AI: Module 3

Which resources help define a machine learning (ML) problem? (Select TWO.)

• A neural network

• Sufficient hardware

• A traditional coded solution

• A domain expert to consult

• Access to labeled data

When preparing data for supervised classification machine learning, which attributes should the data have? (Select TWO.)

• Data should be labeled

• Data should contain only instances of the target

• Anyone in the company should be able to access the data

• Data should be generated randomly by using genetic algorithms

• Data should be representative of production

What can you learn by examining the statistics of your data?

• Identifying anomalies in the data

• Verifying that the data is formatted correctly

• Removing outliers

• Filling in missing data

You have a preprocessed dataset that's ready for use in training a model. How should you divide your training data?

• Use all the data to train the model.

• Split the data into two equal sets. Use one half for training and the other half for testing.

• Split the data into three sets. Use 80% for training, 10% for testing, and 10% for validation.

• Split the data into two sets. Use 80% for training, and 20% for testing and validation.

You can select between single model and multi-model hosting with Amazon SageMaker.

• True

• False

What is the purpose of a confusion matrix?

• To plot the labels from the predicted dataset

• To show the true or false positives, along with the true or false negatives

• To show the correlation between two columns in the dataset

• To stratify the classes across training and testing datasets

What does a correlation heatmap show?

• The level of correlation between features in a dataset

• The level of correlation between the test and the validation data

• The level of correlation between the predicted and actual values

• The level of correlation between encoded and text data

Which of the following file formats does pandas support for data importing? (Select TWO.)

• JSON

• MS Word

• CSV

• Binary files

• PDF

Which Amazon service can you use to deploy machine learning instances and run Jupyter Notebooks?

• Amazon Comprehend

• Amazon SageMaker

• Amazon Polly

• Amazon Lex

What is the goal of an Amazon SageMaker hyperparameter tuning job?

• To optimize the validation metrics for training

• To optimize the model parameters to produce the best model

• To optimize the data inputs to produce the fastest prediction

• To optimize the algorithm choice to produce the best model