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

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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: melisa
Question 8

A Data Scientist is developing a machine learning model to classify whether a financial transaction is fraudulent. The labeled data available for training consists of 100,000 non-fraudulent observations and 1,000 fraudulent observations.

The Data Scientist applies the XGBoost algorithm to the data, resulting in the following confusion matrix when the trained model is applied to a previously unseen validation dataset. The accuracy of the model is 99.1%, but the Data Scientist needs to reduce the number of false negatives.

Questions 8

Which combination of steps should the Data Scientist take to reduce the number of false negative predictions by the model? (Choose two.)

Options:

A.

Change the XGBoost eval_metric parameter to optimize based on Root Mean Square Error (RMSE).

B.

Increase the XGBoost scale_pos_weight parameter to adjust the balance of positive and negative weights.

C.

Increase the XGBoost max_depth parameter because the model is currently underfitting the data.

D.

Change the XGBoost eval_metric parameter to optimize based on Area Under the ROC Curve (AUC).

E.

Decrease the XGBoost max_depth parameter because the model is currently overfitting the data.

Discussion
Question 9

A data scientist has been running an Amazon SageMaker notebook instance for a few weeks. During this time, a new version of Jupyter Notebook was released along with additional software updates. The security team mandates that all running SageMaker notebook instances use the latest security and software updates provided by SageMaker.

How can the data scientist meet these requirements?

Options:

A.

Call the CreateNotebookInstanceLifecycleConfig API operation

B.

Create a new SageMaker notebook instance and mount the Amazon Elastic Block Store (Amazon EBS) volume from the original instance

C.

Stop and then restart the SageMaker notebook instance

D.

Call the UpdateNotebookInstanceLifecycleConfig API operation

Discussion
Question 10

A bank's Machine Learning team is developing an approach for credit card fraud detection The company has a large dataset of historical data labeled as fraudulent The goal is to build a model to take the information from new transactions and predict whether each transaction is fraudulent or not

Which built-in Amazon SageMaker machine learning algorithm should be used for modeling this problem?

Options:

A.

Seq2seq

B.

XGBoost

C.

K-means

D.

Random Cut Forest (RCF)

Discussion
Question 11

An insurance company developed a new experimental machine learning (ML) model to replace an existing model that is in production. The company must validate the quality of predictions from the new experimental model in a production environment before the company uses the new experimental model to serve general user requests.

Which one model can serve user requests at a time. The company must measure the performance of the new experimental model without affecting the current live traffic

Which solution will meet these requirements?

Options:

A.

A/B testing

B.

Canary release

C.

Shadow deployment

D.

Blue/green deployment

Discussion
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