You work for a large hotel chain and have been asked to assist the marketing team in gathering predictions for a targeted marketing strategy. You need to make predictions about user lifetime value (LTV) over the next 20 days so that marketing can be adjusted accordingly. The customer dataset is in BigQuery, and you are preparing the tabular data for training with AutoML Tables.
This data has a time signal that is spread across multiple columns. How should you ensure that AutoML fits the best model to your data?

• A. Submit the data for training without performing any manual transformations.
  AIlow AutoML to handle the appropriate transformations.
  Choose an automatic data split across the training, validation, and testing sets.
• B. Submit the data for training without performing any manual transformations, and indicate an appropriate column as the Time column.
  AIlow AutoML to split your data based on the time signal provided, and reserve the more recent data for the validation and testing sets.
• C. Manually combine all columns that contain a time signal into an array.
  AIlow AutoML to interpret this array appropriately.
  Choose an automatic data split across the training, validation, and testing sets.
• D. Submit the data for training without performing any manual transformations.
  Use the columns that have a time signal to manually split your data.
  Ensure that the data in your validation set is from 30 days after the data in your training set and that the data in your testing sets from 30 days after your validation set.

You are building a linear model with over 100 input features, all with values between -1 and 1.
You suspect that many features are non-informative. You want to remove the non-informative features from your model while keeping the informative ones in their original form. Which technique should you use?

• A. Use L1 regularization to reduce the coefficients of uninformative features to 0.
• B. Use an iterative dropout technique to identify which features do not degrade the model when removed.
• C. Use principal component analysis (PCA) to eliminate the least informative features.
• D. After building your model, use Shapley values to determine which features are the most informative.

You are developing an automated training workflow using Agent Platform Pipelines. The pipeline trains a new custom model and registers it to Model Registry. You need to validate the model's performance on a held-out test dataset. You want to ensure that the model is deployed to a production Agent Platform endpoint only if its evaluation metrics meet a specific threshold. You want to minimize cost and administrative overhead. What should you do?

• A. Deploy the model to a temporary Agent Platform endpoint. Use a custom component to send the test dataset for online prediction, calculate the metrics, and deploy to the production endpoint if the evaluation metrics exceed the specified threshold.
• B. Add a batch prediction component to generate predictions on the test dataset. Pass the results to a model evaluation component. Use a conditional task to deploy the model if the evaluation metrics exceed the specified threshold.
• C. Configure the training component to calculate evaluation metrics on the training dataset during the training job. Output the metrics to the next step and use a conditional task to deploy the model if the evaluation metrics exceed the specified threshold.
• D. Insert a Python component that pulls the latest model version from Model Registry. Compare its training metrics against the current production model, and use a conditional task to deploy the new model if it performs better.

You need to write a generic test to verify whether Dense Neural Network (DNN) models automatically released by your team have a sufficient number of parameters to learn the task for which they were built. What should you do?

• A. Train the model with no regularization, and verify that the loss function is close to zero.
• B. Train the model for a few iterations, and verify that the loss is constant.
• C. Train the model for a few iterations, and check for NaN values.
• D. Train a simple linear model, and determine if the DNN model outperforms it.