You have a functioning end-to-end ML pipeline that involves tuning the hyperparameters of your ML model using Al Platform, and then using the best-tuned parameters for training. Hypertuning is taking longer than expected and is delaying the downstream processes. You want to speed up the tuning job without significantly compromising its effectiveness. Which actions should you take?
Choose 2 answers

• A.Change the search algorithm from Bayesian search to random search.
• B.Set the early stopping parameter to TRUE
• C.Decrease the number of parallel trials
• D.Decrease the range of floating-point values
• E.Decrease the maximum number of trials during subsequent training phases.

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You have a functioning end-to-end ML pipeline that involves tuning the hyperparameters of your ML model using Al Platform, and then using the best-tuned parameters for training. Hypertuning is taking longer than expected and is delaying the downstream processes. You want to speed up the tuning job without significantly compromising its effectiveness. Which actions should you take?
Choose 2 answers

• A.Decrease the number of parallel trials
• B.Decrease the range of floating-point values
• C.Set the early stopping parameter to TRUE
• D.Change the search algorithm from Bayesian search to random search.
• E.Decrease the maximum number of trials during subsequent training phases.

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

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Your team is building an application for a global bank that will be used by millions of customers. You built a forecasting model that predicts customers1 account balances 3 days in the future. Your team will use the results in a new feature that will notify users when their account balance is likely to drop below $25. How should you serve your predictions?

• A.1 Build a notification system on Firebase
  2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when your model predicts that a user's account balance will drop below the $25 threshold
• B.1. Build a notification system on Firebase
  2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when the average of all account balance predictions drops below the $25 threshold
• C.1. Create a Pub/Sub topic for each user
  2 Deploy a Cloud Function that sends a notification when your model predicts that a user's account balance will drop below the $25 threshold.
• D.1. Create a Pub/Sub topic for each user
  2. Deploy an application on the App Engine standard environment that sends a notification when your model predicts that a user's account balance will drop below the $25 threshold

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You are working on a classification problem with time series data and achieved an area under the receiver operating characteristic curve (AUC ROC) value of 99% for training data after just a few experiments. You haven't explored using any sophisticated algorithms or spent any time on hyperparameter tuning. What should your next step be to identify and fix the problem?

• A.Address the model overfitting by using a less complex algorithm.
• B.Address data leakage by applying nested cross-validation during model training.
• C.Address data leakage by removing features highly correlated with the target value.
• D.Address the model overfitting by tuning the hyperparameters to reduce the AUC ROC value.

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