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

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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: 307 Q&A's Shared By: nayla
Question 60

A company wants to classify user behavior as either fraudulent or normal. Based on internal research, a Machine Learning Specialist would like to build a binary classifier based on two features: age of account and transaction month. The class distribution for these features is illustrated in the figure provided.

Questions 60

Based on this information which model would have the HIGHEST accuracy?

Options:

A.

Long short-term memory (LSTM) model with scaled exponential linear unit (SELL))

B.

Logistic regression

C.

Support vector machine (SVM) with non-linear kernel

D.

Single perceptron with tanh activation function

Discussion
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Question 61

A Machine Learning Specialist works for a credit card processing company and needs to predict which transactions may be fraudulent in near-real time. Specifically, the Specialist must train a model that returns the probability that a given transaction may be fraudulent

How should the Specialist frame this business problem'?

Options:

A.

Streaming classification

B.

Binary classification

C.

Multi-category classification

D.

Regression classification

Discussion
Question 62

A car company is developing a machine learning solution to detect whether a car is present in an image. The image dataset consists of one million images. Each image in the dataset is 200 pixels in height by 200 pixels in width. Each image is labeled as either having a car or not having a car.

Which architecture is MOST likely to produce a model that detects whether a car is present in an image with the highest accuracy?

Options:

A.

Use a deep convolutional neural network (CNN) classifier with the images as input. Include a linear output layer that outputs the probability that an image contains a car.

B.

Use a deep convolutional neural network (CNN) classifier with the images as input. Include a softmax output layer that outputs the probability that an image contains a car.

C.

Use a deep multilayer perceptron (MLP) classifier with the images as input. Include a linear output layer that outputs the probability that an image contains a car.

D.

Use a deep multilayer perceptron (MLP) classifier with the images as input. Include a softmax output layer that outputs the probability that an image contains a car.

Discussion
Question 63

A data engineer at a bank is evaluating a new tabular dataset that includes customer data. The data engineer will use the customer data to create a new model to predict customer behavior. After creating a correlation matrix for the variables, the data engineer notices that many of the 100 features are highly correlated with each other.

Which steps should the data engineer take to address this issue? (Choose two.)

Options:

A.

Use a linear-based algorithm to train the model.

B.

Apply principal component analysis (PCA).

C.

Remove a portion of highly correlated features from the dataset.

D.

Apply min-max feature scaling to the dataset.

E.

Apply one-hot encoding category-based variables.

Discussion
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