Question 1

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

Answer: C

Question 2

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 fraudulent.

How should the Specialist frame this business problem?

Options :

A : Streaming classification

B : Binary classification

C : Multi-category classification

D : Regression classification

Answer: B

Question 3

A company is using Amazon SageMaker to build a machine learning (ML) model to predict customer churn based on customer call transcripts. Audio files from customer calls are located in an on-premises VoIP system that has petabytes of recorded calls. The on-premises infrastructure has high-velocity networking and connects to the company's AWS infrastructure through a VPN connection over a 100 Mbps connection. The company has an algorithm for transcribing customer calls that requires GPUs for inference. The company wants to store these transcriptions in an Amazon S3 bucket in the AWS Cloud for model development. Which solution should an ML specialist use to deliver the transcriptions to the S3 bucket as quickly as possible?

Options :

A :

Order and use an AWS Snowball Edge Compute Optimized device with an NVIDIA Tesla module to run the transcription algorithm. Use AWS DataSync to send the resulting transcriptions to the transcription S3 bucket.

B :

Order and use an AWS Snowcone device with Amazon EC2 Inf1 instances to run the transcription algorithm. Use AWS DataSync to send the resulting transcriptions to the transcription S3 bucket.

C :

Order and use AWS Outposts to run the transcription algorithm on GPU-based Amazon EC2 instances. Store the resulting transcriptions in the transcription S3 bucket.

D :

Use AWS DataSync to ingest the audio files to Amazon S3. Create an AWS Lambda function to run the transcription algorithm on the audio files when they are uploaded to Amazon S3. Configure the function to write the resulting transcriptions to the transcription S3 bucket.

Answer: A

Question 4

A large mobile network operating company is building a machine learning model to predict customers who are likely to unsubscribe from the service. The company plans to offer an incentive for these customers as the cost of churn is far greater than the cost of the incentive.

The model produces the following confusion matrix after evaluating on a test dataset of 100 customers:

Based on the model evaluation results, why is this a viable model for production?

Options :

A : The model is 86?curate and the cost incurred by the company as a result of false negatives is less than the false positives.

B : The precision of the model is 86%, which is less than the accuracy of the model.

C :

The model is 86% accurate and the cost incurred by the company as a result of false positives is less than the false negatives.

D : The precision of the model is 86%, which is greater than the accuracy of the model.

Answer: C

Question 5

A company wants to segment a large group of customers into subgroups based on shared characteristics. The company’s data scientist is planning to use the Amazon SageMaker built-in k-means clustering algorithm for this task. The data scientist needs to determine the optimal number of subgroups (k) to use. Which data visualization approach will MOST accurately determine the optimal value of k?

Options :

A :

Calculate the principal component analysis (PCA) components. Run the k-means clustering algorithm for a range of k by using only the first two PCA components. For each value of k, create a scatter plot with a different color for each cluster. The optimal value of k is the value where the clusters start to look reasonably separated

B :

Calculate the principal component analysis (PCA) components. Create a line plot of the number of components against the explained variance. The optimal value of k is the number of PCA components after which the curve starts decreasing in a linear fashion

C :

Create a t-distributed stochastic neighbor embedding (t-SNE) plot for a range of perplexity values. The optimal value of k is the value of perplexity, where the clusters start to look reasonably separated

D :

Run the k-means clustering algorithm for a range of k. For each value of k, calculate the sum of squared errors (SSE). Plot a line chart of the SSE for each value of k. The optimal value of k is the point after which the curve starts decreasing in a linear fashion.

Answer: D

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