MLS-C01: AWS Certified Machine Learning - Specialty
The displayed graph is from a forecasting model for testing a time series.
Considering the graph only, which conclusion should a Machine Learning Specialist make about the behavior of the model?
The model predicts both the trend and the seasonality well
The model predicts the trend well, but not the seasonality.
The model predicts the seasonality well, but not the trend.
The model does not predict the trend or the seasonality well.
Answer is The model predicts both the trend and the seasonality well
Reference:
https://machinelearningmastery.com/decompose-time-series-data-trend-seasonality/
A Data Engineer needs to build a model using a dataset containing customer credit card information.
How can the Data Engineer ensure the data remains encrypted and the credit card information is secure?
Use a custom encryption algorithm to encrypt the data and store the data on an Amazon SageMaker instance in a VPC. Use the SageMaker DeepAR algorithm to randomize the credit card numbers.
Use an IAM policy to encrypt the data on the Amazon S3 bucket and Amazon Kinesis to automatically discard credit card numbers and insert fake credit card numbers.
Use an Amazon SageMaker launch configuration to encrypt the data once it is copied to the SageMaker instance in a VPUse the SageMaker principal component analysis (PCA) algorithm to reduce the length of the credit card numbers.
Use AWS KMS to encrypt the data on Amazon S3 and Amazon SageMaker, and redact the credit card numbers from the customer data with AWS Glue.
Answer is Use AWS KMS to encrypt the data on Amazon S3 and Amazon SageMaker, and redact the credit card numbers from the customer data with AWS Glue.
KMS key can be used for encrypting the data at rest
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?
Review SageMaker logs that have been written to Amazon S3 by leveraging Amazon Athena and Amazon QuickSight to visualize logs as they are being produced.
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.
Build custom Amazon CloudWatch Logs and then leverage Amazon ES and Kibana to query and visualize the log data as it is generated by Amazon SageMaker.
Send Amazon CloudWatch Logs that were generated by Amazon SageMaker to Amazon ES and use Kibana to query and visualize the log data.
Answer is 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.
The specialist is seeking for latency, memory utilization, and CPU utilization during the load test and the ideal answer for all of these is amazon cloud watch which give you all these metrics
Reference:
https://docs.aws.amazon.com/sagemaker/latest/dg/monitoring-cloudwatch.html