AWS Lambda function to process the dat
2. B. Use two arrays to compare equal strings in the fields from the two datasets and remove any duplicates.
3. C. Create AWS Glue crawlers for reading and populating the AWS Glue Data Catalo
4. D. Call the AWS Glue SearchTables API operation to perform a fuzzy-matching search on the two datasets, and cleanse the data accordingly.
5. E. Create AWS Glue crawlers for reading and populating the AWS Glue Data Catalo
6. F. Use the FindMatches transform to cleanse the data.
7. G. Create an AWS Lake Formation custom transfor
8. H. Run a transformation for matching products from the Lake Formation console to cleanse the data automatically.

Correct Answer: D