You are an ML engineer at a regulated insurance company. You are asked to develop an insurance approval model that accepts or rejects insurance applications from potential customers. What factors should you consider before building the model?

• A.Traceability, reproducibility, and explainability
• B.Federated learning, reproducibility, and explainability
• C.Redaction, reproducibility, and explainability
• D.Differential privacy federated learning, and explainability

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You work for a large technology company that wants to modernize their contact center. You have been asked to develop a solution to classify incoming calls by product so that requests can be more quickly routed to the correct support team. You have already transcribed the calls using the Speech-to-Text API. You want to minimize data preprocessing and development time. How should you build the model?

• A.Use the Al Platform Training built-in algorithms to create a custom model
• B.Use the Cloud Natural Language API to extract custom entities for classification
• C.Build a custom model to identify the product keywords from the transcribed calls, and then run the keywords through a classification algorithm
• D.Use AutoML Natural Language to extract custom entities for classification

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You have trained a deep neural network model on Google Cloud. The model has low loss on the training data, but is performing worse on the validation dat a. You want the model to be resilient to overfitting. Which strategy should you use when retraining the model?

• A.Apply a 12 regularization parameter of 0.4, and decrease the learning rate by a factor of 10.
• B.Run a hyperparameter tuning job on Al Platform to optimize for the learning rate, and increase the number of neurons by a factor of 2.
• C.Run a hyperparameter tuning job on Al Platform to optimize for the L2 regularization and dropout parameters
• D.Apply a dropout parameter of 0 2, and decrease the learning rate by a factor of 10

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