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You work for a public transportation company and need to build a model to estimate delay times for multiple transportation routes. Predictions are served directly to users in an app in real time. Because different seasons and population increases impact the data relevance, you will retrain the model every month. You want to follow Google-recommended best practices. How should you configure the end-to-end architecture of the predictive model?

A. Configure Kubeflow Pipelines to schedule your multi-step workflow from training to deploying your model.

B. Use a model trained and deployed on BigQuery ML, and trigger retraining with the scheduled query feature in BigQuery.

C. Write a Cloud Functions script that launches a training and deploying job on AI Platform that is triggered by Cloud Scheduler.

D. Use Cloud Composer to programmatically schedule a Dataflow job that executes the workflow from training to deploying your model.

Your organization's call center has asked you to develop a model that analyzes customer sentiments in each call. The call center receives over one million calls daily, and data is stored in Cloud Storage. The data collected must not leave the region in which the call originated, and no Personally Identifiable Information (PII) can be stored or analyzed. The data science team has a third-party tool for visualization and access which requires a SQL ANSI-2011 compliant interface. You need to select components for data processing and for analytics. How should the data pipeline be designed?

A. 1= Dataflow, 2= BigQuery

B. 1 = Pub/Sub, 2= Datastore

C. 1 = Dataflow, 2 = Cloud SQL

D. 1 = Cloud Function, 2= Cloud SQL

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 customers' 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. 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.

B. 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.

C. 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.

D. 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.

You recently developed a deep learning model using Keras, and now you are experimenting with different training strategies. First, you trained the model using a single GPU, but the training process was too slow. Next, you distributed the training across 4 GPUs using tf.distribute.MirroredStrategy (with no other changes), but you did not observe a decrease in training time. What should you do?

A. Distribute the dataset with tf.distribute.Strategy.experimental_distribute_dataset

B. Create a custom training loop.

C. Use a TPU with tf.distribute.TPUStrategy.

D. Increase the batch size.

During batch training of a neural network, you notice that there is an oscillation in the loss. How should you adjust your model to ensure that it converges?

A. Decrease the size of the training batch.

B. Decrease the learning rate hyperparameter.

C. Increase the learning rate hyperparameter.

D. Increase the size of the training batch.