AWS Certified Specialty Changed MLS-C01 Questions

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Question 88

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:

Modify the HPO configuration as follows:

Select the most accurate hyperparameter configuration form this HPO job.

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.

Modify the HPO configuration as follows:

Select the most accurate hyperparameter configuration form this training job.

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.

Question 89

An ecommerce company wants to train a large image classification model with 10.000 classes. The company runs multiple model training iterations and needs to minimize operational overhead and cost. The company also needs to avoid loss of work and model retraining.

Which solution will meet these requirements?

Options:

Create the training jobs as AWS Batch jobs that use Amazon EC2 Spot Instances in a managed compute environment.

Use Amazon EC2 Spot Instances to run the training jobs. Use a Spot Instance interruption notice to save a snapshot of the model to Amazon S3 before an instance is terminated.

Use AWS Lambda to run the training jobs. Save model weights to Amazon S3.

Use managed spot training in Amazon SageMaker. Launch the training jobs with checkpointing enabled.

Question 90

A financial services company wants to automate its loan approval process by building a machine learning (ML) model. Each loan data point contains credit history from a third-party data source and demographic information about the customer. Each loan approval prediction must come with a report that contains an explanation for why the customer was approved for a loan or was denied for a loan. The company will use Amazon SageMaker to build the model.

Which solution will meet these requirements with the LEAST development effort?

Options:

Use SageMaker Model Debugger to automatically debug the predictions, generate the explanation, and attach the explanation report.

Use AWS Lambda to provide feature importance and partial dependence plots. Use the plots to generate and attach the explanation report.

Use SageMaker Clarify to generate the explanation report. Attach the report to the predicted results.

Use custom Amazon Cloud Watch metrics to generate the explanation report. Attach the report to the predicted results.

Question 91

A Data Scientist needs to analyze employment data. The dataset contains approximately 10 million

observations on people across 10 different features. During the preliminary analysis, the Data Scientist notices

that income and age distributions are not normal. While income levels shows a right skew as expected, with fewer individuals having a higher income, the age distribution also show a right skew, with fewer older

individuals participating in the workforce.

Which feature transformations can the Data Scientist apply to fix the incorrectly skewed data? (Choose two.)