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

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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: baxter
Question 12

A chemical company has developed several machine learning (ML) solutions to identify chemical process abnormalities. The time series values of independent variables and the labels are available for the past 2 years and are sufficient to accurately model the problem.

The regular operation label is marked as 0. The abnormal operation label is marked as 1 . Process abnormalities have a significant negative effect on the companys profits. The company must avoid these abnormalities.

Which metrics will indicate an ML solution that will provide the GREATEST probability of detecting an abnormality?

Options:

A.

Precision = 0.91 Recall = 0.6

B.

Precision = 0.61 Recall = 0.98

C.

Precision = 0.7 Recall = 0.9

D.

Precision = 0.98 Recall = 0.8

Discussion
Question 13

A Data Scientist needs to analyze employment data. The dataset contains approximately 10 million

observations on people across 10 different features. During the preliminary analysis, the Data Scientist notices

that income and age distributions are not normal. While income levels shows a right skew as expected, with fewer individuals having a higher income, the age distribution also show a right skew, with fewer older

individuals participating in the workforce.

Which feature transformations can the Data Scientist apply to fix the incorrectly skewed data? (Choose two.)

Options:

A.

Cross-validation

B.

Numerical value binning

C.

High-degree polynomial transformation

D.

Logarithmic transformation

E.

One hot encoding

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Question 14

A Machine Learning Specialist is working with multiple data sources containing billions of records that need to be joined. What feature engineering and model development approach should the Specialist take with a dataset this large?

Options:

A.

Use an Amazon SageMaker notebook for both feature engineering and model development

B.

Use an Amazon SageMaker notebook for feature engineering and Amazon ML for model development

C.

Use Amazon EMR for feature engineering and Amazon SageMaker SDK for model development

D.

Use Amazon ML for both feature engineering and model development.

Discussion
Question 15

A machine learning (ML) specialist must develop a classification model for a financial services company. A domain expert provides the dataset, which is tabular with 10,000 rows and 1,020 features. During exploratory data analysis, the specialist finds no missing values and a small percentage of duplicate rows. There are correlation scores of > 0.9 for 200 feature pairs. The mean value of each feature is similar to its 50th percentile.

Which feature engineering strategy should the ML specialist use with Amazon SageMaker?

Options:

A.

Apply dimensionality reduction by using the principal component analysis (PCA) algorithm.

B.

Drop the features with low correlation scores by using a Jupyter notebook.

C.

Apply anomaly detection by using the Random Cut Forest (RCF) algorithm.

D.

Concatenate the features with high correlation scores by using a Jupyter notebook.

Discussion
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