Question 1

An online store is predicting future book sales by using a linear regression model that is based on past sales data. The data includes duration, a numerical feature that represents the number of days that a book has been listed in the online store. A data scientist performs an exploratory data analysis and discovers that the relationship between book sales and duration is skewed and non-linear.

Which data transformation step should the data scientist take to improve the predictions of the model?

Options :

A : One-hot encoding

B : Cartesian product transformation

C : Quantile binning

D : Normalization

Answer: C

Question 2

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

Answer: C

Question 3

A retail company uses a machine learning (ML) model for daily sales forecasting. The model has provided inaccurate results for the past 3 weeks. At the end of each day, an AWS Glue job consolidates the input data that is used for the forecasting with the actual daily sales data and the predictions of the model. The AWS Glue job stores the data in Amazon S3.

The company's ML team determines that the inaccuracies are occurring because of a change in the value distributions of the model features. The ML team must implement a solution that will detect when this type of change occurs in the future.

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

Options :

A : Use Amazon SageMaker Model Monitor to create a data quality baseline. Confirm that the emit_metrics option is set to Enabled in the baseline constraints file. Set up an Amazon CloudWatch alarm for the metric.

B : Use Amazon SageMaker Model Monitor to create a model quality baseline. Confirm that the emit_metrics option is set to Enabled in the baseline constraints file. Set up an Amazon CloudWatch alarm for the metric.

C : Use Amazon SageMaker Debugger to create rules to capture feature values Set up an Amazon CloudWatch alarm for the rules.

D : Use Amazon CloudWatch to monitor Amazon SageMaker endpoints. Analyze logs in Amazon CloudWatch Logs to check for data drift.

Answer: A

Question 4

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 5

Given the following confusion matrix for a movie classification model, what is the true class frequency for Romance and the predicted class frequency for Adventure?

Options :

A : The true class frequency for Romance is 77.56% and the predicted class frequency for Adventure is 20 85%

B : The true class frequency for Romance is 57.92% and the predicted class frequency for Adventure is 1312%

C : The true class frequency for Romance is 0 78 and the predicted class frequency for Adventure is (0 47 - 0.32).

D : The true class frequency for Romance is 77.56% * 0.78 and the predicted class frequency for Adventure is 20 85% ' 0.32

Answer: B

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