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

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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: zayaan
Question 48

A Machine Learning Specialist is using Amazon Sage Maker to host a model for a highly available customer-facing application.

The Specialist has trained a new version of the model, validated it with historical data, and now wants to deploy it to production To limit any risk of a negative customer experience, the Specialist wants to be able to monitor the model and roll it back, if needed

What is the SIMPLEST approach with the LEAST risk to deploy the model and roll it back, if needed?

Options:

A.

Create a SageMaker endpoint and configuration for the new model version. Redirect production traffic to the new endpoint by updating the client configuration. Revert traffic to the last version if the model does not perform as expected.

B.

Create a SageMaker endpoint and configuration for the new model version. Redirect production traffic to the new endpoint by using a load balancer Revert traffic to the last version if the model does not perform as expected.

C.

Update the existing SageMaker endpoint to use a new configuration that is weighted to send 5% of the traffic to the new variant. Revert traffic to the last version by resetting the weights if the model does not perform as expected.

D.

Update the existing SageMaker endpoint to use a new configuration that is weighted to send 100% of the traffic to the new variant Revert traffic to the last version by resetting the weights if the model does not perform as expected.

Discussion
Question 49

A Machine Learning Specialist has built a model using Amazon SageMaker built-in algorithms and is not getting expected accurate results The Specialist wants to use hyperparameter optimization to increase the model's accuracy

Which method is the MOST repeatable and requires the LEAST amount of effort to achieve this?

Options:

A.

Launch multiple training jobs in parallel with different hyperparameters

B.

Create an AWS Step Functions workflow that monitors the accuracy in Amazon CloudWatch Logs and relaunches the training job with a defined list of hyperparameters

C.

Create a hyperparameter tuning job and set the accuracy as an objective metric.

D.

Create a random walk in the parameter space to iterate through a range of values that should be used for each individual hyperparameter

Discussion
Question 50

An aircraft engine manufacturing company is measuring 200 performance metrics in a time-series. Engineers

want to detect critical manufacturing defects in near-real time during testing. All of the data needs to be stored

for offline analysis.

What approach would be the MOST effective to perform near-real time defect detection?

Options:

A.

Use AWS IoT Analytics for ingestion, storage, and further analysis. Use Jupyter notebooks from within

AWS IoT Analytics to carry out analysis for anomalies.

B.

Use Amazon S3 for ingestion, storage, and further analysis. Use an Amazon EMR cluster to carry out

Apache Spark ML k-means clustering to determine anomalies.

C.

Use Amazon S3 for ingestion, storage, and further analysis. Use the Amazon SageMaker Random Cut

Forest (RCF) algorithm to determine anomalies.

D.

Use Amazon Kinesis Data Firehose for ingestion and Amazon Kinesis Data Analytics Random Cut Forest

(RCF) to perform anomaly detection. Use Kinesis Data Firehose to store data in Amazon S3 for further

analysis.

Discussion
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Question 51

An insurance company is developing a new device for vehicles that uses a camera to observe drivers' behavior and alert them when they appear distracted The company created approximately 10,000 training images in a controlled environment that a Machine Learning Specialist will use to train and evaluate machine learning models

During the model evaluation the Specialist notices that the training error rate diminishes faster as the number of epochs increases and the model is not accurately inferring on the unseen test images

Which of the following should be used to resolve this issue? (Select TWO)

Options:

A.

Add vanishing gradient to the model

B.

Perform data augmentation on the training data

C.

Make the neural network architecture complex.

D.

Use gradient checking in the model

E.

Add L2 regularization to the model

Discussion
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