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Amazon Web Services Updated MLS-C01 Exam Questions and Answers by renae

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Amazon Web Services MLS-C01 Exam Overview :

Exam Name: AWS Certified Machine Learning - Specialty
Exam Code: MLS-C01 Dumps
Vendor: Amazon Web Services Certification: AWS Certified Specialty
Questions: 281 Q&A's Shared By: renae
Question 36

A company has set up and deployed its machine learning (ML) model into production with an endpoint using Amazon SageMaker hosting services. The ML team has configured automatic scaling for its SageMaker instances to support workload changes. During testing, the team notices that additional instances are being launched before the new instances are ready. This behavior needs to change as soon as possible.

How can the ML team solve this issue?

Options:

A.

Decrease the cooldown period for the scale-in activity. Increase the configured maximum capacity of instances.

B.

Replace the current endpoint with a multi-model endpoint using SageMaker.

C.

Set up Amazon API Gateway and AWS Lambda to trigger the SageMaker inference endpoint.

D.

Increase the cooldown period for the scale-out activity.

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Question 37

A logistics company needs a forecast model to predict next month's inventory requirements for a single item in 10 warehouses. A machine learning specialist uses Amazon Forecast to develop a forecast model from 3 years of monthly data. There is no missing data. The specialist selects the DeepAR+ algorithm to train a predictor. The predictor means absolute percentage error (MAPE) is much larger than the MAPE produced by the current human forecasters.

Which changes to the CreatePredictor API call could improve the MAPE? (Choose two.)

Options:

A.

Set PerformAutoML to true.

B.

Set ForecastHorizon to 4.

C.

Set ForecastFrequency to W for weekly.

D.

Set PerformHPO to true.

E.

Set FeaturizationMethodName to filling.

Discussion
Question 38

A manufacturing company asks its Machine Learning Specialist to develop a model that classifies defective parts into one of eight defect types. The company has provided roughly 100000 images per defect type for training During the injial training of the image classification model the Specialist notices that the validation accuracy is 80%, while the training accuracy is 90% It is known that human-level performance for this type of image classification is around 90%

What should the Specialist consider to fix this issue1?

Options:

A.

A longer training time

B.

Making the network larger

C.

Using a different optimizer

D.

Using some form of regularization

Discussion
Question 39

A Machine Learning Specialist is implementing a full Bayesian network on a dataset that describes public transit in New York City. One of the random variables is discrete, and represents the number of minutes New Yorkers wait for a bus given that the buses cycle every 10 minutes, with a mean of 3 minutes.

Which prior probability distribution should the ML Specialist use for this variable?

Options:

A.

Poisson distribution ,

B.

Uniform distribution

C.

Normal distribution

D.

Binomial distribution

Discussion
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