A retail company is using Amazon Personalize to provide personalized product recommendations for its customers during a marketing campaign. The company sees a significant increase in sales of recommended items to existing customers immediately after deploying a new solution version, but these sales decrease a short time after deployment. Only historical data from before the marketing campaign is available for training.
How should a data scientist adjust the solution?

1. A. Use the event tracker in Amazon Personalize to include real-time user interactions.
2. B. Add user metadata and use the HRNN-Metadata recipe in Amazon Personalize.
3. C. Implement a new solution using the built-in factorization machines (FM) algorithm in Amazon SageMaker.
4. D. Add event type and event value fields to the interactions dataset in Amazon Personalize.

Correct Answer: A

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

For the given confusion matrix, what is the recall and precision of the model?

1. A. Recall = 0.92 Precision = 0.84
2. B. Recall = 0.84 Precision = 0.8
3. C. Recall = 0.92 Precision = 0.8
4. D. Recall = 0.8 Precision = 0.92

Correct Answer: C

A Data Scientist is building a linear regression model and will use resulting p-values to evaluate the statistical significance of each coefficient. Upon inspection of the dataset, the Data Scientist discovers that most of the features are normally distributed. The plot of one feature in the dataset is shown in the graphic.

What transformation should the Data Scientist apply to satisfy the statistical assumptions of the linear regression model?

1. A. Exponential transformation
2. B. Logarithmic transformation
3. C. Polynomial transformation
4. D. Sinusoidal transformation

Correct Answer: A

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