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Download AWS Certified Machine Learning - Specialty Exam Dumps
NEW QUESTION 26
A Machine Learning Specialist is applying a linear least squares regression model to a dataset with 1 000 records and 50 features Prior to training, the ML Specialist notices that two features are perfectly linearly dependent Why could this be an issue for the linear least squares regression model?
- A. It could introduce non-linear dependencies within the data which could invalidate the linear assumptions of the model
- B. It could modify the loss function during optimization causing it to fail during training
- C. It could cause the backpropagation algorithm to fail during training
- D. It could create a singular matrix during optimization which fails to define a unique solution
NEW QUESTION 27
A Machine Learning Specialist has completed a proof of concept for a company using a small data sample and now the Specialist is ready to implement an end-to-end solution in AWS using Amazon SageMaker The historical training data is stored in Amazon RDS Which approach should the Specialist use for training a model using that data?
- A. Write a direct connection to the SQL database within the notebook and pull data in
- B. Move the data to Amazon DynamoDB and set up a connection to DynamoDB within the notebook to pull data in
- C. Move the data to Amazon ElastiCache using AWS DMS and set up a connection within the notebook to pull data in for fast access.
- D. Push the data from Microsoft SQL Server to Amazon S3 using an AWS Data Pipeline and provide the S3 location within the notebook.
NEW QUESTION 28
A company wants to classify user behavior as either fraudulent or normal. Based on internal research, a Machine Learning Specialist would like to build a binary classifier based on two features: age of account and transaction month. The class distribution for these features is illustrated in the figure provided.
Based on this information which model would have the HIGHEST accuracy?
- A. Single perceptron with tanh activation function
- B. Logistic regression
- C. Long short-term memory (LSTM) model with scaled exponential linear unit (SELL))
- D. Support vector machine (SVM) with non-linear kernel
NEW QUESTION 29
A company has collected customer comments on its products, rating them as safe or unsafe, using decision trees. The training dataset has the following features: id, date, full review, full review summary, and a binary safe/unsafe tag. During training, any data sample with missing features was dropped. In a few instances, the test set was found to be missing the full review text field.
For this use case, which is the most effective course of action to address test data samples with missing features?
- A. Generate synthetic data to fill in the fields that are missing data, and then run through the test set.
- B. Use an algorithm that handles missing data better than decision trees.
- C. Drop the test samples with missing full review text fields, and then run through the test set.
- D. Copy the summary text fields and use them to fill in the missing full review text fields, and then run through the test set.
In this case, a full review summary usually contains the most descriptive phrases of the entire review and is a valid stand-in for the missing full review text field.
NEW QUESTION 30
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