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NEW QUESTION 41 A Data Science team is designing a dataset repository where it will store a large amount of training data commonly used in its machine learning models. As Data Scientists may create an arbitrary number of new datasets every day the solution has to scale automatically and be cost-effective. Also, it must be possible to explore the data using SQL. Which storage scheme is MOST adapted to this scenario?

• A. Store datasets as files in an Amazon EBS volume attached to an Amazon EC2 instance.
• B. Store datasets as tables in a multi-node Amazon Redshift cluster.
• C. Store datasets as files in Amazon S3.
• D. Store datasets as global tables in Amazon DynamoDB.

Answer: C   NEW QUESTION 42 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? (Choose two.)

• A. Change the XGBoost eval_metric parameter to optimize based on AUC instead of error.
• B. Increase the XGBoost scaleposweight parameter to adjust the balance of positive and negative weights.
• C. Change the XGBoost eval_metric parameter to optimize based on rmse instead of error.
• D. Increase the XGBoost max_depth parameter because the model is currently underfitting the data.
• E. Decrease the XGBoost max_depth parameter because the model is currently overfitting the data.

Answer: A,E   NEW QUESTION 43 A health care company is planning to use neural networks to classify their X-ray images into normal and abnormal classes. The labeled data is divided into a training set of 1,000 images and a test set of 200 images. The initial training of a neural network model with 50 hidden layers yielded 99% accuracy on the training set, but only 55% accuracy on the test set. What changes should the Specialist consider to solve this issue? (Choose three.)

• A. Enable early stopping
• B. Include all the images from the test set in the training set
• C. Choose a smaller learning rate
• D. Enable dropout
• E. Choose a higher number of layers
• F. Choose a lower number of layers

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