Online Access Free Amazon.AWS-Certified-Machine-Learning-Specialty.v2022-11-29.q177 Practice Test (Page 29)

AWS-Certified-Machine-Learning-Specialty Exam Question 136

A data scientist is working on a public sector project for an urban traffic system. While studying the traffic patterns, it is clear to the data scientist that the traffic behavior at each light is correlated, subject to a small stochastic error term. The data scientist must model the traffic behavior to analyze the traffic patterns and reduce congestion.
How will the data scientist MOST effectively model the problem?

A.The data scientist should obtain the optimal equilibrium policy by formulating this problem as a single-agent reinforcement learning problem.

B.The data scientist should obtain a correlated equilibrium policy by formulating this problem as a multi-agent reinforcement learning problem.

C.Rather than finding an equilibrium policy, the data scientist should obtain accurate predictors of traffic flow by using unlabeled simulated data representing the new traffic patterns in the city and applying an unsupervised learning approach.

D.Rather than finding an equilibrium policy, the data scientist should obtain accurate predictors of traffic flow by using historical data through a supervised learning approach.

AWS-Certified-Machine-Learning-Specialty Exam Question 137

A Data Engineer needs to build a model using a dataset containing customer credit card information.
How can the Data Engineer ensure the data remains encrypted and the credit card information is secure?

A.Use AWS KMS to encrypt the data on Amazon S3

B.Use an Amazon SageMaker launch configuration to encrypt the data once it is copied to the SageMaker instance in a VPC. Use the SageMaker principal component analysis (PCA) algorithm to reduce the length of the credit card numbers.

C.Use a custom encryption algorithm to encrypt the data and store the data on an Amazon SageMaker instance in a VPC. Use the SageMaker DeepAR algorithm to randomize the credit card numbers.

D.Use an IAM policy to encrypt the data on the Amazon S3 bucket and Amazon Kinesis to automatically discard credit card numbers and insert fake credit card numbers.

AWS-Certified-Machine-Learning-Specialty Exam Question 138

A company is observing low accuracy while training on the default built-in image classification algorithm in Amazon SageMaker. The Data Science team wants to use an Inception neural network architecture instead of a ResNet architecture.
Which of the following will accomplish this? (Choose two.)

A.Download and apt-get installthe inception network code into an Amazon EC2 instance and use this instance as a Jupyter notebook in Amazon SageMaker.

B.Bundle a Docker container with TensorFlow Estimator loaded with an Inception network and use this for model training.

C.Customize the built-in image classification algorithm to use Inception and use this for model training.

D.Create a support case with the SageMaker team to change the default image classification algorithm to Inception.

E.Use custom code in Amazon SageMaker with TensorFlow Estimator to load the model with an Inception network, and use this for model training.

AWS-Certified-Machine-Learning-Specialty Exam Question 139

A Data Scientist is developing a machine learning model to classify whether a financial transaction is fraudulent. The labeled data available for training consists of 100,000 non-fraudulent observations and 1,000 fraudulent observations.
The Data Scientist applies the XGBoost algorithm to the data, resulting in the following confusion matrix when the trained model is applied to a previously unseen validation dataset. The accuracy of the model is
99.1%, but the Data Scientist has been asked to reduce the number of false negatives.

Which combination of steps should the Data Scientist take to reduce the number of false positive predictions by the model? (Select TWO.)

A.Change the XGBoost eval_metric parameter to optimize based on rmse instead of error.

B.Increase the XGBoost scale_pos_weight parameter to adjust the balance of positive and negative weights.

C.Decrease the XGBoost max_depth parameter because the model is currently overfitting the data.

D.Increase the XGBoost max_depth parameter because the model is currently underfitting the data.

E.Change the XGBoost evaljnetric parameter to optimize based on AUC instead of error.

AWS-Certified-Machine-Learning-Specialty Exam Question 140

A Machine Learning Specialist deployed a model that provides product recommendations on a company's website. Initially, the model was performing very well and resulted in customers buying more products on average. However, within the past few months, the Specialist has noticed that the effect of product recommendations has diminished and customers are starting to return to their original habits of spending less. The Specialist is unsure of what happened, as the model has not changed from its initial deployment over a year ago.
Which method should the Specialist try to improve model performance?

A.The model needs to be completely re-engineered because it is unable to handle product inventory changes.

B.The model should be periodically retrained from scratch using the original data while adding a regularization term to handle product inventory changes

C.The model's hyperparameters should be periodically updated to prevent drift.

D.The model should be periodically retrained using the original training data plus new data as product inventory changes.

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AWS-Certified-Machine-Learning-Specialty Exam Question 136

AWS-Certified-Machine-Learning-Specialty Exam Question 137

AWS-Certified-Machine-Learning-Specialty Exam Question 138

AWS-Certified-Machine-Learning-Specialty Exam Question 139

AWS-Certified-Machine-Learning-Specialty Exam Question 140

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