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QUESTION NO: 2 A Machine Learning Specialist is building a logistic regression model that will predict whether or not a person will order a pizza. The Specialist is trying to build the optimal model with an ideal classification threshold. What model evaluation technique should the Specialist use to understand how different classification thresholds will impact the model's performance? A. Receiver operating characteristic (ROC) curve B. Misclassification rate C. Root Mean Square Error (RM&) D. L1 norm Answer: A
QUESTION NO: 3 A Machine Learning Specialist built an image classification deep learning model. However the Specialist ran into an overfitting problem in which the training and testing accuracies were 99% and 75%r respectively. How should the Specialist address this issue and what is the reason behind it? A. The learning rate should be increased because the optimization process was trapped at a local minimum. B. The dimensionality of dense layer next to the flatten layer should be increased because the model is not complex enough. C. The epoch number should be increased because the optimization process was terminated before it reached the global minimum. D. The dropout rate at the flatten layer should be increased because the model is not generalized enough. Answer: C
QUESTION NO: 4 A Machine Learning Specialist working for an online fashion company wants to build a data ingestion solution for the company's Amazon S3-based data lake. The Specialist wants to create a set of ingestion mechanisms that will enable future capabilities comprised of: * Real-time analytics * Interactive analytics of historical data * Clickstream analytics * Product recommendations Which services should the Specialist use? A. Amazon Athena as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon DynamoDB streams for clickstream analytics; AWS Glue to generate personalized product recommendations B. AWS Glue as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations C. AWS Glue as the data dialog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for real-time data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations D. Amazon Athena as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for near-realtime data insights; Amazon Kinesis Data Firehose for clickstream analytics; AWS Glue to generate personalized product recommendations Answer: C
QUESTION NO: 5 A Machine Learning Specialist has created a deep learning neural network model that performs well on the training data but performs poorly on the test data. Which of the following methods should the Specialist consider using to correct this? (Select THREE.) A. Decrease dropout. B. Increase regularization. C. Increase feature combinations. D. Decrease feature combinations. E. Decrease regularization. F. Increase dropout. Answer: A,B,C
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Updated: May 28, 2022
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