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NEW QUESTION 30
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?
- A. The model is 86% accurate and the cost incurred by the company as a result of false positives is less than the false negatives.
- B. The precision of the model is 86%, which is greater than the accuracy of the model.
- C. The precision of the model is 86%, which is less than the accuracy of the model.
- D. The model is 86% accurate and the cost incurred by the company as a result of false negatives is less than the false positives.
Answer: D
NEW QUESTION 31
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?
- A. Use the event tracker in Amazon Personalize to include real-time user interactions.
- B. Implement a new solution using the built-in factorization machines (FM) algorithm in Amazon SageMaker.
- C. Add event type and event value fields to the interactions dataset in Amazon Personalize.
- D. Add user metadata and use the HRNN-Metadata recipe in Amazon Personalize.
Answer: A
NEW QUESTION 32
A company is observing low accuracy while training on the default built-in image classification algorithm in Amazon SageMaker. The Data Science team wants to use an Inception neural network architecture instead of a ResNet architecture.
Which of the following will accomplish this? (Select TWO.)
- A. Bundle a Docker container with TensorFlow Estimator loaded with an Inception network and use this for model training.
- B. Use custom code in Amazon SageMaker with TensorFlow Estimator to load the model with an Inception network and use this for model training.
- C. Download and apt-get install the inception network code into an Amazon EC2 instance and use this instance as a Jupyter notebook in Amazon SageMaker.
- D. Customize the built-in image classification algorithm to use Inception and use this for model training.
- E. Create a support case with the SageMaker team to change the default image classification algorithm to Inception.
Answer: B,D
NEW QUESTION 33
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