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AWS Certified Machine Learning - Specialty Questions and Answers

Question 37

A company provisions Amazon SageMaker notebook instances for its data science team and creates Amazon VPC interface endpoints to ensure communication between the VPC and the notebook instances. All connections to the Amazon SageMaker API are contained entirely and securely using the AWS network. However, the data science team realizes that individuals outside the VPC can still connect to the notebook instances across the internet.

Which set of actions should the data science team take to fix the issue?

Options:

A.

Modify the notebook instances' security group to allow traffic only from the CIDR ranges of the VPC. Apply this security group to all of the notebook instances' VPC interfaces.

B.

Create an IAM policy that allows the sagemaker:CreatePresignedNotebooklnstanceUrl and sagemaker:DescribeNotebooklnstance actions from only the VPC endpoints. Apply this policy to all IAM users, groups, and roles used to access the notebook instances.

C.

Add a NAT gateway to the VPC. Convert all of the subnets where the Amazon SageMaker notebook instances are hosted to private subnets. Stop and start all of the notebook instances to reassign only private IP addresses.

D.

Change the network ACL of the subnet the notebook is hosted in to restrict access to anyone outside the VPC.

Question 38

A Machine Learning Specialist is developing a custom video recommendation model for an application The dataset used to train this model is very large with millions of data points and is hosted in an Amazon S3 bucket The Specialist wants to avoid loading all of this data onto an Amazon SageMaker notebook instance because it would take hours to move and will exceed the attached 5 GB Amazon EBS volume on the notebook instance.

Which approach allows the Specialist to use all the data to train the model?

Options:

A.

Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the trainingcode is executing and the model parameters seem reasonable. Initiate a SageMaker training job using thefull dataset from the S3 bucket using Pipe input mode.

B.

Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to theinstance. Train on a small amount of the data to verify the training code and hyperparameters. Go back toAmazon SageMaker and train using the full dataset

C.

Use AWS Glue to train a model using a small subset of the data to confirm that the data will be compatiblewith Amazon SageMaker. Initiate a SageMaker training job using the full dataset from the S3 bucket usingPipe input mode.

D.

Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the trainingcode is executing and the model parameters seem reasonable. Launch an Amazon EC2 instance with anAWS Deep Learning AMI and attach the S3 bucket to train the full dataset.

Question 39

A Data Scientist received a set of insurance records, each consisting of a record ID, the final outcome among 200 categories, and the date of the final outcome. Some partial information on claim contents is also provided, but only for a few of the 200 categories. For each outcome category, there are hundreds of records distributed over the past 3 years. The Data Scientist wants to predict how many claims to expect in each category from month to month, a few months in advance.

What type of machine learning model should be used?

Options:

A.

Classification month-to-month using supervised learning of the 200 categories based on claim contents.

B.

Reinforcement learning using claim IDs and timestamps where the agent will identify how many claims in each category to expect from month to month.

C.

Forecasting using claim IDs and timestamps to identify how many claims in each category to expect from month to month.

D.

Classification with supervised learning of the categories for which partial information on claim contents is provided, and forecasting using claim IDs and timestamps for all other categories.

Question 40

A machine learning (ML) specialist is using Amazon SageMaker hyperparameter optimization (HPO) to improve a model’s accuracy. The learning rate parameter is specified in the following HPO configuration:

During the results analysis, the ML specialist determines that most of the training jobs had a learning rate between 0.01 and 0.1. The best result had a learning rate of less than 0.01. Training jobs need to run regularly over a changing dataset. The ML specialist needs to find a tuning mechanism that uses different learning rates more evenly from the provided range between MinValue and MaxValue.

Which solution provides the MOST accurate result?

Options:

A.

Modify the HPO configuration as follows:Select the most accurate hyperparameter configuration form this HPO job.

B.

Run three different HPO jobs that use different learning rates form the following intervals for MinValue and MaxValue while using the same number of training jobs for each HPO job:[0.01, 0.1][0.001, 0.01][0.0001, 0.001]Select the most accurate hyperparameter configuration form these three HPO jobs.

C.

Modify the HPO configuration as follows:Select the most accurate hyperparameter configuration form this training job.

D.

Run three different HPO jobs that use different learning rates form the following intervals for MinValue and MaxValue. Divide the number of training jobs for each HPO job by three:[0.01, 0.1][0.001, 0.01][0.0001, 0.001]Select the most accurate hyperparameter configuration form these three HPO jobs.

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Total 322 questions