You have recently used TensorFlow to train a classification model on tabular data You have created a Dataflow pipeline that can transform several terabytes of data into training or prediction datasets consisting of TFRecords. You now need to productionize the model, and you want the predictions to be automatically uploaded to a BigQuery table on a weekly schedule. What should you do?

• A.Import the model into Vertex Al and deploy it to a Vertex Al endpoint Create a Dataflow pipeline that reuses the data processing logic sends requests to the endpoint and then uploads predictions to a BigQuery table.
• B.Import the model into Vertex Al On Vertex Al Pipelines, create a pipeline that uses the DatafIowPythonJobOp and the ModelBatchPredictOp components.
• C.Import the model into BigQuery Implement the data processing logic in a SQL query On Vertex Al Pipelines create a pipeline that uses the BigqueryQueryJobop and the EigqueryPredictModejobOp components.
• D.Import the model into Vertex Al and deploy it to a Vertex Al endpoint On Vertex Al Pipelines create a pipeline that uses the Dataf lowPythonJobop and the Mcdei3archPredictoc components.

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Your data science team has requested a system that supports scheduled model retraining, Docker containers, and a service that supports autoscaling and monitoring for online prediction requests. Which platform components should you choose for this system?

• A.Vertex AI Pipelines and App Engine
• B.Vertex AI Pipelines, Vertex AI Prediction, and Vertex AI Model Monitoring
• C.Cloud Composer, BigQuery ML, and Vertex AI Prediction
• D.Cloud Composer, Vertex AI Training with custom containers, and App Engine

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You created a model that uses BigQuery ML to perform linear regression. You need to retrain the model on the cumulative data collected every week. You want to minimize the development effort and the scheduling cost. What should you do?

• A.Use Cloud Scheduler to trigger a Cloud Function every week that runs the query for retraining the model.
• B.Use the BigQuery API Connector and Cloud Scheduler to trigger Workflows every week that retrains the model.
• C.Use BigQuery's scheduling service to run the model retraining query periodically.
• D.Create a pipeline in Vertex AI Pipelines that executes the retraining query, and use the Cloud Scheduler API to run the query weekly.

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As the lead ML Engineer for your company, you are responsible for building ML models to digitize scanned customer forms. You have developed a TensorFlow model that converts the scanned images into text and stores them in Cloud Storage. You need to use your ML model on the aggregated data collected at the end of each day with minimal manual intervention. What should you do?

• A.Use the batch prediction functionality of Al Platform
• B.Create a serving pipeline in Compute Engine for prediction
• C.Use Cloud Functions for prediction each time a new data point is ingested
• D.Deploy the model on Al Platform and create a version of it for online inference.

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

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