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NEW QUESTION 53
You recently joined a machine learning team that will soon release a new project. As a lead on the project, you are asked to determine the production readiness of the ML components. The team has already tested features and data, model development, and infrastructure. Which additional readiness check should you recommend to the team?

• A. Ensure that feature expectations are captured in the schema
• B. Ensure that training is reproducible
• C. Ensure that model performance is monitored
• D. Ensure that all hyperparameters are tuned

Answer: B

Explanation:
https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/aad9f93b86b7addfea4c419b9100c6cdd26cacea.pdf

NEW QUESTION 54
You have trained a deep neural network model on Google Cloud. The model has low loss on the training data, but is performing worse on the validation dat a. You want the model to be resilient to overfitting. Which strategy should you use when retraining the model?

• A. Apply a 12 regularization parameter of 0.4, and decrease the learning rate by a factor of 10.
• B. Apply a dropout parameter of 0 2, and decrease the learning rate by a factor of 10
• C. Run a hyperparameter tuning job on Al Platform to optimize for the learning rate, and increase the number of neurons by a factor of 2.
• D. Run a hyperparameter tuning job on Al Platform to optimize for the L2 regularization and dropout parameters

Answer: D

Explanation:
https://machinelearningmastery.com/introduction-to-regularization-to-reduce-overfitting-and-improve-generalization-error/

NEW QUESTION 55
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. Use Vertex Al Platform for distributed training
• C. Create a Managed Instance Group with autoscaling
• D. Create a cluster on Dataproc for training

Answer: B

Explanation:
AI platform also contains kubeflow pipelines. you don't need to set up infrastructure to use it. For D you need to set up a kubernetes cluster engine. The question asks us to minimize infrastructure overheard.

NEW QUESTION 56
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