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Newly Released Amazon Web Services MLS-C01 Exam PDF

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Total 322 questions

AWS Certified Machine Learning - Specialty Questions and Answers

Question 29

A retail company stores 100 GB of daily transactional data in Amazon S3 at periodic intervals. The company wants to identify the schema of the transactional data. The company also wants to perform transformations on the transactional data that is in Amazon S3.

The company wants to use a machine learning (ML) approach to detect fraud in the transformed data.

Which combination of solutions will meet these requirements with the LEAST operational overhead? {Select THREE.)

Options:

A.

Use Amazon Athena to scan the data and identify the schema.

B.

Use AWS Glue crawlers to scan the data and identify the schema.

C.

Use Amazon Redshift to store procedures to perform data transformations

D.

Use AWS Glue workflows and AWS Glue jobs to perform data transformations.

E.

Use Amazon Redshift ML to train a model to detect fraud.

F.

Use Amazon Fraud Detector to train a model to detect fraud.

Question 30

A sports analytics company is providing services at a marathon. Each runner in the marathon will have their race ID printed as text on the front of their shirt. The company needs to extract race IDs from images of the runners.

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

Options:

A.

Use Amazon Rekognition.

B.

Use a custom convolutional neural network (CNN).

C.

Use the Amazon SageMaker Object Detection algorithm.

D.

Use Amazon Lookout for Vision.

Question 31

An ecommerce company sends a weekly email newsletter to all of its customers. Management has hired a team of writers to create additional targeted content. A data scientist needs to identify five customer segments based on age, income, and location. The customers’ current segmentation is unknown. The data scientist previously built an XGBoost model to predict the likelihood of a customer responding to an email based on age, income, and location.

Why does the XGBoost model NOT meet the current requirements, and how can this be fixed?

Options:

A.

The XGBoost model provides a true/false binary output. Apply principal component analysis (PCA) with five feature dimensions to predict a segment.

B.

The XGBoost model provides a true/false binary output. Increase the number of classes the XGBoost model predicts to five classes to predict a segment.

C.

The XGBoost model is a supervised machine learning algorithm. Train a k-Nearest-Neighbors (kNN) model with K = 5 on the same dataset to predict a segment.

D.

The XGBoost model is a supervised machine learning algorithm. Train a k-means model with K = 5 on the same dataset to predict a segment.

Question 32

A machine learning (ML) engineer is preparing a dataset for a classification model. The ML engineer notices that some continuous numeric features have a significantly greater value than most other features. A business expert explains that the features are independently informative and that the dataset is representative of the target distribution.

After training, the model's inferences accuracy is lower than expected.

Which preprocessing technique will result in the GREATEST increase of the model's inference accuracy?

Options:

A.

Normalize the problematic features.

B.

Bootstrap the problematic features.

C.

Remove the problematic features.

D.

Extrapolate synthetic features.

Page: 8 / 23
Total 322 questions