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Amazon Web Services MLS-C01 Dumps Questions Answers

MLS-C01 exam

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AWS Certified Machine Learning - Specialty

Last Update Feb 27, 2024
Total Questions : 281

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Last Update Feb 27, 2024
Total Questions : 281

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AWS Certified Machine Learning - Specialty Questions and Answers

Questions 1

A Machine Learning Specialist has built a model using Amazon SageMaker built-in algorithms and is not getting expected accurate results The Specialist wants to use hyperparameter optimization to increase the model's accuracy

Which method is the MOST repeatable and requires the LEAST amount of effort to achieve this?

Options:

A.

Launch multiple training jobs in parallel with different hyperparameters

B.

Create an AWS Step Functions workflow that monitors the accuracy in Amazon CloudWatch Logs and relaunches the training job with a defined list of hyperparameters

C.

Create a hyperparameter tuning job and set the accuracy as an objective metric.

D.

Create a random walk in the parameter space to iterate through a range of values that should be used for each individual hyperparameter

Questions 2

A media company wants to create a solution that identifies celebrities in pictures that users upload. The company also wants to identify the IP address and the timestamp details from the users so the company can prevent users from uploading pictures from unauthorized locations.

Which solution will meet these requirements with LEAST development effort?

Options:

A.

Use AWS Panorama to identify celebrities in the pictures. Use AWS CloudTrail to capture IP address and timestamp details.

B.

Use AWS Panorama to identify celebrities in the pictures. Make calls to the AWS Panorama Device SDK to capture IP address and timestamp details.

C.

Use Amazon Rekognition to identify celebrities in the pictures. Use AWS CloudTrail to capture IP address and timestamp details.

D.

Use Amazon Rekognition to identify celebrities in the pictures. Use the text detection feature to capture IP address and timestamp details.

Questions 3

A credit card company wants to build a credit scoring model to help predict whether a new credit card applicant

will default on a credit card payment. The company has collected data from a large number of sources with

thousands of raw attributes. Early experiments to train a classification model revealed that many attributes are

highly correlated, the large number of features slows down the training speed significantly, and that there are

some overfitting issues.

The Data Scientist on this project would like to speed up the model training time without losing a lot of

information from the original dataset.

Which feature engineering technique should the Data Scientist use to meet the objectives?

Options:

A.

Run self-correlation on all features and remove highly correlated features

B.

Normalize all numerical values to be between 0 and 1

C.

Use an autoencoder or principal component analysis (PCA) to replace original features with new features

D.

Cluster raw data using k-means and use sample data from each cluster to build a new dataset