Free Amazon MLS-C01 Exam Questions

Question 1

An insurance company developed a new experimental machine learning (ML) model to replace an existing model that is in production. The company must validate the quality of predictions from the new experimental model in a production environment before the company uses the new experimental model to serve general user requests.

New one model can serve user requests at a time. The company must measure the performance of the new experimental model without affecting the current live traffic.

Which solution will meet these requirements?

A : A/B testing

B : Canary release

C : Shadow deployment

D : Blue/green deployment

Answer: D

Question 2

A machine learning specialist is running an Amazon SageMaker endpoint using the built-in object detection algorithm on a P3 instance for real-time predictions in a company's production application. When evaluating the model's resource utilization, the specialist notices that the model is using only a fraction of the GPU.

Which architecture changes would ensure that provisioned resources are being utilized effectively?

A : deploy the model as a batch transform job on an M5 instance.

B : deploy the model on an M5 instance. Attach Amazon Elastic Inference to the instance.

C : deploy the model on a P3dn instance.

D : ploy the model onto an Amazon Elastic Container Service (Amazon ECS) cluster using a P3 instance.

Answer: D

Question 3

A retail company uses a machine learning (ML) model for daily sales forecasting. The model has provided inaccurate results for the past 3 weeks. At the end of each day, an AWS Glue job consolidates the input data that is used for the forecasting with the actual daily sales data and the predictions of the model. The AWS Glue job stores the data in Amazon S3.

The company's ML team determines that the inaccuracies are occurring because of a change in the value distributions of the model features. The ML team must implement a solution that will detect when this type of change occurs in the future.

Which solution will meet these requirements with the LEAST amount of operational overhead?

A : Use Amazon SageMaker Model Monitor to create a data quality baseline. Confirm that the emit_metrics option is set to Enabled in the baseline constraints file. Set up an Amazon CloudWatch alarm for the metric.

B : Use Amazon SageMaker Model Monitor to create a model quality baseline. Confirm that the emit_metrics option is set to Enabled in the baseline constraints file. Set up an Amazon CloudWatch alarm for the metric.

C : Use Amazon SageMaker Debugger to create rules to capture feature values Set up an Amazon CloudWatch alarm for the rules.

D : Use Amazon CloudWatch to monitor Amazon SageMaker endpoints. Analyze logs in Amazon CloudWatch Logs to check for data drift.

Answer: A

Question 4

A chemical company has developed several machine learning (ML) solutions to identify chemical process abnormalities. The time series values of independent variables and the labels are available for the past 2 years and are sufficient to accurately model the problem.

The regular operation label is marked as 0 The abnormal operation label is marked as 1. Process abnormalities have a significant negative effect on the company’s profits. The company must avoid these abnormalities.

Which metrics will indicate an ML solution that will provide the GREATEST probability of detecting an abnormality?

A : Precision = 0.91 -Recall = 0.6

B : Precision = 0.61 -Recall = 0.98

C : Precision = 0.7 -Recall = 0.9

D : Precision = 0.98 -Recall = 0.8

Answer: A

Question 5

A data scientist has been running an Amazon SageMaker notebook instance for a few weeks. During this time, a new version of Jupyter Notebook was released along with additional software updates. The security team mandates that all running SageMaker notebook instances use the latest security and software updates provided by SageMaker.

How can the data scientist meet this requirements?

A : ll the CreateNotebookInstanceLifecycleConfig API operation

B : eate a new SageMaker notebook instance and mount the Amazon Elastic Block Store (Amazon EBS) volume from the original instance

C : op and then restart the SageMaker notebook instance

D : ll the UpdateNotebookInstanceLifecycleConfig API operation

Answer: C