You are an ML engineer at a travel company. You have been researching customers' travel behavior for many years, and you have deployed models that predict customers' vacation patterns. You have observed that customers' vacation destinations vary based on seasonality and holidays; however, these seasonal variations are similar across years. You want to quickly and easily store and compare the model versions and performance statistics across years. What should you do?

• A.Store the performance statistics in Cloud SQL. Query that database to compare the performance statistics across the model versions.
• B.Create versions of your models for each season per year in Vertex AI. Compare the performance statistics across the models in the Evaluate tab of the Vertex AI UI.
• C.Store the performance statistics of each version of your models using seasons and years as events in Vertex ML Metadata. Compare the results across the slices.
• D.Store the performance statistics of each pipeline run in Kubeflow under an experiment for each season per year. Compare the results across the experiments in the Kubeflow UI.

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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.Increase the size of the training batch
• B.Decrease the size of the training batch
• C.Increase the learning rate hyperparameter
• D.Decrease the learning rate hyperparameter

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You lead a data science team at a large international corporation. Most of the models your team trains are large-scale models using high-level TensorFlow APIs on AI Platform with GPUs. Your team usually takes a few weeks or months to iterate on a new version of a model. You were recently asked to review your team's spending. How should you reduce your Google Cloud compute costs without impacting the model's performance?

• A.Migrate to training with Kuberflow on Google Kubernetes Engine, and use preemptible VMs without checkpoints.
• B.Use AI Platform to run distributed training jobs without checkpoints.
• C.Migrate to training with Kuberflow on Google Kubernetes Engine, and use preemptible VMs with checkpoints.
• D.Use AI Platform to run distributed training jobs with checkpoints.

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You have trained a text classification model in TensorFlow using AI Platform. You want to use the trained model for batch predictions on text data stored in BigQuery while minimizing computational overhead.
What should you do?

• A.Export the model to BigQuery ML.
• B.Deploy and version the model on AI Platform.
• C.Use Dataflow with the SavedModel to read the data from BigQuery.
• D.Submit a batch prediction job on AI Platform that points to the model location in Cloud Storage.

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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.Write a Cloud Functions script that launches a training and deploying job on Ai Platform that is triggered by Cloud Scheduler
• B.Use a model trained and deployed on BigQuery ML and trigger retraining with the scheduled query feature in BigQuery
• C.Use Cloud Composer to programmatically schedule a Dataflow job that executes the workflow from training to deploying your model
• D.Configure Kubeflow Pipelines to schedule your multi-step workflow from training to deploying your model.

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