A large mobile network operating company is building a machine learning model to predict customers who are likely to unsubscribe from the service. The company plans to offer an incentive for these customers as the cost of churn is far greater than the cost of the incentive.
The model produces the following confusion matrix after evaluating on a test dataset of 100 customers: Based on the model evaluation results, why is this a viable model for production?

1. A. The model is 86% accurate and the cost incurred by the company as a result of false negatives is less than the false positives.
2. B. The precision of the model is 86%, which is less than the accuracy of the model.
3. C. The model is 86% accurate and the cost incurred by the company as a result of false positives is less than the false negatives.
4. D. The precision of the model is 86%, which is greater than the accuracy of the model.

Correct Answer: A

A data engineer at a bank is evaluating a new tabular dataset that includes customer data. The data engineer will use the customer data to create a new model to predict customer behavior. After creating a correlation matrix for the variables, the data engineer notices that many of the 100 features are highly correlated with each other.
Which steps should the data engineer take to address this issue? (Choose two.)

1. A. Use a linear-based algorithm to train the model.
2. B. Apply principal component analysis (PCA).
3. C. Remove a portion of highly correlated features from the dataset.
4. D. Apply min-max feature scaling to the dataset.
5. E. Apply one-hot encoding category-based variables.

Correct Answer: BD

During mini-batch training of a neural network for a classification problem, a Data Scientist notices that training accuracy oscillates What is the MOST likely cause of this issue?

1. A. The class distribution in the dataset is imbalanced
2. B. Dataset shuffling is disabled
3. C. The batch size is too big
4. D. The learning rate is very high

Correct Answer: B

This graph shows the training and validation loss against the epochs for a neural network The network being trained is as follows
• Two dense layers one output neuron
• 100 neurons in each layer
• 100 epochs
• Random initialization of weights

Which technique can be used to improve model performance in terms of accuracy in the validation set?

1. A. Early stopping
2. B. Random initialization of weights with appropriate seed
3. C. Increasing the number of epochs
4. D. Adding another layer with the 100 neurons

Correct Answer: D

A Machine Learning Specialist is working for a credit card processing company and receives an unbalanced dataset containing credit card transactions. It contains 99,000 valid transactions and 1,000 fraudulent transactions The Specialist is asked to score a model that was run against the dataset The Specialist has been advised that identifying valid transactions is equally as important as identifying fraudulent transactions
What metric is BEST suited to score the model?

1. A. Precision
2. B. Recall
3. C. Area Under the ROC Curve (AUC)
4. D. Root Mean Square Error (RMSE)

Correct Answer: A