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

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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: theia
Question 68

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
Question 69

A manufacturing company has a production line with sensors that collect hundreds of quality metrics. The company has stored sensor data and manual inspection results in a data lake for several months. To automate quality control, the machine learning team must build an automated mechanism that determines whether the produced goods are good quality, replacement market quality, or scrap quality based on the manual inspection results.

Which modeling approach will deliver the MOST accurate prediction of product quality?

Options:

A.

Amazon SageMaker DeepAR forecasting algorithm

B.

Amazon SageMaker XGBoost algorithm

C.

Amazon SageMaker Latent Dirichlet Allocation (LDA) algorithm

D.

A convolutional neural network (CNN) and ResNet

Discussion
Question 70

A data engineer is preparing a dataset that a retail company will use to predict the number of visitors to stores. The data engineer created an Amazon S3 bucket. The engineer subscribed the S3 bucket to an AWS Data Exchange data product for general economic indicators. The data engineer wants to join the economic indicator data to an existing table in Amazon Athena to merge with the business data. All these transformations must finish running in 30-60 minutes.

Which solution will meet these requirements MOST cost-effectively?

Options:

A.

Configure the AWS Data Exchange product as a producer for an Amazon Kinesis data stream. Use an Amazon Kinesis Data Firehose delivery stream to transfer the data to Amazon S3 Run an AWS Glue job that will merge the existing business data with the Athena table. Write the result set back to Amazon S3.

B.

Use an S3 event on the AWS Data Exchange S3 bucket to invoke an AWS Lambda function. Program the Lambda function to use Amazon SageMaker Data Wrangler to merge the existing business data with the Athena table. Write the result set back to Amazon S3.

C.

Use an S3 event on the AWS Data Exchange S3 bucket to invoke an AWS Lambda Function Program the Lambda function to run an AWS Glue job that will merge the existing business data with the Athena table Write the results back to Amazon S3.

D.

Provision an Amazon Redshift cluster. Subscribe to the AWS Data Exchange product and use the product to create an Amazon Redshift Table Merge the data in Amazon Redshift. Write the results back to Amazon S3.

Discussion
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Question 71

A data scientist obtains a tabular dataset that contains 150 correlated features with different ranges to build a regression model. The data scientist needs to achieve more efficient model training by implementing a solution that minimizes impact on the model's performance. The data scientist decides to perform a principal component analysis (PCA) preprocessing step to reduce the number of features to a smaller set of independent features before the data scientist uses the new features in the regression model.

Which preprocessing step will meet these requirements?

Options:

A.

Use the Amazon SageMaker built-in algorithm for PCA on the dataset to transform the data

B.

Load the data into Amazon SageMaker Data Wrangler. Scale the data with a Min Max Scaler transformation step Use the SageMaker built-in algorithm for PCA on the scaled dataset to transform the data.

C.

Reduce the dimensionality of the dataset by removing the features that have the highest correlation Load the data into Amazon SageMaker Data Wrangler Perform a Standard Scaler transformation step to scale the data Use the SageMaker built-in algorithm for PCA on the scaled dataset to transform the data

D.

Reduce the dimensionality of the dataset by removing the features that have the lowest correlation. Load the data into Amazon SageMaker Data Wrangler. Perform a Min Max Scaler transformation step to scale the data. Use the SageMaker built-in algorithm for PCA on the scaled dataset to transform the data.

Discussion
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