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

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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: esa
Question 76

A machine learning specialist needs to analyze comments on a news website with users across the globe. The specialist must find the most discussed topics in the comments that are in either English or Spanish.

What steps could be used to accomplish this task? (Choose two.)

Options:

A.

Use an Amazon SageMaker BlazingText algorithm to find the topics independently from language. Proceed with the analysis.

B.

Use an Amazon SageMaker seq2seq algorithm to translate from Spanish to English, if necessary. Use a SageMaker Latent Dirichlet Allocation (LDA) algorithm to find the topics.

C.

Use Amazon Translate to translate from Spanish to English, if necessary. Use Amazon Comprehend topic modeling to find the topics.

D.

Use Amazon Translate to translate from Spanish to English, if necessary. Use Amazon Lex to extract topics form the content.

E.

Use Amazon Translate to translate from Spanish to English, if necessary. Use Amazon SageMaker Neural Topic Model (NTM) to find the topics.

Discussion
Question 77

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:

A.

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

B.

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

C.

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

D.

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

Discussion
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Question 78

A data scientist must build a custom recommendation model in Amazon SageMaker for an online retail company. Due to the nature of the company's products, customers buy only 4-5 products every 5-10 years. So, the company relies on a steady stream of new customers. When a new customer signs up, the company collects data on the customer's preferences. Below is a sample of the data available to the data scientist.

Questions 78

How should the data scientist split the dataset into a training and test set for this use case?

Options:

A.

Shuffle all interaction data. Split off the last 10% of the interaction data for the test set.

B.

Identify the most recent 10% of interactions for each user. Split off these interactions for the test set.

C.

Identify the 10% of users with the least interaction data. Split off all interaction data from these users for the test set.

D.

Randomly select 10% of the users. Split off all interaction data from these users for the test set.

Discussion
Question 79

A machine learning (ML) specialist needs to solve a binary classification problem for a marketing dataset. The ML specialist must maximize the Area Under the ROC Curve (AUC) of the algorithm by training an XGBoost algorithm. The ML specialist must find values for the eta, alpha, min_child_weight, and max_depth hyperparameter that will generate the most accurate model.  

Which approach will meet these requirements with the LEAST operational overhead?  

Options:

A.

Use a bootstrap script to install scikit-learn on an Amazon EMR cluster. Deploy the EMR cluster. Apply k-fold cross-validation methods to the algorithm.

B.

Deploy Amazon SageMaker prebuilt Docker images that have scikit-learn installed. Apply k-fold cross-validation methods to the algorithm.

C.

Use Amazon SageMaker automatic model tuning (AMT). Specify a range of values for each hyperparameter.

D.

Subscribe to an AUC algorithm that is on AWS Marketplace. Specify a range of values for each hyperparameter.

Discussion
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