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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: 322 Q&A's Shared By: zayaan
Question 48

A Machine Learning Specialist deployed a model that provides product recommendations on a company's website Initially, the model was performing very well and resulted in customers buying more products on average However within the past few months the Specialist has noticed that the effect of product recommendations has diminished and customers are starting to return to their original habits of spending less The Specialist is unsure of what happened, as the model has not changed from its initial deployment over a year ago

Which method should the Specialist try to improve model performance?

Options:

A.

The model needs to be completely re-engineered because it is unable to handle product inventory changes

B.

The model's hyperparameters should be periodically updated to prevent drift

C.

The model should be periodically retrained from scratch using the original data while adding a regularization term to handle product inventory changes

D.

The model should be periodically retrained using the original training data plus new data as product inventory changes

Discussion
Question 49

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
Question 50

A manufacturing company has a large set of labeled historical sales data The manufacturer would like to predict how many units of a particular part should be produced each quarter Which machine learning approach should be used to solve this problem?

Options:

A.

Logistic regression

B.

Random Cut Forest (RCF)

C.

Principal component analysis (PCA)

D.

Linear regression

Discussion
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Question 51

A Data Scientist is building a model to predict customer churn using a dataset of 100 continuous numerical

features. The Marketing team has not provided any insight about which features are relevant for churn

prediction. The Marketing team wants to interpret the model and see the direct impact of relevant features on

the model outcome. While training a logistic regression model, the Data Scientist observes that there is a wide

gap between the training and validation set accuracy.

Which methods can the Data Scientist use to improve the model performance and satisfy the Marketing team’s

needs? (Choose two.)

Options:

A.

Add L1 regularization to the classifier

B.

Add features to the dataset

C.

Perform recursive feature elimination

D.

Perform t-distributed stochastic neighbor embedding (t-SNE)

E.

Perform linear discriminant analysis

Discussion
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