You are developing models to classify customer support emails. You created models with TensorFlow Estimators using small datasets on your on-premises system, but you now need to train the models using large datasets to ensure high performance. You will port your models to Google Cloud and want to minimize code refactoring and infrastructure overhead for easier migration from on-prem to cloud. What should you do?

• A.Use Kubeflow Pipelines to train on a Google Kubernetes Engine cluster.
• B.Create a Managed Instance Group with autoscaling
• C.Create a cluster on Dataproc for training
• D.Use Al Platform for distributed training

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

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

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You were asked to investigate failures of a production line component based on sensor readings. After receiving the dataset, you discover that less than 1% of the readings are positive examples representing failure incidents. You have tried to train several classification models, but none of them converge. How should you resolve the class imbalance problem?

• A.Remove negative examples until the numbers of positive and negative examples are equal
• B.Use the class distribution to generate 10% positive examples
• C.Downsample the data with upweighting to create a sample with 10% positive examples
• D.Use a convolutional neural network with max pooling and softmax activation

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You are building a model to predict daily temperatures. You split the data randomly and then transformed the training and test datasets. Temperature data for model training is uploaded hourly. During testing, your model performed with 97% accuracy; however, after deploying to production, the model's accuracy dropped to 66%. How can you make your production model more accurate?

• A.Add more data to your test set to ensure that you have a fair distribution and sample for testing
• B.Normalize the data for the training, and test datasets as two separate steps.
• C.Split the training and test data based on time rather than a random split to avoid leakage
• D.Apply data transformations before splitting, and cross-validate to make sure that the transformations are applied to both the training and test sets.

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